Release: v4.22.0

* fix preprocessing for instance segmentation maps

* add support for per-image instance2class_id mapping

* edit docstrings for clarity

.circleci/create_circleci_config.py

@@ -1,391 +0,0 @@
-# coding=utf-8
-# Copyright 2022 The HuggingFace Inc. team.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import argparse
-import copy
-import os
-from dataclasses import dataclass
-from typing import Any, Dict, List, Optional
-
-import yaml
-
-
-COMMON_ENV_VARIABLES = {"OMP_NUM_THREADS": 1, "TRANSFORMERS_IS_CI": True, "PYTEST_TIMEOUT": 120}
-COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "dist": "loadfile", "s": None}
-DEFAULT_DOCKER_IMAGE = [{"image": "cimg/python:3.7.12"}]
-TORCH_SCATTER_INSTALL = "pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.0+cpu.html"
-
-
-@dataclass
-class CircleCIJob:
-    name: str
-    additional_env: Dict[str, Any] = None
-    cache_name: str = None
-    cache_version: str = "0.5"
-    docker_image: List[Dict[str, str]] = None
-    install_steps: List[str] = None
-    marker: Optional[str] = None
-    parallelism: Optional[int] = 1
-    pytest_num_workers: int = 8
-    pytest_options: Dict[str, Any] = None
-    resource_class: Optional[str] = "xlarge"
-    tests_to_run: Optional[List[str]] = None
-    working_directory: str = "~/transformers"
-
-    def __post_init__(self):
-        # Deal with defaults for mutable attributes.
-        if self.additional_env is None:
-            self.additional_env = {}
-        if self.cache_name is None:
-            self.cache_name = self.name
-        if self.docker_image is None:
-            # Let's avoid changing the default list and make a copy.
-            self.docker_image = copy.deepcopy(DEFAULT_DOCKER_IMAGE)
-        if self.install_steps is None:
-            self.install_steps = []
-        if self.pytest_options is None:
-            self.pytest_options = {}
-        if isinstance(self.tests_to_run, str):
-            self.tests_to_run = [self.tests_to_run]
-
-    def to_dict(self):
-        job = {
-            "working_directory": self.working_directory,
-            "docker": self.docker_image,
-            "environment": {**COMMON_ENV_VARIABLES, **self.additional_env},
-        }
-        if self.resource_class is not None:
-            job["resource_class"] = self.resource_class
-        if self.parallelism is not None:
-            job["parallelism"] = self.parallelism
-        steps = [
-            "checkout",
-            {"attach_workspace": {"at": "~/transformers/test_preparation"}},
-            {
-                "restore_cache": {
-                    "keys": [
-                        f"v{self.cache_version}-{self.cache_name}-" + '{{ checksum "setup.py" }}',
-                        f"v{self.cache_version}-{self.cache_name}-",
-                    ]
-                }
-            },
-        ]
-        steps.extend([{"run": l} for l in self.install_steps])
-        steps.append(
-            {
-                "save_cache": {
-                    "key": f"v{self.cache_version}-{self.cache_name}-" + '{{ checksum "setup.py" }}',
-                    "paths": ["~/.cache/pip"],
-                }
-            }
-        )
-
-        all_options = {**COMMON_PYTEST_OPTIONS, **self.pytest_options}
-        pytest_flags = [f"--{key}={value}" if value is not None else f"-{key}" for key, value in all_options.items()]
-        pytest_flags.append(
-            f"--make-reports={self.name}" if "examples" in self.name else f"--make-reports=tests_{self.name}"
-        )
-        test_command = f"python -m pytest -n {self.pytest_num_workers} " + " ".join(pytest_flags)
-        if self.tests_to_run is None:
-            test_command += " << pipeline.parameters.tests_to_run >>"
-        else:
-            test_command += " " + " ".join(self.tests_to_run)
-        if self.marker is not None:
-            test_command += f" -m {self.marker}"
-        test_command += " | tee tests_output.txt"
-        steps.append({"run": {"name": "Run tests", "command": test_command}})
-        steps.append({"store_artifacts": {"path": "~/transformers/tests_output.txt"}})
-        steps.append({"store_artifacts": {"path": "~/transformers/reports"}})
-        job["steps"] = steps
-        return job
-
-    @property
-    def job_name(self):
-        return self.name if "examples" in self.name else f"tests_{self.name}"
-
-
-# JOBS
-torch_and_tf_job = CircleCIJob(
-    "torch_and_tf",
-    additional_env={"RUN_PT_TF_CROSS_TESTS": True},
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng git-lfs",
-        "git lfs install",
-        "pip install --upgrade pip",
-        "pip install .[sklearn,tf-cpu,torch,testing,sentencepiece,torch-speech,vision]",
-        TORCH_SCATTER_INSTALL,
-        "pip install tensorflow_probability",
-        "pip install https://github.com/kpu/kenlm/archive/master.zip",
-        "pip install git+https://github.com/huggingface/accelerate",
-    ],
-    marker="is_pt_tf_cross_test",
-    pytest_options={"rA": None, "durations": 0},
-)
-
-
-torch_and_flax_job = CircleCIJob(
-    "torch_and_flax",
-    additional_env={"RUN_PT_FLAX_CROSS_TESTS": True},
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
-        "pip install --upgrade pip",
-        "pip install .[sklearn,flax,torch,testing,sentencepiece,torch-speech,vision]",
-        TORCH_SCATTER_INSTALL,
-        "pip install https://github.com/kpu/kenlm/archive/master.zip",
-        "pip install git+https://github.com/huggingface/accelerate",
-    ],
-    marker="is_pt_flax_cross_test",
-    pytest_options={"rA": None, "durations": 0},
-)
-
-
-torch_job = CircleCIJob(
-    "torch",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng time",
-        "pip install --upgrade pip",
-        "pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]",
-        TORCH_SCATTER_INSTALL,
-        "pip install https://github.com/kpu/kenlm/archive/master.zip",
-        "pip install git+https://github.com/huggingface/accelerate",
-    ],
-    pytest_num_workers=3,
-)
-
-
-tf_job = CircleCIJob(
-    "tf",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
-        "pip install --upgrade pip",
-        "pip install .[sklearn,tf-cpu,testing,sentencepiece,tf-speech,vision]",
-        "pip install tensorflow_probability",
-        "pip install https://github.com/kpu/kenlm/archive/master.zip",
-    ],
-    pytest_options={"rA": None},
-)
-
-
-flax_job = CircleCIJob(
-    "flax",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
-        "pip install --upgrade pip",
-        "pip install .[flax,testing,sentencepiece,flax-speech,vision]",
-        "pip install https://github.com/kpu/kenlm/archive/master.zip",
-    ],
-    pytest_options={"rA": None},
-)
-
-
-pipelines_torch_job = CircleCIJob(
-    "pipelines_torch",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
-        "pip install --upgrade pip",
-        "pip install .[sklearn,torch,testing,sentencepiece,torch-speech,vision,timm]",
-        TORCH_SCATTER_INSTALL,
-        "pip install https://github.com/kpu/kenlm/archive/master.zip",
-    ],
-    pytest_options={"rA": None},
-    tests_to_run="tests/pipelines/"
-)
-
-
-pipelines_tf_job = CircleCIJob(
-    "pipelines_tf",
-    install_steps=[
-        "pip install --upgrade pip",
-        "pip install .[sklearn,tf-cpu,testing,sentencepiece]",
-        "pip install tensorflow_probability",
-    ],
-    pytest_options={"rA": None},
-    tests_to_run="tests/pipelines/"
-)
-
-
-custom_tokenizers_job = CircleCIJob(
-    "custom_tokenizers",
-    additional_env={"RUN_CUSTOM_TOKENIZERS": True},
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y cmake",
-        {
-            "name": "install jumanpp",
-            "command":
-                "wget https://github.com/ku-nlp/jumanpp/releases/download/v2.0.0-rc3/jumanpp-2.0.0-rc3.tar.xz\n"
-                "tar xvf jumanpp-2.0.0-rc3.tar.xz\n"
-                "mkdir jumanpp-2.0.0-rc3/bld\n"
-                "cd jumanpp-2.0.0-rc3/bld\n"
-                "sudo cmake .. -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local\n"
-                "sudo make install\n",
-        },
-        "pip install --upgrade pip",
-        "pip install .[ja,testing,sentencepiece,jieba,spacy,ftfy,rjieba]",
-        "python -m unidic download",
-    ],
-    parallelism=None,
-    resource_class=None,
-    tests_to_run=[
-        "./tests/models/bert_japanese/test_tokenization_bert_japanese.py",
-        "./tests/models/openai/test_tokenization_openai.py",
-        "./tests/models/clip/test_tokenization_clip.py",
-    ],
-)
-
-
-examples_torch_job = CircleCIJob(
-    "examples_torch",
-    cache_name="torch_examples",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev espeak-ng",
-        "pip install --upgrade pip",
-        "pip install .[sklearn,torch,sentencepiece,testing,torch-speech]",
-        "pip install -r examples/pytorch/_tests_requirements.txt",
-    ],
-    tests_to_run="./examples/pytorch/",
-)
-
-
-examples_tensorflow_job = CircleCIJob(
-    "examples_tensorflow",
-    cache_name="tensorflow_examples",
-    install_steps=[
-        "pip install --upgrade pip",
-        "pip install .[sklearn,tensorflow,sentencepiece,testing]",
-        "pip install -r examples/tensorflow/_tests_requirements.txt",
-    ],
-    tests_to_run="./examples/tensorflow/",
-)
-
-
-examples_flax_job = CircleCIJob(
-    "examples_flax",
-    cache_name="flax_examples",
-    install_steps=[
-        "pip install --upgrade pip",
-        "pip install .[flax,testing,sentencepiece]",
-        "pip install -r examples/flax/_tests_requirements.txt",
-    ],
-    tests_to_run="./examples/flax/",
-)
-
-
-hub_job = CircleCIJob(
-    "hub",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install git-lfs",
-        'git config --global user.email "ci@dummy.com"',
-        'git config --global user.name "ci"',
-        "pip install --upgrade pip",
-        "pip install .[torch,sentencepiece,testing]",
-    ],
-    marker="is_staging_test",
-    pytest_num_workers=1,
-)
-
-
-onnx_job = CircleCIJob(
-    "onnx",
-    install_steps=[
-        "pip install --upgrade pip",
-        "pip install .[torch,tf,testing,sentencepiece,onnxruntime,vision,rjieba]",
-    ],
-    pytest_options={"k onnx": None},
-    pytest_num_workers=1,
-)
-
-
-layoutlm_job = CircleCIJob(
-    "layoutlmv2_and_v3",
-    install_steps=[
-        "sudo apt-get -y update && sudo apt-get install -y libsndfile1-dev",
-        "pip install --upgrade pip",
-        "pip install .[torch,testing,vision]",
-        "pip install torchvision",
-        "pip install 'git+https://github.com/facebookresearch/detectron2.git'",
-        "sudo apt install tesseract-ocr",
-        "pip install pytesseract",
-    ],
-    tests_to_run="tests/models/*layoutlmv*",
-    pytest_num_workers=1,
-    pytest_options={"durations": 100},
-)
-
-
-REGULAR_TESTS = [
-    torch_and_tf_job,
-    torch_and_flax_job,
-    torch_job,
-    tf_job,
-    flax_job,
-    custom_tokenizers_job,
-    hub_job,
-    onnx_job,
-    layoutlm_job,
-]
-EXAMPLES_TESTS = [
-    examples_torch_job,
-    examples_tensorflow_job,
-    examples_flax_job,
-]
-PIPELINE_TESTS = [
-    pipelines_torch_job,
-    pipelines_tf_job,
-]
-
-
-def create_circleci_config(folder=None):
-    if folder is None:
-        folder = os.getcwd()
-    jobs = []
-    all_test_file = os.path.join(folder, "test_list.txt")
-    if os.path.exists(all_test_file):
-        with open(all_test_file) as f:
-            all_test_list = f.read()
-    else:
-        all_test_list = []
-    if len(all_test_list) > 0:
-        jobs.extend(PIPELINE_TESTS)
-
-    test_file = os.path.join(folder, "filtered_test_list.txt")
-    if os.path.exists(test_file):
-        with open(test_file) as f:
-            test_list = f.read()
-    else:
-        test_list = []
-    if len(test_list) > 0:
-        jobs.extend(REGULAR_TESTS)
-
-    example_file = os.path.join(folder, "examples_test_list.txt")
-    if os.path.exists(example_file) and os.path.getsize(example_file) > 0:
-        jobs.extend(EXAMPLES_TESTS)
-
-    if len(jobs) > 0:
-        config = {"version": "2.1"}
-        config["parameters"] = {"tests_to_run": {"type": "string", "default": test_list}}
-        config["jobs"] = {j.job_name: j.to_dict() for j in jobs}
-        config["workflows"] = {"version": 2, "run_tests": {"jobs": [j.job_name for j in jobs]}}
-        with open(os.path.join(folder, "generated_config.yml"), "w") as f:
-            f.write(yaml.dump(config, indent=2, width=1000000, sort_keys=False))
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--fetcher_folder", type=str, default=None, help="Only test that all tests and modules are accounted for."
-    )
-    args = parser.parse_args()
-
-    create_circleci_config(args.fetcher_folder)

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,6 @@
 name: "\U0001F41B Bug Report"
 description: Submit a bug report to help us improve transformers
+labels: [ "bug" ]
 body:
   - type: textarea
     id: system-info

.github/workflows/build-docker-images.yml

@@ -6,10 +6,6 @@ on:
       - docker-image*
   repository_dispatch:
   workflow_call:
-    inputs:
-      image_postfix:
-        required: true
-        type: string
   schedule:
     - cron: "0 1 * * *"
 
@@ -27,7 +23,7 @@ jobs:
         uses: docker/setup-buildx-action@v1
       -
         name: Check out code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v2
       -
         name: Login to DockerHub
         uses: docker/login-action@v1
@@ -42,25 +38,10 @@ jobs:
           build-args: |
             REF=main
           push: true
-          tags: huggingface/transformers-all-latest-gpu${{ inputs.image_postfix }}
-      # Push CI images still need to be re-built daily
-      -
-        name: Build and push (for Push CI) in a daily basis
-        # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`.
-        # The later case is useful for manual image building for debugging purpose. Use another tag in this case!
-        if: inputs.image_postfix != '-push-ci'
-        uses: docker/build-push-action@v2
-        with:
-          context: ./docker/transformers-all-latest-gpu
-          build-args: |
-            REF=main
-          push: true
-          tags: huggingface/transformers-all-latest-gpu-push-ci
+          tags: huggingface/transformers-all-latest-gpu
 
   latest-with-torch-nightly-docker:
     name: "Nightly PyTorch + Stable TensorFlow"
-    # Push CI doesn't need this image
-    if: inputs.image_postfix != '-push-ci'
     runs-on: ubuntu-latest
     steps:
       -
@@ -110,25 +91,10 @@ jobs:
           build-args: |
             REF=main
           push: true
-          tags: huggingface/transformers-pytorch-deepspeed-latest-gpu${{ inputs.image_postfix }}
-      # Push CI images still need to be re-built daily
-      -
-        name: Build and push (for Push CI) in a daily basis
-        # This condition allows `schedule` events, or `push` events that trigger this workflow NOT via `workflow_call`.
-        # The later case is useful for manual image building for debugging purpose. Use another tag in this case!
-        if: inputs.image_postfix != '-push-ci'
-        uses: docker/build-push-action@v2
-        with:
-          context: ./docker/transformers-pytorch-deepspeed-latest-gpu
-          build-args: |
-            REF=main
-          push: true
-          tags: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
+          tags: huggingface/transformers-pytorch-deepspeed-latest-gpu
 
   nightly-torch-deepspeed-docker:
     name: "Nightly PyTorch + DeepSpeed"
-    # Push CI doesn't need this image
-    if: inputs.image_postfix != '-push-ci'
     runs-on: ubuntu-latest
     steps:
       -
@@ -155,8 +121,6 @@ jobs:
 
   doc-builder:
     name: "Doc builder"
-    # Push CI doesn't need this image
-    if: inputs.image_postfix != '-push-ci'
     runs-on: ubuntu-latest
     steps:
       -
@@ -181,8 +145,6 @@ jobs:
 
   latest-pytorch:
     name: "Latest PyTorch [dev]"
-    # Push CI doesn't need this image
-    if: inputs.image_postfix != '-push-ci'
     runs-on: ubuntu-latest
     steps:
       -
@@ -209,8 +171,6 @@ jobs:
 
   latest-tensorflow:
     name: "Latest TensorFlow [dev]"
-    # Push CI doesn't need this image
-    if: inputs.image_postfix != '-push-ci'
     runs-on: ubuntu-latest
     steps:
       -

.github/workflows/check_runner_status.yml

@@ -1,67 +0,0 @@
-name: Self-hosted runner (check runner status)
-
-# Note that each job's dependencies go into a corresponding docker file.
-#
-# For example for `run_all_tests_torch_cuda_extensions_gpu` the docker image is
-# `huggingface/transformers-pytorch-deepspeed-latest-gpu`, which can be found at
-# `docker/transformers-pytorch-deepspeed-latest-gpu/Dockerfile`
-
-on:
-  repository_dispatch:
-  schedule:
-    # run per hour
-    - cron: "0 */1 * * *"
-
-env:
-  TRANSFORMERS_IS_CI: yes
-
-jobs:
-  check_runner_status:
-    name: Check Runner Status
-    runs-on: ubuntu-latest
-    outputs:
-      offline_runners: ${{ steps.set-offline_runners.outputs.offline_runners }}
-    steps:
-      - name: Checkout transformers
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: Check Runner Status
-        run: python utils/check_self_hosted_runner.py --target_runners single-gpu-ci-runner-docker,multi-gpu-ci-runner-docker,single-gpu-scheduled-ci-runner-docker,multi-scheduled-scheduled-ci-runner-docker,single-gpu-doctest-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-
-      - id: set-offline_runners
-        name: Set output for offline runners
-        if: ${{ always() }}
-        run: |
-          offline_runners=$(python3 -c 'fp = open("offline_runners.txt"); failed = fp.read(); fp.close(); print(failed)')
-          echo "::set-output name=offline_runners::$offline_runners"
-
-  send_results:
-    name: Send results to webhook
-    runs-on: ubuntu-latest
-    needs: check_runner_status
-    if: ${{ failure() }}
-    steps:
-      - name: Preliminary job status
-        shell: bash
-        run: |
-          echo "Runner availability: ${{ needs.check_runner_status.result }}"
-
-      - uses: actions/checkout@v2
-      - uses: actions/download-artifact@v2
-      - name: Send message to Slack
-        env:
-          CI_SLACK_BOT_TOKEN: ${{ secrets.CI_SLACK_BOT_TOKEN }}
-          CI_SLACK_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID }}
-          CI_SLACK_CHANNEL_ID_DAILY: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
-          CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
-          CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
-          CI_EVENT: runner status check
-          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
-          OFFLINE_RUNNERS: ${{ needs.check_runner_status.outputs.offline_runners }}
-        # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
-        # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
-        run: |
-          pip install slack_sdk
-          python utils/notification_service.py

.github/workflows/self-nightly-scheduled.yml

@@ -23,21 +23,8 @@ env:
   RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
-  check_runner_status:
-    name: Check Runner Status
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout transformers
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: Check Runner Status
-        run: python utils/check_self_hosted_runner.py --target_runners single-gpu-scheduled-ci-runner-docker,multi-gpu-scheduled-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-
-  check_runners:
+  run_check_runners:
     name: Check Runners
-    needs: check_runner_status
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
@@ -52,7 +39,7 @@ jobs:
 
   setup:
     name: Setup
-    needs: check_runners
+    needs: run_check_runners
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
@@ -96,7 +83,7 @@ jobs:
     container:
       image: huggingface/transformers-all-latest-torch-nightly-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
@@ -149,7 +136,7 @@ jobs:
     container:
       image: huggingface/transformers-all-latest-torch-nightly-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
@@ -198,7 +185,7 @@ jobs:
       matrix:
         machine_type: [single-gpu, multi-gpu]
     runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
-    needs: setup
+    needs: [run_check_runners, setup]
     container:
       image: huggingface/transformers-pytorch-deepspeed-nightly-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -249,21 +236,13 @@ jobs:
     name: Send results to webhook
     runs-on: ubuntu-latest
     if: always()
-    needs: [
-      check_runner_status,
-      check_runners,
-      setup,
-      run_tests_single_gpu,
-      run_tests_multi_gpu,
-      run_all_tests_torch_cuda_extensions_gpu
-    ]
+    needs: [run_check_runners, setup, run_tests_single_gpu, run_tests_multi_gpu, run_all_tests_torch_cuda_extensions_gpu]
     steps:
       - name: Preliminary job status
         shell: bash
         # For the meaning of these environment variables, see the job `Setup`
         run: |
-          echo "Runner availability: ${{ needs.check_runner_status.result }}"
-          echo "Runner status: ${{ needs.check_runners.result }}"
+          echo "Runner status: ${{ needs.run_check_runners.result }}"
           echo "Setup status: ${{ needs.setup.result }}"
 
       - uses: actions/checkout@v2
@@ -276,9 +255,8 @@ jobs:
           CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
           CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_PAST_FUTURE }}
           CI_EVENT: nightly-build
-          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
-          RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
           SETUP_STATUS: ${{ needs.setup.result }}
+          RUNNER_STATUS: ${{ needs.run_check_runners.result }}
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         run: |

.github/workflows/self-past.yml

@@ -15,11 +15,6 @@ on:
       version:
         required: true
         type: string
-      # Use this to control the commit to test against
-      sha:
-        default: 'main'
-        required: false
-        type: string
 
 env:
   HF_HOME: /mnt/cache
@@ -32,21 +27,32 @@ env:
   RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
-  check_runner_status:
-    name: Check Runner Status
+  setup:
+    name: Setup
     runs-on: ubuntu-latest
+    outputs:
+      matrix: ${{ steps.set-matrix.outputs.matrix }}
     steps:
       - name: Checkout transformers
         uses: actions/checkout@v2
         with:
           fetch-depth: 2
 
-      - name: Check Runner Status
-        run: python utils/check_self_hosted_runner.py --target_runners single-gpu-past-ci-runner-docker,multi-gpu-past-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
+      - name: Cleanup
+        run: |
+          rm -rf tests/__pycache__
+          rm -rf tests/models/__pycache__
+          rm -rf reports
 
-  check_runners:
+      - id: set-matrix
+        name: Identify models to test
+        run: |
+          cd tests
+          echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')"
+
+  run_check_runners:
     name: Check Runners
-    needs: check_runner_status
+    needs: setup
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
@@ -59,37 +65,6 @@ jobs:
         run: |
           nvidia-smi
 
-  setup:
-    name: Setup
-    needs: check_runners
-    strategy:
-      matrix:
-        machine_type: [single-gpu, multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
-    container:
-      image: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    outputs:
-      matrix: ${{ steps.set-matrix.outputs.matrix }}
-    steps:
-      - name: Update clone
-        working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.sha }}
-
-      - name: Cleanup
-        working-directory: /transformers
-        run: |
-          rm -rf tests/__pycache__
-          rm -rf tests/models/__pycache__
-          rm -rf reports
-
-      - id: set-matrix
-        working-directory: /transformers
-        name: Identify models to test
-        run: |
-          cd tests
-          echo "::set-output name=matrix::$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')"
-
   run_tests_single_gpu:
     name: Model tests
     strategy:
@@ -101,11 +76,11 @@ jobs:
     container:
       image: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [setup, run_check_runners]
     steps:
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
@@ -136,15 +111,6 @@ jobs:
         continue-on-error: true
         run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
 
-      - name: Save job name
-        if: ${{ always() }}
-        shell: bash
-        run: |
-          matrix_folders=${matrix_folders/'models_'/'models/'}
-          job_name="Model tests ($matrix_folders, ${{ matrix.machine_type }})"
-          echo "$job_name"
-          echo "$job_name" > /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/job_name.txt
-
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -163,11 +129,11 @@ jobs:
     container:
       image: huggingface/transformers-${{ inputs.framework }}-past-${{ inputs.version }}-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [setup, run_check_runners]
     steps:
       - name: Update clone
         working-directory: /transformers
-        run: git fetch && git checkout ${{ inputs.sha }}
+        run: git fetch && git checkout ${{ github.sha }}
 
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
@@ -198,15 +164,6 @@ jobs:
         continue-on-error: true
         run: cat /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/failures_short.txt
 
-      - name: Save job name
-        if: ${{ always() }}
-        shell: bash
-        run: |
-          matrix_folders=${matrix_folders/'models_'/'models/'}
-          job_name="Model tests ($matrix_folders, ${{ matrix.machine_type }})"
-          echo "$job_name"
-          echo "$job_name" > /transformers/reports/${{ matrix.machine_type }}_tests_gpu_${{ matrix.folders }}/job_name.txt
-
       - name: Test suite reports artifacts
         if: ${{ always() }}
         uses: actions/upload-artifact@v2
@@ -218,14 +175,13 @@ jobs:
     name: Send results to webhook
     runs-on: ubuntu-latest
     if: always()
-    needs: [check_runner_status, check_runners, setup, run_tests_single_gpu, run_tests_multi_gpu]
+    needs: [setup, run_check_runners, run_tests_single_gpu, run_tests_multi_gpu]
     steps:
       - name: Preliminary job status
         shell: bash
         # For the meaning of these environment variables, see the job `Setup`
         run: |
-          echo "Runner availability: ${{ needs.check_runner_status.result }}"
-          echo "Runner status: ${{ needs.check_runners.result }}"
+          echo "Runner status: ${{ needs.run_check_runners.result }}"
           echo "Setup status: ${{ needs.setup.result }}"
 
       - uses: actions/checkout@v2
@@ -243,9 +199,8 @@ jobs:
           CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
           CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_PAST_FUTURE }}
           CI_EVENT: Past CI - ${{ inputs.framework }}-${{ inputs.version }}
-          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
-          RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
           SETUP_STATUS: ${{ needs.setup.result }}
+          RUNNER_STATUS: ${{ needs.run_check_runners.result }}
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         run: |

.github/workflows/self-push-caller.yml

@@ -40,8 +40,6 @@ jobs:
     needs: check-for-setup
     if: (github.event_name == 'push') && (needs.check-for-setup.outputs.changed == '1')
     uses: ./.github/workflows/build-docker-images.yml
-    with:
-      image_postfix: "-push-ci"
     secrets: inherit
 
   run_push_ci:

.github/workflows/self-push.yml

@@ -27,43 +27,9 @@ env:
   RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
-  check_runner_status:
-    name: Check Runner Status
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout transformers
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: Check Runner Status
-        run: python utils/check_self_hosted_runner.py --target_runners single-gpu-ci-runner-docker,multi-gpu-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-
-  check_runners:
-    name: Check Runners
-    needs: check_runner_status
-    strategy:
-      matrix:
-        machine_type: [single-gpu, multi-gpu]
-    runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
-    container:
-      image: huggingface/transformers-all-latest-gpu-push-ci
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    steps:
-      - name: NVIDIA-SMI
-        run: |
-          nvidia-smi
-
   setup:
     name: Setup
-    needs: check_runners
-    strategy:
-      matrix:
-        machine_type: [single-gpu, multi-gpu]
-    runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
-    container:
-      image: huggingface/transformers-all-latest-gpu-push-ci
-      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    runs-on: ubuntu-latest
     outputs:
       matrix: ${{ steps.set-matrix.outputs.matrix }}
       test_map: ${{ steps.set-matrix.outputs.test_map }}
@@ -96,8 +62,12 @@ jobs:
           echo "env.CI_BRANCH = ${{ env.CI_BRANCH }}"
           echo "env.CI_SHA = ${{ env.CI_SHA }}"
 
+      - name: Checkout transformers
+        uses: actions/checkout@v2
+        with:
+          fetch-depth: 2
+
       - name: Update clone using environment variables
-        working-directory: /transformers
         run: |
           echo "original branch = $(git branch --show-current)"
           git fetch && git checkout ${{ env.CI_BRANCH }}
@@ -106,28 +76,25 @@ jobs:
           echo "log = $(git log -n 1)"
 
       - name: Cleanup
-        working-directory: /transformers
         run: |
           rm -rf tests/__pycache__
           rm -rf tests/models/__pycache__
           rm -rf reports
 
       - name: Fetch the tests to run
-        working-directory: /transformers
         # TODO: add `git-python` in the docker images
         run: |
           pip install --upgrade git-python
-          python3 utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
+          python utils/tests_fetcher.py --diff_with_last_commit | tee test_preparation.txt
 
       - name: Report fetched tests
         uses: actions/upload-artifact@v2
         with:
           name: test_fetched
-          path: /transformers/test_preparation.txt
+          path: test_preparation.txt
 
       - id: set-matrix
         name: Organize tests into models
-        working-directory: /transformers
         # The `keys` is used as GitHub actions matrix for jobs, i.e. `models/bert`, `tokenization`, `pipeline`, etc.
         # The `test_map` is used to get the actual identified test files under each key.
         # If no test to run (so no `test_map.json` file), create a dummy map (empty matrix will fail)
@@ -144,9 +111,24 @@ jobs:
           echo "::set-output name=matrix::$keys"
           echo "::set-output name=test_map::$test_map"
 
+  run_check_runners:
+    name: Check Runners
+    needs: setup
+    strategy:
+      matrix:
+        machine_type: [single-gpu, multi-gpu]
+    runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
+    container:
+      image: huggingface/transformers-all-latest-gpu
+      options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
+    steps:
+      - name: NVIDIA-SMI
+        run: |
+          nvidia-smi
+
   run_tests_single_gpu:
     name: Model tests
-    needs: setup
+    needs: [setup, run_check_runners]
     # `dummy` means there is no test to run
     if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true
     strategy:
@@ -156,7 +138,7 @@ jobs:
         machine_type: [single-gpu]
     runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
     container:
-      image: huggingface/transformers-all-latest-gpu-push-ci
+      image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       # Necessary to get the correct branch name and commit SHA for `workflow_run` event
@@ -231,7 +213,7 @@ jobs:
 
   run_tests_multi_gpu:
     name: Model tests
-    needs: setup
+    needs: [setup, run_check_runners]
     # `dummy` means there is no test to run
     if: contains(fromJson(needs.setup.outputs.matrix), 'dummy') != true
     strategy:
@@ -241,7 +223,7 @@ jobs:
         machine_type: [multi-gpu]
     runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
     container:
-      image: huggingface/transformers-all-latest-gpu-push-ci
+      image: huggingface/transformers-all-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       # Necessary to get the correct branch name and commit SHA for `workflow_run` event
@@ -318,7 +300,7 @@ jobs:
 
   run_tests_torch_cuda_extensions_single_gpu:
     name: Torch CUDA extension tests
-    needs: setup
+    needs: [setup, run_check_runners]
     if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended')
     strategy:
       fail-fast: false
@@ -326,7 +308,7 @@ jobs:
         machine_type: [single-gpu]
     runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
     container:
-      image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
+      image: huggingface/transformers-pytorch-deepspeed-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       # Necessary to get the correct branch name and commit SHA for `workflow_run` event
@@ -400,7 +382,7 @@ jobs:
 
   run_tests_torch_cuda_extensions_multi_gpu:
     name: Torch CUDA extension tests
-    needs: setup
+    needs: [setup, run_check_runners]
     if: contains(fromJson(needs.setup.outputs.matrix), 'deepspeed') || contains(fromJson(needs.setup.outputs.matrix), 'extended')
     strategy:
       fail-fast: false
@@ -408,7 +390,7 @@ jobs:
         machine_type: [multi-gpu]
     runs-on: [self-hosted, docker-gpu, '${{ matrix.machine_type }}']
     container:
-      image: huggingface/transformers-pytorch-deepspeed-latest-gpu-push-ci
+      image: huggingface/transformers-pytorch-deepspeed-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
     steps:
       # Necessary to get the correct branch name and commit SHA for `workflow_run` event
@@ -485,9 +467,8 @@ jobs:
     runs-on: ubuntu-latest
     if: always()
     needs: [
-        check_runner_status,
-        check_runners,
         setup,
+        run_check_runners,
         run_tests_single_gpu,
         run_tests_multi_gpu,
         run_tests_torch_cuda_extensions_single_gpu,
@@ -498,9 +479,8 @@ jobs:
         shell: bash
         # For the meaning of these environment variables, see the job `Setup`
         run: |
-          echo "Runner availability: ${{ needs.check_runner_status.result }}"
           echo "Setup status: ${{ needs.setup.result }}"
-          echo "Runner status: ${{ needs.check_runners.result }}"
+          echo "Runner status: ${{ needs.run_check_runners.result }}"
 
       # Necessary to get the correct branch name and commit SHA for `workflow_run` event
       # We also take into account the `push` event (we might want to test some changes in a branch)
@@ -526,11 +506,6 @@ jobs:
           echo "env.CI_SHA = ${{ env.CI_SHA }}"
 
       - uses: actions/checkout@v2
-        # To avoid failure when multiple commits are merged into `main` in a short period of time.
-        # Checking out to an old commit beyond the fetch depth will get an error `fatal: reference is not a tree: ...
-        # (Only required for `workflow_run` event, where we get the latest HEAD on `main` instead of the event commit)
-        with:
-          fetch-depth: 20
 
       - name: Update clone using environment variables
         run: |
@@ -552,9 +527,8 @@ jobs:
           CI_TITLE_PUSH: ${{ github.event.head_commit.message }}
           CI_TITLE_WORKFLOW_RUN: ${{ github.event.workflow_run.head_commit.message }}
           CI_SHA: ${{ env.CI_SHA }}
-          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
-          RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
           SETUP_STATUS: ${{ needs.setup.result }}
+          RUNNER_STATUS: ${{ needs.run_check_runners.result }}
 
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.

.github/workflows/self-scheduled.yml

@@ -22,21 +22,8 @@ env:
   RUN_PT_TF_CROSS_TESTS: 1
 
 jobs:
-  check_runner_status:
-    name: Check Runner Status
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout transformers
-        uses: actions/checkout@v2
-        with:
-          fetch-depth: 2
-
-      - name: Check Runner Status
-        run: python utils/check_self_hosted_runner.py --target_runners single-gpu-scheduled-ci-runner-docker,multi-gpu-scheduled-ci-runner-docker --token ${{ secrets.ACCESS_REPO_INFO_TOKEN }}
-
-  check_runners:
+  run_check_runners:
     name: Check Runners
-    needs: check_runner_status
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
@@ -51,7 +38,7 @@ jobs:
 
   setup:
     name: Setup
-    needs: check_runners
+    needs: run_check_runners
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
@@ -95,7 +82,7 @@ jobs:
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
@@ -148,7 +135,7 @@ jobs:
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
@@ -196,7 +183,7 @@ jobs:
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Update clone
         working-directory: /transformers
@@ -239,7 +226,7 @@ jobs:
     container:
       image: huggingface/transformers-pytorch-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Update clone
         working-directory: /transformers
@@ -256,8 +243,10 @@ jobs:
 
       - name: Run all pipeline tests on GPU
         working-directory: /transformers
+        env:
+          RUN_PIPELINE_TESTS: yes
         run: |
-          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_tests_torch_pipeline_gpu tests/pipelines
+          python3 -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=${{ matrix.machine_type }}_tests_torch_pipeline_gpu tests
 
       - name: Failure short reports
         if: ${{ failure() }}
@@ -281,7 +270,7 @@ jobs:
     container:
       image: huggingface/transformers-tensorflow-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
-    needs: setup
+    needs: [run_check_runners, setup]
     steps:
       - name: Update clone
         working-directory: /transformers
@@ -299,8 +288,10 @@ jobs:
 
       - name: Run all pipeline tests on GPU
         working-directory: /transformers
+        env:
+          RUN_PIPELINE_TESTS: yes
         run: |
-          python3 -m pytest -n 1 -v --dist=loadfile --make-reports=${{ matrix.machine_type }}_tests_tf_pipeline_gpu tests/pipelines
+          python3 -m pytest -n 1 -v --dist=loadfile -m is_pipeline_test --make-reports=${{ matrix.machine_type }}_tests_tf_pipeline_gpu tests
 
       - name: Failure short reports
         if: ${{ always() }}
@@ -321,7 +312,7 @@ jobs:
       matrix:
         machine_type: [single-gpu, multi-gpu]
     runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
-    needs: setup
+    needs: [run_check_runners, setup]
     container:
       image: huggingface/transformers-pytorch-deepspeed-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -371,8 +362,7 @@ jobs:
     runs-on: ubuntu-latest
     if: always()
     needs: [
-      check_runner_status,
-      check_runners,
+      run_check_runners,
       setup,
       run_tests_single_gpu,
       run_tests_multi_gpu,
@@ -386,8 +376,7 @@ jobs:
         shell: bash
         # For the meaning of these environment variables, see the job `Setup`
         run: |
-          echo "Runner availability: ${{ needs.check_runner_status.result }}"
-          echo "Runner status: ${{ needs.check_runners.result }}"
+          echo "Runner status: ${{ needs.run_check_runners.result }}"
           echo "Setup status: ${{ needs.setup.result }}"
 
       - uses: actions/checkout@v2
@@ -400,9 +389,8 @@ jobs:
           CI_SLACK_CHANNEL_DUMMY_TESTS: ${{ secrets.CI_SLACK_CHANNEL_DUMMY_TESTS }}
           CI_SLACK_REPORT_CHANNEL_ID: ${{ secrets.CI_SLACK_CHANNEL_ID_DAILY }}
           CI_EVENT: scheduled
-          RUNNER_STATUS: ${{ needs.check_runner_status.result }}
-          RUNNER_ENV_STATUS: ${{ needs.check_runners.result }}
           SETUP_STATUS: ${{ needs.setup.result }}
+          RUNNER_STATUS: ${{ needs.run_check_runners.result }}
         # We pass `needs.setup.outputs.matrix` as the argument. A processing in `notification_service.py` to change
         # `models/bert` to `models_bert` is required, as the artifact names use `_` instead of `/`.
         run: |

.github/workflows/stale.yml

@@ -12,10 +12,10 @@ jobs:
     env:
       GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v2
 
     - name: Setup Python
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v1
       with:
         python-version: 3.7
 
@@ -24,4 +24,4 @@ jobs:
         pip install PyGithub
     - name: Close stale issues
       run: |
-        python scripts/stale.py
+        python scripts/stale.py

Makefile

@@ -41,7 +41,6 @@ repo-consistency:
 	python utils/check_inits.py
 	python utils/check_config_docstrings.py
 	python utils/tests_fetcher.py --sanity_check
-	python utils/update_metadata.py --check-only
 
 # this target runs checks on all files
 

README.md

@@ -43,8 +43,7 @@ limitations under the License.
         <b>English</b> |
         <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> 
+        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a>
     <p>
 </h4>
 
@@ -56,13 +55,13 @@ limitations under the License.
     <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
 </h3>
 
-🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.
+🤗 Transformers provides thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio. 
 
 These models can be applied on:
 
-* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages.
-* 🖼️ Images, for tasks like image classification, object detection, and segmentation.
-* 🗣️ Audio, for tasks like speech recognition and audio classification.
+* 📝 Text, for tasks like text classification, information extraction, question answering, summarization, translation, text generation, in over 100 languages. 
+* 🖼️ Images, for tasks like image classification, object detection, and segmentation. 
+* 🗣️ Audio, for tasks like speech recognition and audio classification. 
 
 Transformer models can also perform tasks on **several modalities combined**, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
 
@@ -134,7 +133,7 @@ Many tasks have a pre-trained `pipeline` ready to go, in NLP but also in compute
 >>> image = Image.open(image_data)
 
 # Allocate a pipeline for object detection
->>> object_detector = pipeline('object-detection')
+>>> object_detector = pipeline('object_detection')
 >>> object_detector(image)
 [{'score': 0.9982201457023621,
   'label': 'remote',
@@ -228,7 +227,7 @@ You should install 🤗 Transformers in a [virtual environment](https://docs.pyt
 First, create a virtual environment with the version of Python you're going to use and activate it.
 
 Then, you will need to install at least one of Flax, PyTorch or TensorFlow.
-Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific installation command for your platform.
+Please refer to [TensorFlow installation page](https://www.tensorflow.org/install/), [PyTorch installation page](https://pytorch.org/get-started/locally/#start-locally) and/or [Flax](https://github.com/google/flax#quick-install) and [Jax](https://github.com/google/jax#installation) installation pages regarding the specific install command for your platform.
 
 When one of those backends has been installed, 🤗 Transformers can be installed using pip as follows:
 
@@ -250,8 +249,6 @@ conda install -c huggingface transformers
 
 Follow the installation pages of Flax, PyTorch or TensorFlow to see how to install them with conda.
 
-> **_NOTE:_**  On Windows, you may be prompted to activate Developer Mode in order to benefit from caching. If this is not an option for you, please let us know in [this issue](https://github.com/huggingface/huggingface_hub/issues/1062).
-
 ## Model architectures
 
 **[All the model checkpoints](https://huggingface.co/models)** provided by 🤗 Transformers are seamlessly integrated from the huggingface.co [model hub](https://huggingface.co) where they are uploaded directly by [users](https://huggingface.co/users) and [organizations](https://huggingface.co/organizations).
@@ -279,7 +276,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
 1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
 1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
-1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
 1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
 1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
@@ -289,7 +285,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
@@ -301,7 +296,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
-1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
 1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
@@ -310,7 +304,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
-1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
 1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
@@ -323,7 +316,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
-1. **[LiLT](https://huggingface.co/docs/transformers/main/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
@@ -331,7 +323,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
 1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
 1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
 1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
 1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
 1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
@@ -377,7 +368,6 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)**  (from HuggingFace).
 1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
@@ -390,12 +380,10 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
 1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
 1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
 1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
@@ -411,7 +399,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 
 To check if each model has an implementation in Flax, PyTorch or TensorFlow, or has an associated tokenizer backed by the 🤗 Tokenizers library, refer to [this table](https://huggingface.co/docs/transformers/index#supported-frameworks).
 
-These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://github.com/huggingface/transformers/tree/main/examples).
+These implementations have been tested on several datasets (see the example scripts) and should match the performance of the original implementations. You can find more details on performance in the Examples section of the [documentation](https://huggingface.co/docs/transformers/examples).
 
 
 ## Learn more

README_es.md

@@ -1,444 +0,0 @@
-<!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
--->
-
-<p align="center">
-    <br>
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" width="400"/>
-    <br>
-<p>
-<p align="center">
-    <a href="https://circleci.com/gh/huggingface/transformers">
-        <img alt="Build" src="https://img.shields.io/circleci/build/github/huggingface/transformers/main">
-    </a>
-    <a href="https://github.com/huggingface/transformers/blob/main/LICENSE">
-        <img alt="GitHub" src="https://img.shields.io/github/license/huggingface/transformers.svg?color=blue">
-    </a>
-    <a href="https://huggingface.co/docs/transformers/index">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/transformers/index.svg?down_color=red&down_message=offline&up_message=online">
-    </a>
-    <a href="https://github.com/huggingface/transformers/releases">
-        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/transformers.svg">
-    </a>
-    <a href="https://github.com/huggingface/transformers/blob/main/CODE_OF_CONDUCT.md">
-        <img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg">
-    </a>
-    <a href="https://zenodo.org/badge/latestdoi/155220641"><img src="https://zenodo.org/badge/155220641.svg" alt="DOI"></a>
-</p>
-
-<h4 align="center">
-    <p>
-        <a href="https://github.com/huggingface/transformers/">English</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
-        <b>Español</b> 
-    <p>
-</h4>
-
-<h3 align="center">
-    <p>Lo último de Machine Learning para JAX, PyTorch y TensorFlow</p>
-</h3>
-
-<h3 align="center">
-    <a href="https://hf.co/course"><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/course_banner.png"></a>
-</h3>
-
-🤗 Transformers aporta miles de modelos preentrenados Para realizar tareas en diferentes modalidades como texto, vision, y audio. 
-
-Estos modelos pueden ser aplicados en:
-
-* 📝 Texto, Para tareas como clasificación de texto, extracción de información, responder preguntas, resumir, traducir, generación de texto, en más de 100 idiomas. 
-* 🖼️ Imágenes, para tareas como clasificación de imágenes, detección the objetos, y segmentación. 
-* 🗣️ Audio, para tareas como reconocimiento de voz y clasificación de audio. 
-
-Los modelos de Transformer también pueden realizar tareas en **muchas modalidades combinadas**, como responder pregunstas, reconocimiento de carácteres ópticos,extracción de información de documentos escaneados, clasificación de video, y respuesta de preguntas visuales.
-
-🤗 Transformers aporta APIs para descargar rápidamente y usar estos modelos preentrenados en un texto dado, afinarlos en tus propios sets de datos y compartirlos con la comunidad en nuestro [centro de modelos](https://huggingface.co/models). Al mismo tiempo, cada módulo de Python que define una arquitectura es completamente independiente y se puede modificar para permitir experimentos de investigación rápidos.
-
-🤗 Transformers está respaldado por las tres bibliotecas de deep learning más populares — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) y [TensorFlow](https://www.tensorflow.org/) — con una perfecta integración entre ellos. Es sencillo entrenar sus modelos con uno antes de cargarlos para la inferencia con el otro.
-
-## Demostraciones en línea
-
-Puedes probar la mayoría de nuestros modelos directamente en sus páginas desde el [centro de modelos](https://huggingface.co/models). También ofrecemos [alojamiento de modelos privados, control de versiones y una API de inferencia](https://huggingface.co/pricing) para modelos públicos y privados.
-
-Aquí hay algunos ejemplos:
-
- En procesamiento del lenguaje natural:
-- [Terminación de palabras enmascaradas con BERT](https://huggingface.co/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
-- [Reconocimiento del nombre de la entidad con Electra](https://huggingface.co/dbmdz/electra-large-discriminator-finetuned-conll03-english?text=My+name+is+Sarah+and+I+live+in+London+city)
-- [Generación de texto con GPT-2](https://huggingface.co/gpt2?text=A+long+time+ago%2C+)
-- [Inferencia del lenguaje natural con RoBERTa](https://huggingface.co/roberta-large-mnli?text=The+dog+was+lost.+Nobody+lost+any+animal)
-- [Resumen con BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Tower+surpassed+the+Washington+Monument+to+become+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Viaduct)
-- [Responder a preguntas con DistilBERT](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
-- [Traducción con T5](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
-
-En visión de ordenador:
-- [Clasificación de imágenes con ViT](https://huggingface.co/google/vit-base-patch16-224)
-- [Detección de objetos con DETR](https://huggingface.co/facebook/detr-resnet-50)
-- [Segmentación semántica con SegFormer](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512)
-- [Segmentación panóptica con DETR](https://huggingface.co/facebook/detr-resnet-50-panoptic)
-
-En Audio:
-- [Reconocimiento de voz automático con Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base-960h)
-- [Detección de palabras clave con Wav2Vec2](https://huggingface.co/superb/wav2vec2-base-superb-ks)
-
-En tareas multimodales:
-- [Respuesta visual a preguntas con ViLT](https://huggingface.co/dandelin/vilt-b32-finetuned-vqa)
-
-**[Escribe con Transformer](https://transformer.huggingface.co)**, construido por el equipo de Hugging Face, es la demostración oficial de las capacidades de generación de texto de este repositorio.
-
-## Si está buscando soporte personalizado del equipo de Hugging Face
-
-<a target="_blank" href="https://huggingface.co/support">
-    <img alt="HuggingFace Expert Acceleration Program" src="https://cdn-media.huggingface.co/marketing/transformers/new-support-improved.png" style="max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
-</a><br>
-
-## Tour rápido
-
-Para usar inmediatamente un modelo en una entrada determinada (texto, imagen, audio, ...), proporcionamos la API de `pipeline`. Los pipelines agrupan un modelo previamente entrenado con el preprocesamiento que se usó durante el entrenamiento de ese modelo. Aquí se explica cómo usar rápidamente un pipeline para clasificar textos positivos frente a negativos:
-
-```python
->>> from transformers import pipeline
-
-# Allocate a pipeline for sentiment-analysis
->>> classifier = pipeline('sentiment-analysis')
->>> classifier('We are very happy to introduce pipeline to the transformers repository.')
-[{'label': 'POSITIVE', 'score': 0.9996980428695679}]
-```
-
-La segunda línea de código descarga y almacena en caché el modelo previamente entrenado que usa la canalización, mientras que la tercera lo evalúa en el texto dado. Aquí la respuesta es "positiva" con una confianza del 99,97%.
-
-Muchas tareas tienen un `pipeline` preentrenado listo para funcionar, en NLP pero también en visión por ordenador y habla. Por ejemplo, podemos extraer fácilmente los objetos detectados en una imagen:
-
-``` python
->>> import requests
->>> from PIL import Image
->>> from transformers import pipeline
-
-# Download an image with cute cats
->>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png"
->>> image_data = requests.get(url, stream=True).raw
->>> image = Image.open(image_data)
-
-# Allocate a pipeline for object detection
->>> object_detector = pipeline('object_detection')
->>> object_detector(image)
-[{'score': 0.9982201457023621,
-  'label': 'remote',
-  'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
- {'score': 0.9960021376609802,
-  'label': 'remote',
-  'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
- {'score': 0.9954745173454285,
-  'label': 'couch',
-  'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
- {'score': 0.9988006353378296,
-  'label': 'cat',
-  'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
- {'score': 0.9986783862113953,
-  'label': 'cat',
-  'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}}]
-```
-
-Aquí obtenemos una lista de objetos detectados en la imagen, con un cuadro que rodea el objeto y una puntuación de confianza. Aquí está la imagen original a la derecha, con las predicciones mostradas a la izquierda:
-
-<h3 align="center">
-    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample.png" width="400"></a>
-    <a><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/coco_sample_post_processed.png" width="400"></a>
-</h3>
-
-Puedes obtener más información sobre las tareas admitidas por la API de `pipeline` en [este tutorial](https://huggingface.co/docs/transformers/task_summary).
-
-Además de `pipeline`, para descargar y usar cualquiera de los modelos previamente entrenados en su tarea dada, todo lo que necesita son tres líneas de código. Aquí está la versión de PyTorch:
-```python
->>> from transformers import AutoTokenizer, AutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
->>> model = AutoModel.from_pretrained("bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="pt")
->>> outputs = model(**inputs)
-```
-
-Y aquí está el código equivalente para TensorFlow:
-```python
->>> from transformers import AutoTokenizer, TFAutoModel
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
->>> model = TFAutoModel.from_pretrained("bert-base-uncased")
-
->>> inputs = tokenizer("Hello world!", return_tensors="tf")
->>> outputs = model(**inputs)
-```
-
-El tokenizador es responsable de todo el preprocesamiento que espera el modelo preentrenado y se puede llamar directamente en una sola cadena (como en los ejemplos anteriores) o en una lista. Dará como resultado un diccionario que puedes usar en el código descendente o simplemente pasarlo directamente a su modelo usando el operador de desempaquetado de argumento **.
-
-El modelo en si es un [Pytorch `nn.Module`](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) normal o un [TensorFlow `tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) (dependiendo De tu backend) que puedes usar de forma habitual. [Este tutorial](https://huggingface.co/docs/transformers/training) explica cómo integrar un modelo de este tipo en un ciclo de entrenamiento PyTorch o TensorFlow clásico, o como usar nuestra API `Trainer` para ajustar rápidamente un nuevo conjunto de datos.
-
-## ¿Por qué debo usar transformers?
-
-1. Modelos de última generación fáciles de usar:
-    - Alto rendimiento en comprensión y generación de lenguaje natural, visión artificial y tareas de audio.
-    - Baja barrera de entrada para educadores y profesionales.
-    - Pocas abstracciones de cara al usuario con solo tres clases para aprender.
-    - Una API unificada para usar todos nuestros modelos preentrenados.
-
-1. Menores costes de cómputo, menor huella de carbono:
-    - Los investigadores pueden compartir modelos entrenados en lugar de siempre volver a entrenar.
-    - Los profesionales pueden reducir el tiempo de cómputo y los costos de producción.
-    - Docenas de arquitecturas con más de 60 000 modelos preentrenados en todas las modalidades.
-
-1. Elija el marco adecuado para cada parte de la vida útil de un modelo:
-    - Entrene modelos de última generación en 3 líneas de código.
-    - Mueva un solo modelo entre los marcos TF2.0/PyTorch/JAX a voluntad.
-    - Elija sin problemas el marco adecuado para la formación, la evaluación y la producción.
-
-1. Personalice fácilmente un modelo o un ejemplo según sus necesidades:
-    - Proporcionamos ejemplos de cada arquitectura para reproducir los resultados publicados por sus autores originales..
-    - Los internos del modelo están expuestos lo más consistentemente posible..
-    - Los archivos modelo se pueden usar independientemente de la biblioteca para experimentos rápidos.
-
-## ¿Por qué no debería usar transformers?
-
-- Esta biblioteca no es una caja de herramientas modular de bloques de construcción para redes neuronales. El código en los archivos del modelo no se refactoriza con abstracciones adicionales a propósito, de modo que los investigadores puedan iterar rápidamente en cada uno de los modelos sin sumergirse en abstracciones/archivos adicionales.
-- La API de entrenamiento no está diseñada para funcionar en ningún modelo, pero está optimizada para funcionar con los modelos proporcionados por la biblioteca. Para bucles genéricos de aprendizaje automático, debe usar otra biblioteca (posiblemente, [Accelerate](https://huggingface.co/docs/accelerate)).
-- Si bien nos esforzamos por presentar tantos casos de uso como sea posible, los scripts en nuestra [carpeta de ejemplos](https://github.com/huggingface/transformers/tree/main/examples) son solo eso: ejemplos. Se espera que no funcionen de forma inmediata en su problema específico y que deba cambiar algunas líneas de código para adaptarlas a sus necesidades.
-
-## Instalación
-
-### Con pip
-
-Este repositorio está probado en Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+ y TensorFlow 2.3+.
-
-Deberías instalar 🤗 Transformers en un [ambiente virtual](https://docs.python.org/3/library/venv.html). Si no estas familiarizado con los entornos virtuales de Python, consulta la [guía de usuario](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
-
-Primero, crea un entorno virtual con la versión de Python que vas a usar y actívalo.
-
-Luego, deberás instalar al menos uno de Flax, PyTorch o TensorFlow.
-Por favor, ve a la [página de instalación de TensorFlow](https://www.tensorflow.org/install/), [página de instalación de PyTorch](https://pytorch.org/get-started/locally/#start-locally) y/o las páginas de instalación de [Flax](https://github.com/google/flax#quick-install) y [Jax](https://github.com/google/jax#installation) con respecto al comando de instalación específico para tu plataforma.
-
-Cuando se ha instalado uno de esos backends, los 🤗 Transformers se pueden instalar usando pip de la siguiente manera:
-
-```bash
-pip install transformers
-```
-
-Si deseas jugar con los ejemplos o necesitas la última versión del código y no puedes esperar a una nueva versión, tienes que [instalar la librería de la fuente](https://huggingface.co/docs/transformers/installation#installing-from-source).
-
-### Con conda
-
-Desde la versión v4.0.0 de Transformers, ahora tenemos un canal conda: `huggingface`.
-
-🤗 Transformers se puede instalar usando conda de la siguiente manera:
-
-```shell script
-conda install -c huggingface transformers
-```
-
-Sigue las páginas de instalación de Flax, PyTorch o TensorFlow para ver cómo instalarlos con conda.
-
-> **_NOTA:_**  En Windows, es posible que se le pida que active el modo de desarrollador para beneficiarse del almacenamiento en caché. Si esta no es una opción para usted, háganoslo saber en [esta issue](https://github.com/huggingface/huggingface_hub/issues/1062).
-
-## Arquitecturas modelo
-
-**[Todos los puntos de control del modelo](https://huggingface.co/models)** aportados por 🤗 Transformers están perfectamente integrados desde huggingface.co [Centro de modelos](https://huggingface.co) donde son subidos directamente por los [usuarios](https://huggingface.co/users) y [organizaciones](https://huggingface.co/organizations).
-
-Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=https://huggingface.co/api/shields/models&color=brightgreen)
-
-🤗 Transformers actualmente proporciona las siguientes arquitecturas (ver [aquí](https://huggingface.co/docs/transformers/model_summary) para un resumen de alto nivel de cada uno de ellas.):
-
-1. **[ALBERT](https://huggingface.co/docs/transformers/model_doc/albert)** (from Google Research and the Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
-1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (from Facebook) released with the paper [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461) by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
-1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (from École polytechnique) released with the paper [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
-1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (from VinAI Research) released with the paper [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
-1. **[BEiT](https://huggingface.co/docs/transformers/model_doc/beit)** (from Microsoft) released with the paper [BEiT: BERT Pre-Training of Image Transformers](https://arxiv.org/abs/2106.08254) by Hangbo Bao, Li Dong, Furu Wei.
-1. **[BERT](https://huggingface.co/docs/transformers/model_doc/bert)** (from Google) released with the paper [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://arxiv.org/abs/1810.04805) by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
-1. **[BERT For Sequence Generation](https://huggingface.co/docs/transformers/model_doc/bert-generation)** (from Google) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[BERTweet](https://huggingface.co/docs/transformers/model_doc/bertweet)** (from VinAI Research) released with the paper [BERTweet: A pre-trained language model for English Tweets](https://aclanthology.org/2020.emnlp-demos.2/) by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
-1. **[BigBird-Pegasus](https://huggingface.co/docs/transformers/model_doc/bigbird_pegasus)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
-1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
-1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigSicence Workshop](https://bigscience.huggingface.co/).
-1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
-1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
-1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
-1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
-1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
-1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
-1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
-1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
-1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
-1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
-1. **[CTRL](https://huggingface.co/docs/transformers/model_doc/ctrl)** (from Salesforce) released with the paper [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://arxiv.org/abs/1909.05858) by Nitish Shirish Keskar*, Bryan McCann*, Lav R. Varshney, Caiming Xiong and Richard Socher.
-1. **[CvT](https://huggingface.co/docs/transformers/model_doc/cvt)** (from Microsoft) released with the paper [CvT: Introducing Convolutions to Vision Transformers](https://arxiv.org/abs/2103.15808) by Haiping Wu, Bin Xiao, Noel Codella, Mengchen Liu, Xiyang Dai, Lu Yuan, Lei Zhang.
-1. **[Data2Vec](https://huggingface.co/docs/transformers/model_doc/data2vec)** (from Facebook) released with the paper [Data2Vec:  A General Framework for Self-supervised Learning in Speech, Vision and Language](https://arxiv.org/abs/2202.03555) by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
-1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
-1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
-1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
-1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
-1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
-1. **[DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), RoBERTa into [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation), Multilingual BERT into [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation) and a German version of DistilBERT.
-1. **[DiT](https://huggingface.co/docs/transformers/model_doc/dit)** (from Microsoft Research) released with the paper [DiT: Self-supervised Pre-training for Document Image Transformer](https://arxiv.org/abs/2203.02378) by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
-1. **[Donut](https://huggingface.co/docs/transformers/model_doc/donut)** (from NAVER), released together with the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewook Kim, Teakgyu Hong, Moonbin Yim, Jeongyeon Nam, Jinyoung Park, Jinyeong Yim, Wonseok Hwang, Sangdoo Yun, Dongyoon Han, Seunghyun Park.
-1. **[DPR](https://huggingface.co/docs/transformers/model_doc/dpr)** (from Facebook) released with the paper [Dense Passage Retrieval for Open-Domain Question Answering](https://arxiv.org/abs/2004.04906) by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
-1. **[DPT](https://huggingface.co/docs/transformers/master/model_doc/dpt)** (from Intel Labs) released with the paper [Vision Transformers for Dense Prediction](https://arxiv.org/abs/2103.13413) by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
-1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
-1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
-1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
-1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
-1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
-1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
-1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
-1. **[Funnel Transformer](https://huggingface.co/docs/transformers/model_doc/funnel)** (from CMU/Google Brain) released with the paper [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://arxiv.org/abs/2006.03236) by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
-1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
-1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
-1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
-1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
-1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
-1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
-1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
-1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
-1. **[Hubert](https://huggingface.co/docs/transformers/model_doc/hubert)** (from Facebook) released with the paper [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://arxiv.org/abs/2106.07447) by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
-1. **[I-BERT](https://huggingface.co/docs/transformers/model_doc/ibert)** (from Berkeley) released with the paper [I-BERT: Integer-only BERT Quantization](https://arxiv.org/abs/2101.01321) by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
-1. **[ImageGPT](https://huggingface.co/docs/transformers/model_doc/imagegpt)** (from OpenAI) released with the paper [Generative Pretraining from Pixels](https://openai.com/blog/image-gpt/) by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
-1. **[LayoutLM](https://huggingface.co/docs/transformers/model_doc/layoutlm)** (from Microsoft Research Asia) released with the paper [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://arxiv.org/abs/1912.13318) by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
-1. **[LayoutLMv2](https://huggingface.co/docs/transformers/model_doc/layoutlmv2)** (from Microsoft Research Asia) released with the paper [LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding](https://arxiv.org/abs/2012.14740) by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
-1. **[LayoutLMv3](https://huggingface.co/docs/transformers/model_doc/layoutlmv3)** (from Microsoft Research Asia) released with the paper [LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking](https://arxiv.org/abs/2204.08387) by Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei.
-1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
-1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
-1. **[LiLT](https://huggingface.co/docs/transformers/main/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
-1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
-1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
-1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
-1. **[LXMERT](https://huggingface.co/docs/transformers/model_doc/lxmert)** (from UNC Chapel Hill) released with the paper [LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering](https://arxiv.org/abs/1908.07490) by Hao Tan and Mohit Bansal.
-1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
-1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
-1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
-1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
-1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
-1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
-1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
-1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
-1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (from Studio Ousia) released with the paper [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
-1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (from CMU/Google Brain) released with the paper [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) by Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou.
-1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (from Apple) released with the paper [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) by Sachin Mehta and Mohammad Rastegari.
-1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (from Microsoft Research) released with the paper [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
-1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (from Google AI) released with the paper [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
-1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (from RUC AI Box) released with the paper [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) by Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen.
-1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (from Huawei Noah’s Ark Lab) released with the paper [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) by Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu.
-1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (from Meta) released with the paper [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) by the NLLB team.
-1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (from the University of Wisconsin - Madison) released with the paper [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
-1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
-1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
-1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, and Peter J. Liu.
-1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
-1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
-1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
-1. **[PoolFormer](https://huggingface.co/docs/transformers/model_doc/poolformer)** (from Sea AI Labs) released with the paper [MetaFormer is Actually What You Need for Vision](https://arxiv.org/abs/2111.11418) by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
-1. **[ProphetNet](https://huggingface.co/docs/transformers/model_doc/prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[QDQBert](https://huggingface.co/docs/transformers/model_doc/qdqbert)** (from NVIDIA) released with the paper [Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation](https://arxiv.org/abs/2004.09602) by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
-1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
-1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
-1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Platforms) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
-1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/abs/2010.12821) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
-1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
-1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
-1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
-1. **[SEW](https://huggingface.co/docs/transformers/model_doc/sew)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SEW-D](https://huggingface.co/docs/transformers/model_doc/sew_d)** (from ASAPP) released with the paper [Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition](https://arxiv.org/abs/2109.06870) by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
-1. **[SpeechToTextTransformer](https://huggingface.co/docs/transformers/model_doc/speech_to_text)** (from Facebook), released together with the paper [fairseq S2T: Fast Speech-to-Text Modeling with fairseq](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
-1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook), released together with the paper [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
-1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (from Tel Aviv University), released together with the paper [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
-1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (from Berkeley) released with the paper [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
-1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (from Microsoft) released with the paper [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
-1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (from Microsoft) released with the paper [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) by Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo.
-1. **[T5](https://huggingface.co/docs/transformers/model_doc/t5)** (from Google AI) released with the paper [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/abs/1910.10683) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
-1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
-1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)**  (from HuggingFace).
-1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
-1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
-1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
-1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
-1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
-1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
-1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
-1. **[VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
-1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
-1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
-1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
-1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
-1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
-1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
-1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
-1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
-1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
-1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
-1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
-1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
-1. **[XLM-RoBERTa-XL](https://huggingface.co/docs/transformers/model_doc/xlm-roberta-xl)** (from Facebook AI), released together with the paper [Larger-Scale Transformers for Multilingual Masked Language Modeling](https://arxiv.org/abs/2105.00572) by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
-1. **[XLNet](https://huggingface.co/docs/transformers/model_doc/xlnet)** (from Google/CMU) released with the paper [​XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-1. **[XLS-R](https://huggingface.co/docs/transformers/model_doc/xls_r)** (from Facebook AI) released with the paper [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://arxiv.org/abs/2111.09296) by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
-1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
-1. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
-1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling](https://arxiv.org/abs/2111.09714) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
-1. ¿Quieres aportar un nuevo modelo? Hemos agregado una **guía detallada y plantillas** para guiarte en el proceso de agregar un nuevo modelo. Puedes encontrarlos en la carpeta de [`templates`](./templates) del repositorio. Asegúrate de revisar las [pautas de contribución](./CONTRIBUTING.md) y comunícate con los mantenedores o abra un problema para recopilar comentarios antes de comenzar su PR.
-
-Para comprobar si cada modelo tiene una implementación en Flax, PyTorch o TensorFlow, o tiene un tokenizador asociado respaldado por la librería 🤗 Tokenizers , ve a [esta tabla](https://huggingface.co/docs/transformers/index#supported-frameworks).
-
-Estas implementaciones se han probado en varios conjuntos de datos (consulte los scripts de ejemplo) y deberían coincidir con el rendimiento de las implementaciones originales. Puede encontrar más detalles sobre el rendimiento en la sección Examples de la [documentación](https://github.com/huggingface/transformers/tree/main/examples).
-
-
-## Aprender más
-
-| Sección | Descripción |
-|-|-|
-| [Documentación](https://huggingface.co/docs/transformers/) | Toda la documentación de la API y tutoriales |
-| [Resumen de tareas](https://huggingface.co/docs/transformers/task_summary) | Tareas soportadas 🤗 Transformers |
-| [Tutorial de preprocesAmiento](https://huggingface.co/docs/transformers/preprocessing) | Usando la clase `Tokenizer` para preparar datos para los modelos |
-| [Entrenamiento y puesta a punto](https://huggingface.co/docs/transformers/training) | Usando los modelos aportados por 🤗 Transformers en un bucle de entreno de PyTorch/TensorFlow y la API de `Trainer` |
-| [Recorrido rápido: secuencias de comandos de ajuste/uso](https://github.com/huggingface/transformers/tree/main/examples) | Scripts de ejemplo para ajustar modelos en una amplia gama de tareas |
-| [Compartir y subir modelos](https://huggingface.co/docs/transformers/model_sharing) | Carga y comparte tus modelos perfeccionados con la comunidad |
-| [Migración](https://huggingface.co/docs/transformers/migration) | Migra a 🤗 Transformers desde `pytorch-transformers` o `pytorch-pretrained-bert` |
-
-## Citación
-
-Ahora nosotros tenemos un [papel](https://www.aclweb.org/anthology/2020.emnlp-demos.6/) que puedes citar para la librería de  🤗 Transformers:
-```bibtex
-@inproceedings{wolf-etal-2020-transformers,
-    title = "Transformers: State-of-the-Art Natural Language Processing",
-    author = "Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush",
-    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
-    month = oct,
-    year = "2020",
-    address = "Online",
-    publisher = "Association for Computational Linguistics",
-    url = "https://www.aclweb.org/anthology/2020.emnlp-demos.6",
-    pages = "38--45"
-}
-```

README_ko.md

@@ -43,8 +43,7 @@ limitations under the License.
         <a href="https://github.com/huggingface/transformers/">English</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
-        <b>한국어</b> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> 
+        <b>한국어</b>
     <p>
 </h4>
 
@@ -60,7 +59,7 @@ limitations under the License.
 
 🤗 Transformers는 이러한 사전학습 모델을 빠르게 다운로드해 특정 텍스트에 사용하고, 원하는 데이터로 fine-tuning해 커뮤니티나 우리의 [모델 허브](https://huggingface.co/models)에 공유할 수 있도록 API를 제공합니다. 또한, 모델 구조를 정의하는 각 파이썬 모듈은 완전히 독립적이여서 연구 실험을 위해 손쉽게 수정할 수 있습니다.
 
-🤗 Transformers는 가장 유명한 3개의 딥러닝 라이브러리를 지원합니다. 이들은 서로 완벽히 연동됩니다 — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/). 간단하게 이 라이브러리 중 하나로 모델을 학습하고, 또 다른 라이브러리로 추론을 위해 모델을 불러올 수 있습니다.
+🤗 Transformers는 가장 유명한 3개의 딥러닝 라이브러리를 지원합니다. 이들은 서로 완벽히 연동됩니다 — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/). 간단하게 이 라이브러리 중 하나로 모델을 학습하고, 또 다른 라이브러리로 추론을 위해 모델을 불러올 수 있습니다. 
 
 ## 온라인 데모
 
@@ -75,7 +74,7 @@ limitations under the License.
 - [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazon%C3%ADa+or+usually+Amazonia%3B+French%3A+For%C3%AAt+amazonienne%3B+Dutch%3A+Amazoneregenwoud%29%2C+also+known+in+English+as+Amazonia+or+the+Amazon+Jungle%2C+is+a+moist+broadleaf+forest+that+covers+most+of+the+Amazon+basin+of+South+America.+This+basin+encompasses+7%2C000%2C000+square+kilometres+%282%2C700%2C000+sq+mi%29%2C+of+which+5%2C500%2C000+square+kilometres+%282%2C100%2C000+sq+mi%29+are+covered+by+the+rainforest.+This+region+includes+territory+belonging+to+nine+nations.+The+majority+of+the+forest+is+contained+within+Brazil%2C+with+60%25+of+the+rainforest%2C+followed+by+Peru+with+13%25%2C+Colombia+with+10%25%2C+and+with+minor+amounts+in+Venezuela%2C+Ecuador%2C+Bolivia%2C+Guyana%2C+Suriname+and+French+Guiana.+States+or+departments+in+four+nations+contain+%22Amazonas%22+in+their+names.+The+Amazon+represents+over+half+of+the+planet%27s+remaining+rainforests%2C+and+comprises+the+largest+and+most+biodiverse+tract+of+tropical+rainforest+in+the+world%2C+with+an+estimated+390+billion+individual+trees+divided+into+16%2C000+species)
 - [T5로 번역하기](https://huggingface.co/t5-base?text=My+name+is+Wolfgang+and+I+live+in+Berlin)
 
-**[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다.
+**[Transformer와 글쓰기](https://transformer.huggingface.co)** 는 이 저장소의 텍스트 생성 능력에 관한 Hugging Face 팀의 공식 데모입니다. 
 
 ## Hugging Face 팀의 커스텀 지원을 원한다면
 
@@ -229,7 +228,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
 1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
 1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
-1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
 1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
 1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
@@ -239,7 +237,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
@@ -251,7 +248,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
-1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
 1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
@@ -259,8 +255,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
-1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
-1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
+1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach 
 1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
@@ -273,7 +268,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
-1. **[LiLT](https://huggingface.co/docs/transformers/main/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
@@ -281,7 +275,6 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
 1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
 1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
 1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
 1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
 1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
@@ -299,7 +292,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (from Meta AI) released with the paper [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) by Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al.
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (from Google AI) released with the paper [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) by Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby.
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (from Google) released with the paper [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
-1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, Peter J. Liu.
+1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (from Google) released with the paper [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) by Jason Phang, Yao Zhao, Peter J. Liu. 
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (from Deepmind) released with the paper [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (from VinAI Research) released with the paper [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) by Dat Quoc Nguyen and Anh Tuan Nguyen.
 1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (from UCLA NLP) released with the paper [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
@@ -309,9 +302,9 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
 1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
+1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. 
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
 1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
 1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
@@ -327,11 +320,10 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)** (from HuggingFace).
-1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
+1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine 
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
-1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
+1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler 
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
 1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
 1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
@@ -340,14 +332,12 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
 1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
 1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
@@ -357,7 +347,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[XLSR-Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/xlsr_wav2vec2)** (from Facebook AI) released with the paper [Unsupervised Cross-Lingual Representation Learning For Speech Recognition](https://arxiv.org/abs/2006.13979) by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
 1. **[YOLOS](https://huggingface.co/docs/transformers/model_doc/yolos)** (from Huazhong University of Science & Technology) released with the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Yuxin Fang, Bencheng Liao, Xinggang Wang, Jiemin Fang, Jiyang Qi, Rui Wu, Jianwei Niu, Wenyu Liu.
 1. **[YOSO](https://huggingface.co/docs/transformers/model_doc/yoso)** (from the University of Wisconsin - Madison) released with the paper [You Only Sample (Almost) by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
-1. 새로운 모델을 올리고 싶나요? 우리가 **상세한 가이드와 템플릿** 으로 새로운 모델을 올리도록 도와드릴게요. 가이드와 템플릿은 이 저장소의 [`templates`](./templates) 폴더에서 확인하실 수 있습니다. [컨트리뷰션 가이드라인](./CONTRIBUTING.md)을 꼭 확인해주시고, PR을 올리기 전에 메인테이너에게 연락하거나 이슈를 오픈해 피드백을 받으시길 바랍니다.
+1. 새로운 모델을 올리고 싶나요? 우리가 **상세한 가이드와 템플릿** 으로 새로운 모델을 올리도록 도와드릴게요. 가이드와 템플릿은 이 저장소의 [`templates`](./templates) 폴더에서 확인하실 수 있습니다. [컨트리뷰션 가이드라인](./CONTRIBUTING.md)을 꼭 확인해주시고, PR을 올리기 전에 메인테이너에게 연락하거나 이슈를 오픈해 피드백을 받으시길 바랍니다. 
 
 각 모델이 Flax, PyTorch, TensorFlow으로 구현되었는지 또는 🤗 Tokenizers 라이브러리가 지원하는 토크나이저를 사용하는지 확인하려면, [이 표](https://huggingface.co/docs/transformers/index#supported-frameworks)를 확인하세요.
 

README_zh-hans.md

@@ -68,8 +68,7 @@ checkpoint: 检查点
         <a href="https://github.com/huggingface/transformers/">English</a> |
         <b>简体中文</b> |
         <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hant.md">繁體中文</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> 
+        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a>
     <p>
 </h4>
 
@@ -246,14 +245,13 @@ conda install -c huggingface transformers
 1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (来自 Google Research) 伴随论文 [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) 由 Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed 发布。
 1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 发布。
 1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (来自 Facebook) 伴随论文 [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) 由 Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston 发布。
-1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigSicence Workshop](https://bigscience.huggingface.co/).
+1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigSicence Workshop](https://bigscience.huggingface.co/). 
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (来自 Alexa) 伴随论文 [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) 由 Adrian de Wynter and Daniel J. Perry 发布。
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (来自 Google Research) 伴随论文 [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) 由 Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel 发布。
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (来自 Inria/Facebook/Sorbonne) 伴随论文 [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) 由 Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot 发布。
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (来自 Google Research) 伴随论文 [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) 由 Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting 发布。
 1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (来自 OpenAI) 伴随论文 [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) 由 Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever 发布。
 1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (来自 Salesforce) 伴随论文 [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) 由 Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong 发布。
-1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (来自 Microsoft Research Asia) 伴随论文 [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) 由 Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang 发布。
 1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (来自 YituTech) 伴随论文 [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) 由 Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan 发布。
 1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (来自 Facebook AI) 伴随论文 [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) 由 Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie 发布。
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (来自 Tsinghua University) 伴随论文 [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) 由 Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun 发布。
@@ -263,7 +261,6 @@ conda install -c huggingface transformers
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
 1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (来自 Microsoft) 伴随论文 [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) 由 Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen 发布。
 1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (来自 Berkeley/Facebook/Google) 伴随论文 [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) 由 Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch 发布。
-1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (来自 SenseTime Research) 伴随论文 [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) 由 Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai 发布。
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (来自 Facebook) 伴随论文 [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) 由 Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou 发布。
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (来自 Facebook) 伴随论文 [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) 由 Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko 发布。
 1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (来自 Microsoft Research) 伴随论文 [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) 由 Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan 发布。
@@ -275,7 +272,6 @@ conda install -c huggingface transformers
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (来自 Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning 发布。
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (来自 Google Research) 伴随论文 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 由 Sascha Rothe, Shashi Narayan, Aliaksei Severyn 发布。
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (来自 Baidu) 伴随论文 [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu 发布。
-1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (来自 CNRS) 伴随论文 [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) 由 Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab 发布。
 1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (来自 Facebook AI) 伴随论文 [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) 由 Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela 发布。
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (来自 Google Research) 伴随论文 [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) 由 James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon 发布。
@@ -283,8 +279,7 @@ conda install -c huggingface transformers
 1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (来自 KAIST) 伴随论文 [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) 由 Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim 发布。
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (来自 OpenAI) 伴随论文 [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) 由 Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever 发布。
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (来自 EleutherAI) 随仓库 [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) 发布。作者为 Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy 发布。
-1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
-1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (来自 ABEJA) 由 Shinya Otani, Takayoshi Makabe, Anuj Arora, Kyo Hattori。
+1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach 
 1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (来自 OpenAI) 伴随论文 [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) 由 Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever** 发布。
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (来自 EleutherAI) 伴随论文 [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) 由 Ben Wang and Aran Komatsuzaki 发布。
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (来自 UCSD, NVIDIA) 伴随论文 [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) 由 Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang 发布。
@@ -297,7 +292,6 @@ conda install -c huggingface transformers
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (来自 Microsoft Research Asia) 伴随论文 [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) 由 Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei 发布。
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (来自 Meta AI) 伴随论文 [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) 由 Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze 发布。
-1. **[LiLT](https://huggingface.co/docs/transformers/main/model_doc/lilt)** (来自 South China University of Technology) 伴随论文 [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) 由 Jiapeng Wang, Lianwen Jin, Kai Ding 发布。
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (来自 AllenAI) 伴随论文 [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) 由 Iz Beltagy, Matthew E. Peters, Arman Cohan 发布。
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (来自 Google AI) released 伴随论文 [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) 由 Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang 发布。
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (来自 Studio Ousia) 伴随论文 [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) 由 Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto 发布。
@@ -305,8 +299,7 @@ conda install -c huggingface transformers
 1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (来自 Facebook) 伴随论文 [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) 由 Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert 发布。
 1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (来自 Facebook) 伴随论文 [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) 由 Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin 发布。
 1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** 用 [OPUS](http://opus.nlpl.eu/) 数据训练的机器翻译模型由 Jörg Tiedemann 发布。[Marian Framework](https://marian-nmt.github.io/) 由微软翻译团队开发。
-1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (来自 Microsoft Research Asia) 伴随论文 [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) 由 Junlong Li, Yiheng Xu, Lei Cui, Furu Wei 发布。
-1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov  >>>>>>> Fix rebase
+1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov 
 1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (来自 Facebook) 伴随论文 [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) 由 Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer 发布。
 1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (来自 Facebook) 伴随论文 [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) 由 Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan 发布。
 1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (来自 NVIDIA) 伴随论文 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 由 Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 发布。
@@ -316,14 +309,14 @@ conda install -c huggingface transformers
 1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (来自 Apple) 伴随论文 [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) 由 Sachin Mehta and Mohammad Rastegari 发布。
 1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (来自 Microsoft Research) 伴随论文 [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) 由 Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu 发布。
 1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (来自 Google AI) 伴随论文 [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) 由 Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel 发布。
-1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (来自 中国人民大学 AI Box) 伴随论文 [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) 由 Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen 发布。
+1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (来自 中国人民大学 AI Box) 伴随论文 [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) 由 Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen 发布。 
 1. **[Nezha](https://huggingface.co/docs/transformers/model_doc/nezha)** (来自华为诺亚方舟实验室) 伴随论文 [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204) 由 Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen and Qun Liu 发布。
 1. **[NLLB](https://huggingface.co/docs/transformers/model_doc/nllb)** (来自 Meta) 伴随论文 [No Language Left Behind: Scaling Human-Centered Machine Translation](https://arxiv.org/abs/2207.04672) 由 the NLLB team 发布。
 1. **[Nyströmformer](https://huggingface.co/docs/transformers/model_doc/nystromformer)** (来自 the University of Wisconsin - Madison) 伴随论文 [Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention](https://arxiv.org/abs/2102.03902) 由 Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh 发布。
 1. **[OPT](https://huggingface.co/docs/transformers/master/model_doc/opt)** (来自 Meta AI) 伴随论文 [OPT: Open Pre-trained Transformer Language Models](https://arxiv.org/abs/2205.01068) 由 Susan Zhang, Stephen Roller, Naman Goyal, Mikel Artetxe, Moya Chen, Shuohui Chen et al 发布。
 1. **[OWL-ViT](https://huggingface.co/docs/transformers/model_doc/owlvit)** (来自 Google AI) 伴随论文 [Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230) 由 Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, and Neil Houlsby 发布。
 1. **[Pegasus](https://huggingface.co/docs/transformers/model_doc/pegasus)** (来自 Google) 伴随论文 [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/abs/1912.08777) 由 Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu 发布。
-1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (来自 Google) 伴随论文 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 由 Jason Phang, Yao Zhao, Peter J. Liu 发布。
+1. **[PEGASUS-X](https://huggingface.co/docs/transformers/model_doc/pegasus_x)** (来自 Google) 伴随论文 [Investigating Efficiently Extending Transformers for Long Input Summarization](https://arxiv.org/abs/2208.04347) 由 Jason Phang, Yao Zhao, Peter J. Liu 发布。  
 1. **[Perceiver IO](https://huggingface.co/docs/transformers/model_doc/perceiver)** (来自 Deepmind) 伴随论文 [Perceiver IO: A General Architecture for Structured Inputs & Outputs](https://arxiv.org/abs/2107.14795) 由 Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira 发布。
 1. **[PhoBERT](https://huggingface.co/docs/transformers/model_doc/phobert)** (来自 VinAI Research) 伴随论文 [PhoBERT: Pre-trained language models for Vietnamese](https://www.aclweb.org/anthology/2020.findings-emnlp.92/) 由 Dat Quoc Nguyen and Anh Tuan Nguyen 发布。
 1. **[PLBart](https://huggingface.co/docs/transformers/model_doc/plbart)** (来自 UCLA NLP) 伴随论文 [Unified Pre-training for Program Understanding and Generation](https://arxiv.org/abs/2103.06333) 由 Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang 发布。
@@ -333,9 +326,9 @@ conda install -c huggingface transformers
 1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (来自 Facebook) 伴随论文 [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) 由 Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela 发布。
 1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (来自 Google Research) 伴随论文 [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) 由 Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang 发布。
 1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (来自 Google Research) 伴随论文 [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) 由 Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya 发布。
-1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
+1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. 
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (来自 Google Research) 伴随论文 [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) 由 Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder 发布。
-1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (来自 Facebook), 伴随论文 [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) 由 Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov 发布。
 1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (来自 ZhuiyiTechnology), 伴随论文 [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) 由 Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu 发布。
 1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (来自 NVIDIA) 伴随论文 [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) 由 Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo 发布。
@@ -351,11 +344,10 @@ conda install -c huggingface transformers
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (来自 Google AI) 伴随论文 [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) 由 Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu 发布。
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (来自 Google AI) 伴随论文 [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) 由 Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos 发布。
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (来自 Microsoft Research) 伴随论文 [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) 由 Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou 发布。
-1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)** (from HuggingFace).
-1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
+1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine 
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (来自 Google/CMU) 伴随论文 [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) 由 Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov 发布。
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (来自 Microsoft) 伴随论文 [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) 由 Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei 发布。
-1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
+1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler 
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (来自 Microsoft Research) 伴随论文 [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) 由 Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang 发布。
 1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (来自 Microsoft Research) 伴随论文 [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) 由 Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu 发布。
 1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (来自 Tsinghua University and Nankai University) 伴随论文 [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) 由 Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu 发布。
@@ -364,14 +356,12 @@ conda install -c huggingface transformers
 1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (来自 Google AI) 伴随论文 [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) 由 Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby 发布。
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (来自 UCLA NLP) 伴随论文 [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) 由 Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang 发布。
 1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (来自 Meta AI) 伴随论文 [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) 由 Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick 发布。
-1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (来自 Meta AI) 伴随论文 [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas 发布.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (来自 Facebook AI) 伴随论文 [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) 由 Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli 发布。
 1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (来自 Facebook AI) 伴随论文 [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) 由 Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino 发布。
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (来自 Facebook AI) 伴随论文 [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) 由 Qiantong Xu, Alexei Baevski, Michael Auli 发布。
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (来自 OpenAI) 伴随论文 [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) 由 Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever 发布。
 1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (来自 Microsoft Research) 伴随论文 [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) 由 Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling 发布。
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
 1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (来自 Facebook) 伴随论文 [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) 由 Guillaume Lample and Alexis Conneau 发布。
 1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
 1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (来自 Facebook AI), 伴随论文 [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 由 Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov 发布。

README_zh-hant.md

@@ -80,8 +80,7 @@ user: 使用者
         <a href="https://github.com/huggingface/transformers/">English</a> |
         <a href="https://github.com/huggingface/transformers/blob/main/README_zh-hans.md">简体中文</a> |
         <b>繁體中文</b> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a> |
-        <a href="https://github.com/huggingface/transformers/blob/main/README_es.md">Español</a> 
+        <a href="https://github.com/huggingface/transformers/blob/main/README_ko.md">한국어</a>
     <p>
 </h4>
 
@@ -258,14 +257,13 @@ conda install -c huggingface transformers
 1. **[BigBird-RoBERTa](https://huggingface.co/docs/transformers/model_doc/big_bird)** (from Google Research) released with the paper [Big Bird: Transformers for Longer Sequences](https://arxiv.org/abs/2007.14062) by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
 1. **[Blenderbot](https://huggingface.co/docs/transformers/model_doc/blenderbot)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
 1. **[BlenderbotSmall](https://huggingface.co/docs/transformers/model_doc/blenderbot-small)** (from Facebook) released with the paper [Recipes for building an open-domain chatbot](https://arxiv.org/abs/2004.13637) by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
-1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigSicence Workshop](https://bigscience.huggingface.co/).
+1. **[BLOOM](https://huggingface.co/docs/transformers/model_doc/bloom)** (from BigScience workshop) released by the [BigSicence Workshop](https://bigscience.huggingface.co/). 
 1. **[BORT](https://huggingface.co/docs/transformers/model_doc/bort)** (from Alexa) released with the paper [Optimal Subarchitecture Extraction For BERT](https://arxiv.org/abs/2010.10499) by Adrian de Wynter and Daniel J. Perry.
 1. **[ByT5](https://huggingface.co/docs/transformers/model_doc/byt5)** (from Google Research) released with the paper [ByT5: Towards a token-free future with pre-trained byte-to-byte models](https://arxiv.org/abs/2105.13626) by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
 1. **[CamemBERT](https://huggingface.co/docs/transformers/model_doc/camembert)** (from Inria/Facebook/Sorbonne) released with the paper [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894) by Louis Martin*, Benjamin Muller*, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
 1. **[CANINE](https://huggingface.co/docs/transformers/model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
 1. **[CLIP](https://huggingface.co/docs/transformers/model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
 1. **[CodeGen](https://huggingface.co/docs/transformers/model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
-1. **[Conditional DETR](https://huggingface.co/docs/transformers/model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
 1. **[ConvBERT](https://huggingface.co/docs/transformers/model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
 1. **[ConvNeXT](https://huggingface.co/docs/transformers/model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
 1. **[CPM](https://huggingface.co/docs/transformers/model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
@@ -275,7 +273,6 @@ conda install -c huggingface transformers
 1. **[DeBERTa](https://huggingface.co/docs/transformers/model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeBERTa-v2](https://huggingface.co/docs/transformers/model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[Decision Transformer](https://huggingface.co/docs/transformers/model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[Deformable DETR](https://huggingface.co/docs/transformers/model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
 1. **[DeiT](https://huggingface.co/docs/transformers/model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](https://huggingface.co/docs/transformers/model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](https://huggingface.co/docs/transformers/model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
@@ -287,7 +284,6 @@ conda install -c huggingface transformers
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
-1. **[ESM](https://huggingface.co/docs/transformers/model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **[FlauBERT](https://huggingface.co/docs/transformers/model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
 1. **[FLAVA](https://huggingface.co/docs/transformers/model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 1. **[FNet](https://huggingface.co/docs/transformers/model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
@@ -295,8 +291,7 @@ conda install -c huggingface transformers
 1. **[GLPN](https://huggingface.co/docs/transformers/model_doc/glpn)** (from KAIST) released with the paper [Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth](https://arxiv.org/abs/2201.07436) by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
 1. **[GPT](https://huggingface.co/docs/transformers/model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT Neo](https://huggingface.co/docs/transformers/model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
-1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
-1. **[GPT NeoX Japanese](https://huggingface.co/docs/transformers/model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
+1. **[GPT NeoX](https://huggingface.co/docs/transformers/model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach 
 1. **[GPT-2](https://huggingface.co/docs/transformers/model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
 1. **[GPT-J](https://huggingface.co/docs/transformers/model_doc/gptj)** (from EleutherAI) released with the paper [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GroupViT](https://huggingface.co/docs/transformers/model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
@@ -309,7 +304,6 @@ conda install -c huggingface transformers
 1. **[LayoutXLM](https://huggingface.co/docs/transformers/model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](https://huggingface.co/docs/transformers/model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](https://huggingface.co/docs/transformers/model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
-1. **[LiLT](https://huggingface.co/docs/transformers/main/model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[Longformer](https://huggingface.co/docs/transformers/model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](https://huggingface.co/docs/transformers/model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](https://huggingface.co/docs/transformers/model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
@@ -317,8 +311,7 @@ conda install -c huggingface transformers
 1. **[M-CTC-T](https://huggingface.co/docs/transformers/model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
 1. **[M2M100](https://huggingface.co/docs/transformers/model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
 1. **[MarianMT](https://huggingface.co/docs/transformers/model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MarkupLM](https://huggingface.co/docs/transformers/model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
-1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov
+1. **[MaskFormer](https://huggingface.co/docs/transformers/model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov 
 1. **[mBART](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
 1. **[mBART-50](https://huggingface.co/docs/transformers/model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
 1. **[Megatron-BERT](https://huggingface.co/docs/transformers/model_doc/megatron-bert)** (from NVIDIA) released with the paper [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
@@ -345,9 +338,9 @@ conda install -c huggingface transformers
 1. **[RAG](https://huggingface.co/docs/transformers/model_doc/rag)** (from Facebook) released with the paper [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401) by Patrick Lewis, Ethan Perez, Aleksandara Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, Sebastian Riedel, Douwe Kiela.
 1. **[REALM](https://huggingface.co/docs/transformers/model_doc/realm.html)** (from Google Research) released with the paper [REALM: Retrieval-Augmented Language Model Pre-Training](https://arxiv.org/abs/2002.08909) by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
 1. **[Reformer](https://huggingface.co/docs/transformers/model_doc/reformer)** (from Google Research) released with the paper [Reformer: The Efficient Transformer](https://arxiv.org/abs/2001.04451) by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
-1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
+1. **[RegNet](https://huggingface.co/docs/transformers/model_doc/regnet)** (from META Research) released with the paper [Designing Network Design Space](https://arxiv.org/abs/2003.13678) by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár. 
 1. **[RemBERT](https://huggingface.co/docs/transformers/model_doc/rembert)** (from Google Research) released with the paper [Rethinking embedding coupling in pre-trained language models](https://arxiv.org/pdf/2010.12821.pdf) by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
-1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
+1. **[ResNet](https://huggingface.co/docs/transformers/model_doc/resnet)** (from Microsoft Research) released with the paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun. 
 1. **[RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta)** (from Facebook), released together with the paper a [Robustly Optimized BERT Pretraining Approach](https://arxiv.org/abs/1907.11692) by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
 1. **[RoFormer](https://huggingface.co/docs/transformers/model_doc/roformer)** (from ZhuiyiTechnology), released together with the paper a [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/pdf/2104.09864v1.pdf) by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
 1. **[SegFormer](https://huggingface.co/docs/transformers/model_doc/segformer)** (from NVIDIA) released with the paper [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://arxiv.org/abs/2105.15203) by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
@@ -363,27 +356,24 @@ conda install -c huggingface transformers
 1. **[T5v1.1](https://huggingface.co/docs/transformers/model_doc/t5v1.1)** (from Google AI) released with the paper [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](https://huggingface.co/docs/transformers/model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](https://huggingface.co/docs/transformers/model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Time Series Transformer](https://huggingface.co/docs/transformers/model_doc/time_series_transformer)** (from HuggingFace).
-1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
+1. **[Trajectory Transformer](https://huggingface.co/docs/transformers/model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine 
 1. **[Transformer-XL](https://huggingface.co/docs/transformers/model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](https://huggingface.co/docs/transformers/model_doc/trocr)** (from Microsoft) released with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
-1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler
+1. **[UL2](https://huggingface.co/docs/transformers/model_doc/ul2)** (from Google Research) released with the paper [Unifying Language Learning Paradigms](https://arxiv.org/abs/2205.05131v1) by Yi Tay, Mostafa Dehghani, Vinh Q. Tran, Xavier Garcia, Dara Bahri, Tal Schuster, Huaixiu Steven Zheng, Neil Houlsby, Donald Metzler 
 1. **[UniSpeech](https://huggingface.co/docs/transformers/model_doc/unispeech)** (from Microsoft Research) released with the paper [UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data](https://arxiv.org/abs/2101.07597) by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
 1. **[UniSpeechSat](https://huggingface.co/docs/transformers/model_doc/unispeech-sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
 1. **[VAN](https://huggingface.co/docs/transformers/model_doc/van)** (from Tsinghua University and Nankai University) released with the paper [Visual Attention Network](https://arxiv.org/pdf/2202.09741.pdf) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
 1. **[VideoMAE](https://huggingface.co/docs/transformers/model_doc/videomae)** (from Multimedia Computing Group, Nanjing University) released with the paper [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://arxiv.org/abs/2203.12602) by Zhan Tong, Yibing Song, Jue Wang, Limin Wang.
 1. **[ViLT](https://huggingface.co/docs/transformers/model_doc/vilt)** (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Wonjae Kim, Bokyung Son, Ildoo Kim.
-1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
+1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby. 
 1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[ViTMAE](https://huggingface.co/docs/transformers/model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[ViTMSN](https://huggingface.co/docs/transformers/model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
 1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2-Conformer](https://huggingface.co/docs/transformers/model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
 1. **[Wav2Vec2Phoneme](https://huggingface.co/docs/transformers/model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](https://huggingface.co/docs/transformers/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Whisper](https://huggingface.co/docs/transformers/model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
 1. **[X-CLIP](https://huggingface.co/docs/transformers/model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
-1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
+1. **[XGLM](https://huggingface.co/docs/transformers/model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li. 
 1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
 1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlm-prophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
 1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlm-roberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.

conftest.py

@@ -32,6 +32,7 @@ warnings.simplefilter(action="ignore", category=FutureWarning)
 
 
 def pytest_configure(config):
+    config.addinivalue_line("markers", "is_pipeline_test: mark test to run only when pipeline are tested")
     config.addinivalue_line(
         "markers", "is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested"
     )

docker/transformers-all-latest-gpu/Dockerfile

@@ -33,7 +33,6 @@ RUN echo torch=$VERSION
 RUN [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/$CUDA || python3 -m pip install --no-cache-dir -U --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/$CUDA
 
 RUN python3 -m pip install --no-cache-dir -U tensorflow
-RUN python3 -m pip install --no-cache-dir -U tensorflow_probability
 RUN python3 -m pip uninstall -y flax jax
 
 # Use installed torch version for `torch-scatter` to avid to deal with PYTORCH='pre'.

docker/transformers-pytorch-deepspeed-nightly-gpu/Dockerfile

@@ -27,24 +27,6 @@ RUN python3 -m pip uninstall -y deepspeed
 # RUN git clone https://github.com/microsoft/DeepSpeed && cd DeepSpeed && rm -rf build && \
 #    DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 python3 -m pip install . --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check 2>&1
 
-# For `torchdynamo` tests
-# (see https://github.com/huggingface/transformers/pull/17765)
-RUN git clone https://github.com/pytorch/functorch
-RUN python3 -m pip install --no-cache-dir ./functorch[aot]
-RUN cd functorch && python3 setup.py develop
-
-RUN git clone https://github.com/pytorch/torchdynamo
-RUN python3 -m pip install -r ./torchdynamo/requirements.txt
-RUN cd torchdynamo && python3 setup.py develop
-
-# install TensorRT
-RUN python3 -m pip install --no-cache-dir -U nvidia-pyindex
-RUN python3 -m pip install --no-cache-dir -U nvidia-tensorrt==8.2.4.2
-
-# install torch_tensorrt (fx path)
-RUN git clone https://github.com/pytorch/TensorRT.git
-RUN cd TensorRT/py && python3 setup.py install --fx-only
-
 # When installing in editable mode, `transformers` is not recognized as a package.
 # this line must be added in order for python to be aware of transformers.
 RUN cd transformers && python3 setup.py develop

docs/README.md

@@ -16,7 +16,7 @@ limitations under the License.
 
 # Generating the documentation
 
-To generate the documentation, you first have to build it. Several packages are necessary to build the doc, 
+To generate the documentation, you first have to build it. Several packages are necessary to build the doc,
 you can install them with the following command, at the root of the code repository:
 
 ```bash
@@ -33,7 +33,7 @@ pip install git+https://github.com/huggingface/doc-builder
 **NOTE**
 
 You only need to generate the documentation to inspect it locally (if you're planning changes and want to
-check how they look before committing for instance). You don't have to commit the built documentation.
+check how they look like before committing for instance). You don't have to commit the built documentation.
 
 ---
 
@@ -88,7 +88,7 @@ the filename without the extension in the [`_toctree.yml`](https://github.com/hu
 
 ## Renaming section headers and moving sections
 
-It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
+It helps to keep the old links working when renaming section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums and Social media and it'd be make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
 
 Therefore we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
 
@@ -99,7 +99,7 @@ Sections that were moved:
 
 [ <a href="#section-b">Section A</a><a id="section-a"></a> ]
 ```
-and of course, if you moved it to another file, then:
+and of course if you moved it to another file, then:
 
 ```
 Sections that were moved:
@@ -109,7 +109,7 @@ Sections that were moved:
 
 Use the relative style to link to the new file so that the versioned docs continue to work.
 
-For an example of a rich moved section set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/en/main_classes/trainer.mdx).
+For an example of a rich moved sections set please see the very end of [the Trainer doc](https://github.com/huggingface/transformers/blob/main/docs/source/main_classes/trainer.mdx).
 
 
 ## Writing Documentation - Specification
@@ -126,7 +126,7 @@ Adding a new tutorial or section is done in two steps:
 - Link that file in `./source/_toctree.yml` on the correct toc-tree.
 
 Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
-depending on the intended targets (beginners, more advanced users, or researchers) it should go in sections two, three, or
+depending on the intended targets (beginners, more advanced users or researchers) it should go in section two, three or
 four.
 
 ### Translating
@@ -177,8 +177,8 @@ not to be displayed in the documentation, you can do so by specifying which meth
     - save_vocabulary
 ```
 
-If you just want to add a method that is not documented (for instance magic methods like `__call__` are not documented
-by default) you can put the list of methods to add in a list that contains `all`:
+If you just want to add a method that is not documented (for instance magic method like `__call__` are not documented
+byt default) you can put the list of methods to add in a list that contains `all`:
 
 ```
 ## XXXTokenizer
@@ -191,9 +191,9 @@ by default) you can put the list of methods to add in a list that contains `all`
 ### Writing source documentation
 
 Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
-and objects like True, None, or any strings should usually be put in `code`.
+and objects like True, None or any strings should usually be put in `code`.
 
-When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
+When mentioning a class, function or method, it is recommended to use our syntax for internal links so that our tool
 adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or 
 function to be in the main package.
 
@@ -207,7 +207,7 @@ The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\
 #### Defining arguments in a method
 
 Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
-an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
+an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon and its
 description:
 
 ```
@@ -216,7 +216,7 @@ description:
 ```
 
 If the description is too long to fit in one line, another indentation is necessary before writing the description
-after the argument.
+after th argument.
 
 Here's an example showcasing everything so far:
 
@@ -266,7 +266,7 @@ Multi-line code blocks can be useful for displaying examples. They are done betw
 ````
 
 We follow the [doctest](https://docs.python.org/3/library/doctest.html) syntax for the examples to automatically test
-the results to stay consistent with the library.
+the results stay consistent with the library.
 
 #### Writing a return block
 
@@ -274,27 +274,27 @@ The return block should be introduced with the `Returns:` prefix, followed by a
 The first line should be the type of the return, followed by a line return. No need to indent further for the elements
 building the return.
 
-Here's an example of a single value return:
+Here's an example for a single value return:
 
 ```
     Returns:
         `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
 ```
 
-Here's an example of a tuple return, comprising several objects:
+Here's an example for tuple return, comprising several objects:
 
 ```
     Returns:
         `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
         - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
-          Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
+          Total loss as the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
         - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
           Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
 ```
 
 #### Adding an image
 
-Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos, and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
+Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
 the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference
 them by URL. We recommend putting them in the following dataset: [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
 If an external contribution, feel free to add the images to your PR and ask a Hugging Face member to migrate your images
@@ -312,13 +312,13 @@ easily.
 
 # Testing documentation examples
 
-Good documentation often comes with an example of how a specific function or class should be used. 
+Good documentation oftens comes with an example of how a specific function or class should be used. 
 Each model class should contain at least one example showcasing
 how to use this model class in inference. *E.g.* the class [Wav2Vec2ForCTC](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC) 
 includes an example of how to transcribe speech to text in the 
 [docstring of its forward function](https://huggingface.co/docs/transformers/model_doc/wav2vec2#transformers.Wav2Vec2ForCTC.forward).
 
-## Writing documentation examples
+## Writing documenation examples
 
 The syntax for Example docstrings can look as follows:
 
@@ -364,7 +364,7 @@ We use pytests' [doctest integration](https://docs.pytest.org/doctest.html) to v
 For Transformers, the doctests are run on a daily basis via GitHub Actions as can be 
 seen [here](https://github.com/huggingface/transformers/actions/workflows/doctests.yml).
 
-To include your example in the daily doctests, you need to add the filename that
+To include your example in the daily doctests, you need add the filename that
 contains the example docstring to the [documentation_tests.txt](../utils/documentation_tests.txt).
 
 ### For Python files
@@ -426,6 +426,6 @@ Here are a few tips to help you debug the doctests and make them pass:
 
 - The outputs of the code need to match the expected output **exactly**, so make sure you have the same outputs. In particular doctest will see a difference between single quotes and double quotes, or a missing parenthesis. The only exceptions to that rule are:
   * whitespace: one give whitespace (space, tabulation, new line) is equivalent to any number of whitespace, so you can add new lines where there are spaces to make your output more readable.
-  * numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configured to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits).
+  * numerical values: you should never put more than 4 or 5 digits to expected results as different setups or library versions might get you slightly different results. `doctest` is configure to ignore any difference lower than the precision to which you wrote (so 1e-4 if you write 4 digits).
 - Don't leave a block of code that is very long to execute. If you can't make it fast, you can either not use the doctest syntax on it (so that it's ignored), or if you want to use the doctest syntax to show the results, you can add a comment `# doctest: +SKIP` at the end of the lines of code too long to execute
 - Each line of code that produces a result needs to have that result written below. You can ignore an output if you don't want to show it in your code example by adding a comment ` # doctest: +IGNORE_RESULT` at the end of the line of code producing it.

docs/source/de/_toctree.yml

@@ -9,14 +9,4 @@
 - sections:
   - local: pipeline_tutorial
     title: Pipelines für Inferenzen
-  - local: autoclass_tutorial
-    title: Laden von vortrainierten Instanzen mit einer AutoClass
-  - local: preprocessing
-    title: Vorverarbeiten
-  - local: training
-    title: Optimierung eines vortrainierten Modells
-  - local: accelerate
-    title: Verteiltes Training mit 🤗 Accelerate
-  - local: model_sharing
-    title: Ein Modell teilen
   title: Tutorials

docs/source/de/accelerate.mdx

@@ -1,132 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Verteiltes Training mit 🤗 Accelerate
-
-Da die Modelle immer größer werden, hat sich die Parallelität als Strategie zum Trainieren größerer Modelle auf begrenzter Hardware und zur Beschleunigung der Trainingsgeschwindigkeit um mehrere Größenordnungen erwiesen. Bei Hugging Face haben wir die Bibliothek [🤗 Accelerate](https://huggingface.co/docs/accelerate) entwickelt, um Nutzern zu helfen, ein 🤗 Transformers-Modell auf jeder Art von verteiltem Setup zu trainieren, egal ob es sich um mehrere GPUs auf einer Maschine oder mehrere GPUs auf mehreren Maschinen handelt. In diesem Tutorial lernen Sie, wie Sie Ihre native PyTorch-Trainingsschleife anpassen, um das Training in einer verteilten Umgebung zu ermöglichen.
-
-## Einrichtung
-
-Beginnen Sie mit der Installation von 🤗 Accelerate:
-
-```bash
-pip install accelerate
-```
-
-Dann importieren und erstellen Sie ein [`~accelerate.Accelerator`]-Objekt. Der [`~accelerate.Accelerator`] wird automatisch Ihre Art der verteilten Einrichtung erkennen und alle notwendigen Komponenten für das Training initialisieren. Sie müssen Ihr Modell nicht explizit auf einem Gerät platzieren.
-
-```py
->>> from accelerate import Accelerator
-
->>> accelerator = Accelerator()
-```
-
-## Vorbereiten auf die Beschleunigung
-
-Der nächste Schritt ist die Übergabe aller relevanten Trainingsobjekte an die Methode [`~accelerate.Accelerator.prepare`]. Dazu gehören Ihre Trainings- und Evaluierungs-DataLoader, ein Modell und ein Optimierer:
-
-```py
->>> train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
-...     train_dataloader, eval_dataloader, model, optimizer
-... )
-```
-
-## Rückwärts
-
-Die letzte Ergänzung besteht darin, das typische `loss.backward()` in der Trainingsschleife durch die 🤗 Accelerate-Methode [`~accelerate.Accelerator.backward`] zu ersetzen:
-
-```py
->>> for epoch in range(num_epochs):
-...     for batch in train_dataloader:
-...         outputs = model(**batch)
-...         loss = outputs.loss
-...         accelerator.backward(loss)
-
-...         optimizer.step()
-...         lr_scheduler.step()
-...         optimizer.zero_grad()
-...         progress_bar.update(1)
-```
-
-Wie Sie im folgenden Code sehen können, müssen Sie nur vier zusätzliche Codezeilen zu Ihrer Trainingsschleife hinzufügen, um verteiltes Training zu ermöglichen!
-
-```diff
-+ from accelerate import Accelerator
-  from transformers import AdamW, AutoModelForSequenceClassification, get_scheduler
-
-+ accelerator = Accelerator()
-
-  model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=2)
-  optimizer = AdamW(model.parameters(), lr=3e-5)
-
-- device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
-- model.to(device)
-
-+ train_dataloader, eval_dataloader, model, optimizer = accelerator.prepare(
-+     train_dataloader, eval_dataloader, model, optimizer
-+ )
-
-  num_epochs = 3
-  num_training_steps = num_epochs * len(train_dataloader)
-  lr_scheduler = get_scheduler(
-      "linear",
-      optimizer=optimizer,
-      num_warmup_steps=0,
-      num_training_steps=num_training_steps
-  )
-
-  progress_bar = tqdm(range(num_training_steps))
-
-  model.train()
-  for epoch in range(num_epochs):
-      for batch in train_dataloader:
--         batch = {k: v.to(device) for k, v in batch.items()}
-          outputs = model(**batch)
-          loss = outputs.loss
--         loss.backward()
-+         accelerator.backward(loss)
-
-          optimizer.step()
-          lr_scheduler.step()
-          optimizer.zero_grad()
-          progress_bar.update(1)
-```
-
-## Trainieren
-
-Sobald Sie die entsprechenden Codezeilen hinzugefügt haben, starten Sie Ihr Training in einem Skript oder einem Notebook wie Colaboratory.
-
-### Trainieren mit einem Skript
-
-Wenn Sie Ihr Training mit einem Skript durchführen, führen Sie den folgenden Befehl aus, um eine Konfigurationsdatei zu erstellen und zu speichern:
-
-```bash
-accelerate config
-```
-
-Dann starten Sie Ihr Training mit:
-
-```bash
-accelerate launch train.py
-```
-
-### Trainieren mit einem Notebook
-
-🤗 Accelerate kann auch in einem Notebook laufen, wenn Sie planen, die TPUs von Colaboratory zu verwenden. Verpacken Sie den gesamten Code, der für das Training verantwortlich ist, in eine Funktion und übergeben Sie diese an [`~accelerate.notebook_launcher`]:
-
-```py
->>> from accelerate import notebook_launcher
-
->>> notebook_launcher(training_function)
-```
-
-Weitere Informationen über 🤗 Accelerate und seine umfangreichen Funktionen finden Sie in der [Dokumentation](https://huggingface.co/docs/accelerate).

docs/source/de/autoclass_tutorial.mdx

@@ -1,127 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Vortrainierte Instanzen mit einer AutoClass laden
-
-Bei so vielen verschiedenen Transformator-Architekturen kann es eine Herausforderung sein, eine für Ihren Checkpoint zu erstellen. Als Teil der 🤗 Transformers Kernphilosophie, die Bibliothek leicht, einfach und flexibel nutzbar zu machen, leitet eine `AutoClass` automatisch die richtige Architektur aus einem gegebenen Checkpoint ab und lädt sie. Mit der Methode `from_pretrained()` kann man schnell ein vortrainiertes Modell für eine beliebige Architektur laden, so dass man keine Zeit und Ressourcen aufwenden muss, um ein Modell von Grund auf zu trainieren. Die Erstellung dieser Art von Checkpoint-agnostischem Code bedeutet, dass Ihr Code, wenn er für einen Checkpoint funktioniert, auch mit einem anderen Checkpoint funktionieren wird - solange er für eine ähnliche Aufgabe trainiert wurde - selbst wenn die Architektur unterschiedlich ist.
-
-<Tip>
-
-Denken Sie daran, dass sich die Architektur auf das Skelett des Modells bezieht und die Checkpoints die Gewichte für eine bestimmte Architektur sind. Zum Beispiel ist [BERT](https://huggingface.co/bert-base-uncased) eine Architektur, während `bert-base-uncased` ein Checkpoint ist. Modell ist ein allgemeiner Begriff, der entweder Architektur oder Prüfpunkt bedeuten kann.
-
-</Tip>
-
-In dieser Anleitung lernen Sie, wie man:
-
-* Einen vortrainierten Tokenizer lädt.
-* Einen vortrainierten Merkmalsextraktor lädt.
-* Einen vortrainierten Prozessor lädt.
-* Ein vortrainiertes Modell lädt.
-
-## AutoTokenizer
-
-Nahezu jede NLP-Aufgabe beginnt mit einem Tokenizer. Ein Tokenizer wandelt Ihre Eingabe in ein Format um, das vom Modell verarbeitet werden kann.
-
-Laden Sie einen Tokenizer mit [`AutoTokenizer.from_pretrained`]:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")
-```
-
-Dann tokenisieren Sie Ihre Eingabe wie unten gezeigt:
-
-```py
->>> sequence = "In a hole in the ground there lived a hobbit."
->>> print(tokenizer(sequence))
-{'input_ids': [101, 1999, 1037, 4920, 1999, 1996, 2598, 2045, 2973, 1037, 7570, 10322, 4183, 1012, 102], 
- 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
- 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-```
-
-## AutoFeatureExtractor
-
-Für Audio- und Bildverarbeitungsaufgaben verarbeitet ein Merkmalsextraktor das Audiosignal oder Bild in das richtige Eingabeformat.
-
-Laden Sie einen Merkmalsextraktor mit [`AutoFeatureExtractor.from_pretrained`]:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained(
-...     "ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition"
-... )
-```
-
-## AutoProcessor
-
-Multimodale Aufgaben erfordern einen Prozessor, der zwei Arten von Vorverarbeitungswerkzeugen kombiniert. Das Modell [LayoutLMV2](model_doc/layoutlmv2) beispielsweise benötigt einen Feature-Extraktor für Bilder und einen Tokenizer für Text; ein Prozessor kombiniert beide.
-
-Laden Sie einen Prozessor mit [`AutoProcessor.from_pretrained`]:
-
-```py
->>> from transformers import AutoProcessor
-
->>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased")
-```
-
-## AutoModel
-
-<frameworkcontent>
-<pt>
-Mit den `AutoModelFor`-Klassen können Sie schließlich ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe [hier](model_doc/auto) für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit [`AutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
-
-```py
->>> from transformers import AutoModelForTokenClassification
-
->>> model = AutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
-```
-
-<Tip warning={true}>
-
-Für PyTorch-Modelle verwendet die Methode `from_pretrained()` `torch.load()`, die intern `pickle` verwendet und als unsicher bekannt ist. Generell sollte man niemals ein Modell laden, das aus einer nicht vertrauenswürdigen Quelle stammen könnte, oder das manipuliert worden sein könnte. Dieses Sicherheitsrisiko wird für öffentliche Modelle, die auf dem Hugging Face Hub gehostet werden, teilweise gemildert, da diese bei jeder Übertragung [auf Malware](https://huggingface.co/docs/hub/security-malware) gescannt werden. Siehe die [Hub-Dokumentation](https://huggingface.co/docs/hub/security) für Best Practices wie [signierte Commit-Verifizierung](https://huggingface.co/docs/hub/security-gpg#signing-commits-with-gpg) mit GPG.
-
-TensorFlow- und Flax-Checkpoints sind nicht betroffen und können in PyTorch-Architekturen mit den Kwargs `from_tf` und `from_flax` für die Methode `from_pretrained` geladen werden, um dieses Problem zu umgehen.
-
-</Tip>
-
-Im Allgemeinen empfehlen wir die Verwendung der Klasse "AutoTokenizer" und der Klasse "AutoModelFor", um trainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
-</pt>
-<tf>
-Mit den Klassen `TFAutoModelFor` schließlich können Sie ein vortrainiertes Modell für eine bestimmte Aufgabe laden (siehe [hier](model_doc/auto) für eine vollständige Liste der verfügbaren Aufgaben). Laden Sie zum Beispiel ein Modell für die Sequenzklassifikation mit [`TFAutoModelForSequenceClassification.from_pretrained`]:
-
-```py
->>> from transformers import TFAutoModelForSequenceClassification
-
->>> model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Sie können denselben Prüfpunkt problemlos wiederverwenden, um eine Architektur für eine andere Aufgabe zu laden:
-
-```py
->>> from transformers import TFAutoModelForTokenClassification
-
->>> model = TFAutoModelForTokenClassification.from_pretrained("distilbert-base-uncased")
-```
-
-Im Allgemeinen empfehlen wir, die Klasse "AutoTokenizer" und die Klasse "TFAutoModelFor" zu verwenden, um vortrainierte Instanzen von Modellen zu laden. Dadurch wird sichergestellt, dass Sie jedes Mal die richtige Architektur laden. Im nächsten [Tutorial] (Vorverarbeitung) erfahren Sie, wie Sie Ihren neu geladenen Tokenizer, Feature Extractor und Prozessor verwenden, um einen Datensatz für die Feinabstimmung vorzuverarbeiten.
-</tf>
-</frameworkcontent>

docs/source/de/model_sharing.mdx

@@ -1,228 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Ein Modell teilen
-
-Die letzten beiden Tutorials haben gezeigt, wie man ein Modell mit PyTorch, Keras und 🤗 Accelerate für verteilte Setups feinabstimmen kann. Der nächste Schritt besteht darin, Ihr Modell mit der Community zu teilen! Bei Hugging Face glauben wir an den offenen Austausch von Wissen und Ressourcen, um künstliche Intelligenz für alle zu demokratisieren. Wir ermutigen Sie, Ihr Modell mit der Community zu teilen, um anderen zu helfen, Zeit und Ressourcen zu sparen.
-
-In diesem Tutorial lernen Sie zwei Methoden kennen, wie Sie ein trainiertes oder verfeinertes Modell auf dem [Model Hub](https://huggingface.co/models) teilen können:
-
-- Programmgesteuertes Übertragen Ihrer Dateien auf den Hub.
-- Ziehen Sie Ihre Dateien per Drag-and-Drop über die Weboberfläche in den Hub.
-
-<iframe width="560" height="315" src="https://www.youtube.com/embed/XvSGPZFEjDY" title="YouTube video player"
-frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
-picture-in-picture" allowfullscreen></iframe>
-
-<Tip>
-
-Um ein Modell mit der Öffentlichkeit zu teilen, benötigen Sie ein Konto auf [huggingface.co](https://huggingface.co/join). Sie können auch einer bestehenden Organisation beitreten oder eine neue Organisation gründen.
-
-</Tip>
-
-## Repository-Funktionen
-
-Jedes Repository im Model Hub verhält sich wie ein typisches GitHub-Repository. Unsere Repositorys bieten Versionierung, Commit-Historie und die Möglichkeit, Unterschiede zu visualisieren.
-
-Die integrierte Versionierung des Model Hub basiert auf Git und [git-lfs](https://git-lfs.github.com/). Mit anderen Worten: Sie können ein Modell als ein Repository behandeln, was eine bessere Zugriffskontrolle und Skalierbarkeit ermöglicht. Die Versionskontrolle ermöglicht *Revisionen*, eine Methode zum Anheften einer bestimmten Version eines Modells mit einem Commit-Hash, Tag oder Branch.
-
-Folglich können Sie eine bestimmte Modellversion mit dem Parameter "Revision" laden:
-
-```py
->>> model = AutoModel.from_pretrained(
-...     "julien-c/EsperBERTo-small", revision="v2.0.1"  # tag name, or branch name, or commit hash
-... )
-```
-
-Dateien lassen sich auch in einem Repository leicht bearbeiten, und Sie können die Commit-Historie sowie die Unterschiede einsehen:
-
-![vis_diff](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vis_diff.png)
-
-## Einrichtung
-
-Bevor Sie ein Modell für den Hub freigeben, benötigen Sie Ihre Hugging Face-Anmeldedaten. Wenn Sie Zugang zu einem Terminal haben, führen Sie den folgenden Befehl in der virtuellen Umgebung aus, in der 🤗 Transformers installiert ist. Dadurch werden Ihre Zugangsdaten in Ihrem Hugging Face-Cache-Ordner (standardmäßig `~/.cache/`) gespeichert:
-
-```bash
-huggingface-cli login
-```
-
-Wenn Sie ein Notebook wie Jupyter oder Colaboratory verwenden, stellen Sie sicher, dass Sie die [`huggingface_hub`](https://huggingface.co/docs/hub/adding-a-library) Bibliothek installiert haben. Diese Bibliothek ermöglicht Ihnen die programmatische Interaktion mit dem Hub.
-
-```bash
-pip install huggingface_hub
-```
-
-Verwenden Sie dann `notebook_login`, um sich beim Hub anzumelden, und folgen Sie dem Link [hier](https://huggingface.co/settings/token), um ein Token für die Anmeldung zu generieren:
-
-```py
->>> from huggingface_hub import notebook_login
-
->>> notebook_login()
-```
-
-## Ein Modell für alle Frameworks konvertieren
-
-Um sicherzustellen, dass Ihr Modell von jemandem verwendet werden kann, der mit einem anderen Framework arbeitet, empfehlen wir Ihnen, Ihr Modell sowohl mit PyTorch- als auch mit TensorFlow-Checkpoints zu konvertieren und hochzuladen. Während Benutzer immer noch in der Lage sind, Ihr Modell von einem anderen Framework zu laden, wenn Sie diesen Schritt überspringen, wird es langsamer sein, weil 🤗 Transformers den Checkpoint on-the-fly konvertieren müssen.
-
-Die Konvertierung eines Checkpoints für ein anderes Framework ist einfach. Stellen Sie sicher, dass Sie PyTorch und TensorFlow installiert haben (siehe [hier](installation) für Installationsanweisungen), und finden Sie dann das spezifische Modell für Ihre Aufgabe in dem anderen Framework. 
-
-<frameworkcontent>
-<pt>
-Geben Sie `from_tf=True` an, um einen Prüfpunkt von TensorFlow nach PyTorch zu konvertieren:
-
-```py
->>> pt_model = DistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_tf=True)
->>> pt_model.save_pretrained("path/to/awesome-name-you-picked")
-```
-</pt>
-<tf>
-Geben Sie `from_pt=True` an, um einen Prüfpunkt von PyTorch nach TensorFlow zu konvertieren:
-
-```py
->>> tf_model = TFDistilBertForSequenceClassification.from_pretrained("path/to/awesome-name-you-picked", from_pt=True)
-```
-
-Dann können Sie Ihr neues TensorFlow-Modell mit seinem neuen Checkpoint speichern:
-
-```py
->>> tf_model.save_pretrained("path/to/awesome-name-you-picked")
-```
-</tf>
-<jax>
-Wenn ein Modell in Flax verfügbar ist, können Sie auch einen Kontrollpunkt von PyTorch nach Flax konvertieren:
-
-```py
->>> flax_model = FlaxDistilBertForSequenceClassification.from_pretrained(
-...     "path/to/awesome-name-you-picked", from_pt=True
-... )
-```
-</jax>
-</frameworkcontent>
-
-## Ein Modell während des Trainings hochladen
-
-<frameworkcontent>
-<pt>
-<Youtube id="Z1-XMy-GNLQ"/>
-
-Die Weitergabe eines Modells an den Hub ist so einfach wie das Hinzufügen eines zusätzlichen Parameters oder Rückrufs. Erinnern Sie sich an das [Feinabstimmungs-Tutorial](training), in der Klasse [`TrainingArguments`] geben Sie Hyperparameter und zusätzliche Trainingsoptionen an. Eine dieser Trainingsoptionen beinhaltet die Möglichkeit, ein Modell direkt an den Hub zu pushen. Setzen Sie `push_to_hub=True` in Ihrer [`TrainingArguments`]:
-
-```py
->>> training_args = TrainingArguments(output_dir="my-awesome-model", push_to_hub=True)
-```
-
-Übergeben Sie Ihre Trainingsargumente wie gewohnt an [`Trainer`]:
-
-```py
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=small_train_dataset,
-...     eval_dataset=small_eval_dataset,
-...     compute_metrics=compute_metrics,
-... )
-```
-
-Nach der Feinabstimmung Ihres Modells rufen Sie [`~transformers.Trainer.push_to_hub`] auf [`Trainer`] auf, um das trainierte Modell an den Hub zu übertragen. Transformers fügt sogar automatisch Trainings-Hyperparameter, Trainingsergebnisse und Framework-Versionen zu Ihrer Modellkarte hinzu!
-
-```py
->>> trainer.push_to_hub()
-```
-</pt>
-<tf>
-Geben Sie ein Modell mit [`PushToHubCallback`] an den Hub weiter. In der [`PushToHubCallback`] Funktion, fügen Sie hinzu:
-
-- Ein Ausgabeverzeichnis für Ihr Modell.
-- Einen Tokenizer.
-- Die `hub_model_id`, die Ihr Hub-Benutzername und Modellname ist.
-
-```py
->>> from transformers.keras.callbacks import PushToHubCallback
-
->>> push_to_hub_callback = PushToHubCallback(
-...     output_dir="./your_model_save_path", tokenizer=tokenizer, hub_model_id="your-username/my-awesome-model"
-... )
-```
-
-Fügen Sie den Callback zu [`fit`](https://keras.io/api/models/model_training_apis/) hinzu, und 🤗 Transformers wird das trainierte Modell an den Hub weiterleiten:
-
-```py
->>> model.fit(tf_train_dataset, validation_data=tf_validation_dataset, epochs=3, callbacks=push_to_hub_callback)
-```
-</tf>
-</frameworkcontent>
-
-## Verwenden Sie die Funktion `push_to_hub`.
-
-Sie können `push_to_hub` auch direkt für Ihr Modell aufrufen, um es in den Hub hochzuladen.
-
-Geben Sie den Namen Ihres Modells in "push_to_hub" an:
-
-```py
->>> pt_model.push_to_hub("my-awesome-model")
-```
-
-Dadurch wird ein Repository unter Ihrem Benutzernamen mit dem Modellnamen `my-awesome-model` erstellt. Benutzer können nun Ihr Modell mit der Funktion `from_pretrained` laden:
-
-```py
->>> from transformers import AutoModel
-
->>> model = AutoModel.from_pretrained("your_username/my-awesome-model")
-```
-
-Wenn Sie zu einer Organisation gehören und Ihr Modell stattdessen unter dem Namen der Organisation pushen wollen, fügen Sie diesen einfach zur `repo_id` hinzu:
-
-```py
->>> pt_model.push_to_hub("my-awesome-org/my-awesome-model")
-```
-
-Die Funktion "push_to_hub" kann auch verwendet werden, um andere Dateien zu einem Modell-Repository hinzuzufügen. Zum Beispiel kann man einen Tokenizer zu einem Modell-Repository hinzufügen:
-
-```py
->>> tokenizer.push_to_hub("my-awesome-model")
-```
-
-Oder vielleicht möchten Sie die TensorFlow-Version Ihres fein abgestimmten PyTorch-Modells hinzufügen:
-
-```py
->>> tf_model.push_to_hub("my-awesome-model")
-```
-
-Wenn Sie nun zu Ihrem Hugging Face-Profil navigieren, sollten Sie Ihr neu erstelltes Modell-Repository sehen. Wenn Sie auf die Registerkarte **Dateien** klicken, werden alle Dateien angezeigt, die Sie in das Repository hochgeladen haben.
-
-Weitere Einzelheiten zum Erstellen und Hochladen von Dateien in ein Repository finden Sie in der Hub-Dokumentation [hier](https://huggingface.co/docs/hub/how-to-upstream).
-
-## Hochladen mit der Weboberfläche
-
-Benutzer, die einen no-code Ansatz bevorzugen, können ein Modell über das Webinterface des Hubs hochladen. Besuchen Sie [huggingface.co/new](https://huggingface.co/new) um ein neues Repository zu erstellen:
-
-![new_model_repo](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/new_model_repo.png)
-
-Fügen Sie von hier aus einige Informationen über Ihr Modell hinzu:
-
-- Wählen Sie den **Besitzer** des Repositorys. Dies können Sie selbst oder eine der Organisationen sein, denen Sie angehören.
-- Wählen Sie einen Namen für Ihr Modell, der auch der Name des Repositorys sein wird.
-- Wählen Sie, ob Ihr Modell öffentlich oder privat ist.
-- Geben Sie die Lizenzverwendung für Ihr Modell an.
-
-Klicken Sie nun auf die Registerkarte **Dateien** und klicken Sie auf die Schaltfläche **Datei hinzufügen**, um eine neue Datei in Ihr Repository hochzuladen. Ziehen Sie dann eine Datei per Drag-and-Drop hoch und fügen Sie eine Übergabemeldung hinzu.
-
-![upload_file](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/upload_file.png)
-
-## Hinzufügen einer Modellkarte
-
-Um sicherzustellen, dass die Benutzer die Fähigkeiten, Grenzen, möglichen Verzerrungen und ethischen Aspekte Ihres Modells verstehen, fügen Sie bitte eine Modellkarte zu Ihrem Repository hinzu. Die Modellkarte wird in der Datei `README.md` definiert. Sie können eine Modellkarte hinzufügen, indem Sie:
-
-* Manuelles Erstellen und Hochladen einer "README.md"-Datei.
-* Klicken Sie auf die Schaltfläche **Modellkarte bearbeiten** in Ihrem Modell-Repository.
-
-Werfen Sie einen Blick auf die DistilBert [model card](https://huggingface.co/distilbert-base-uncased) als gutes Beispiel für die Art von Informationen, die eine Modellkarte enthalten sollte. Weitere Details über andere Optionen, die Sie in der Datei "README.md" einstellen können, wie z.B. den Kohlenstoff-Fußabdruck eines Modells oder Beispiele für Widgets, finden Sie in der Dokumentation [hier](https://huggingface.co/docs/hub/models-cards).

docs/source/de/preprocessing.mdx

@@ -1,502 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Vorverarbeiten
-
-[[open-in-colab]]
-
-Bevor Sie Ihre Daten in einem Modell verwenden können, müssen die Daten in ein für das Modell akzeptables Format gebracht werden. Ein Modell versteht keine Rohtexte, Bilder oder Audiodaten. Diese Eingaben müssen in Zahlen umgewandelt und zu Tensoren zusammengesetzt werden. In dieser Anleitung werden Sie:
-
-* Textdaten mit einem Tokenizer vorverarbeiten.
-* Bild- oder Audiodaten mit einem Feature Extractor vorverarbeiten.
-* Daten für eine multimodale Aufgabe mit einem Prozessor vorverarbeiten.
-
-## NLP
-
-<Youtube id="Yffk5aydLzg"/>
-
-Das wichtigste Werkzeug zur Verarbeitung von Textdaten ist ein [Tokenizer](main_classes/tokenizer). Ein Tokenizer zerlegt Text zunächst nach einer Reihe von Regeln in *Token*. Die Token werden in Zahlen umgewandelt, die zum Aufbau von Tensoren als Eingabe für ein Modell verwendet werden. Alle zusätzlichen Eingaben, die ein Modell benötigt, werden ebenfalls vom Tokenizer hinzugefügt.
-
-<Tip>
-
-Wenn Sie ein vortrainiertes Modell verwenden möchten, ist es wichtig, den zugehörigen vortrainierten Tokenizer zu verwenden. Dadurch wird sichergestellt, dass der Text auf die gleiche Weise aufgeteilt wird wie das Pretraining-Korpus und die gleichen entsprechenden Token-zu-Index (in der Regel als *vocab* bezeichnet) während des Pretrainings verwendet werden.
-
-</Tip>
-
-Laden Sie einen vortrainierten Tokenizer mit der Klasse [AutoTokenizer], um schnell loszulegen. Damit wird das *vocab* heruntergeladen, das verwendet wird, wenn ein Modell vortrainiert wird.
-
-### Tokenize
-
-Laden Sie einen vortrainierten Tokenizer mit [`AutoTokenizer.from_pretrained`]:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-```
-
-Dann übergeben Sie Ihren Satz an den Tokenizer:
-
-```py
->>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
->>> print(encoded_input)
-{'input_ids': [101, 2079, 2025, 19960, 10362, 1999, 1996, 3821, 1997, 16657, 1010, 2005, 2027, 2024, 11259, 1998, 4248, 2000, 4963, 1012, 102], 
- 'token_type_ids': [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
- 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
-```
-
-Der Tokenizer gibt ein Wörterbuch mit drei wichtigen Elementen zurück:
-
-* [input_ids](glossary#input-ids) sind die Indizes, die den einzelnen Token im Satz entsprechen.
-* [attention_mask](glossary#attention-mask) gibt an, ob ein Token beachtet werden soll oder nicht.
-* [token_type_ids](glossary#token-type-ids) gibt an, zu welcher Sequenz ein Token gehört, wenn es mehr als eine Sequenz gibt.
-
-Sie können die `input_ids` dekodieren, um die ursprüngliche Eingabe zurückzugeben:
-
-```py
->>> tokenizer.decode(encoded_input["input_ids"])
-'[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
-```
-
-Wie Sie sehen können, hat der Tokenisierer zwei spezielle Token - `CLS` und `SEP` (Klassifikator und Separator) - zum Satz hinzugefügt. Nicht alle Modelle benötigen
-spezielle Token, aber wenn dies der Fall ist, fügt der Tokenisierer sie automatisch für Sie hinzu.
-
-Wenn Sie mehrere Sätze verarbeiten wollen, übergeben Sie die Sätze als Liste an den Tokenizer:
-
-```py
->>> batch_sentences = [
-...     "But what about second breakfast?",
-...     "Don't think he knows about second breakfast, Pip.",
-...     "What about elevensies?",
-... ]
->>> encoded_inputs = tokenizer(batch_sentences)
->>> print(encoded_inputs)
-{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102], 
-               [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], 
-               [101, 1327, 1164, 5450, 23434, 136, 102]], 
- 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0], 
-                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-                    [0, 0, 0, 0, 0, 0, 0]], 
- 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1], 
-                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                    [1, 1, 1, 1, 1, 1, 1]]}
-```
-
-### Pad
-
-Dies bringt uns zu einem wichtigen Thema. Wenn Sie einen Haufen von Sätzen verarbeiten, sind diese nicht immer gleich lang. Das ist ein Problem, weil Tensoren, die Eingabe für das Modell, eine einheitliche Form haben müssen. Padding ist eine Strategie, die sicherstellt, dass Tensoren rechteckig sind, indem ein spezielles *Padding-Token* zu Sätzen mit weniger Token hinzugefügt wird.
-
-Setzen Sie den Parameter "padding" auf "true", um die kürzeren Sequenzen im Stapel so aufzufüllen, dass sie der längsten Sequenz entsprechen:
-
-```py
->>> batch_sentences = [
-...     "But what about second breakfast?",
-...     "Don't think he knows about second breakfast, Pip.",
-...     "What about elevensies?",
-... ]
->>> encoded_input = tokenizer(batch_sentences, padding=True)
->>> print(encoded_input)
-{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0], 
-               [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], 
-               [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]], 
- 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 
- 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 
-                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                    [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
-```
-
-Beachten Sie, dass der Tokenizer den ersten und den dritten Satz mit einer "0" aufgefüllt hat, weil sie kürzer sind!
-
-### Kürzung
-
-Auf der anderen Seite des Spektrums kann es vorkommen, dass eine Sequenz zu lang für ein Modell ist. In diesem Fall müssen Sie die Sequenz auf eine kürzere Länge kürzen.
-
-Setzen Sie den Parameter "truncation" auf "true", um eine Sequenz auf die vom Modell akzeptierte Höchstlänge zu kürzen:
-
-```py
->>> batch_sentences = [
-...     "But what about second breakfast?",
-...     "Don't think he knows about second breakfast, Pip.",
-...     "What about elevensies?",
-... ]
->>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True)
->>> print(encoded_input)
-{'input_ids': [[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0], 
-               [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102], 
-               [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]], 
- 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 
-                    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 
- 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0], 
-                    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 
-                    [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
-```
-
-### Tensoren erstellen
-
-Schließlich möchten Sie, dass der Tokenizer die tatsächlichen Tensoren zurückgibt, die dem Modell zugeführt werden.
-
-Setzen Sie den Parameter `return_tensors` entweder auf `pt` für PyTorch, oder `tf` für TensorFlow:
-
-<frameworkcontent>
-<pt>
-
-```py
->>> batch_sentences = [
-...     "But what about second breakfast?",
-...     "Don't think he knows about second breakfast, Pip.",
-...     "What about elevensies?",
-... ]
->>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="pt")
->>> print(encoded_input)
-{'input_ids': tensor([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
-                      [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
-                      [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]]), 
- 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-                           [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 
- 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
-                           [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-                           [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]])}
-```
-</pt>
-<tf>
-```py
->>> batch_sentences = [
-...     "But what about second breakfast?",
-...     "Don't think he knows about second breakfast, Pip.",
-...     "What about elevensies?",
-... ]
->>> encoded_input = tokenizer(batch_sentences, padding=True, truncation=True, return_tensors="tf")
->>> print(encoded_input)
-{'input_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
-array([[101, 1252, 1184, 1164, 1248, 6462, 136, 102, 0, 0, 0, 0, 0, 0, 0],
-       [101, 1790, 112, 189, 1341, 1119, 3520, 1164, 1248, 6462, 117, 21902, 1643, 119, 102],
-       [101, 1327, 1164, 5450, 23434, 136, 102, 0, 0, 0, 0, 0, 0, 0, 0]],
-      dtype=int32)>, 
- 'token_type_ids': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
-array([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
-       [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>, 
- 'attention_mask': <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
-array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
-       [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
-       [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]], dtype=int32)>}
-```
-</tf>
-</frameworkcontent>
-
-## Audio
-
-Audioeingaben werden anders vorverarbeitet als Texteingaben, aber das Endziel bleibt dasselbe: numerische Sequenzen zu erstellen, die das Modell verstehen kann. Ein [feature extractor](main_classes/feature_extractor) dient dem ausdrücklichen Zweck, Merkmale aus Rohbild- oder Audiodaten zu extrahieren und in Tensoren zu konvertieren. Bevor Sie beginnen, installieren Sie 🤗 Datasets, um einen Audio-Datensatz zu laden, mit dem Sie experimentieren können:
-
-```bash
-pip install datasets
-```
-
-Laden Sie den [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) Datensatz (weitere Informationen zum Laden eines Datensatzes finden Sie im 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html)):
-
-```py
->>> from datasets import load_dataset, Audio
-
->>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
-```
-
-Greifen Sie auf das erste Element der `audio`-Spalte zu, um einen Blick auf die Eingabe zu werfen. Durch den Aufruf der Spalte "audio" wird die Audiodatei automatisch geladen und neu gesampelt:
-
-```py
->>> dataset[0]["audio"]
-{'array': array([ 0.        ,  0.00024414, -0.00024414, ..., -0.00024414,
-         0.        ,  0.        ], dtype=float32),
- 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
- 'sampling_rate': 8000}
-```
-
-Dies gibt drei Elemente zurück:
-
-* "array" ist das Sprachsignal, das als 1D-Array geladen - und möglicherweise neu gesampelt - wurde.
-* Pfad" zeigt auf den Speicherort der Audiodatei.
-* `sampling_rate` bezieht sich darauf, wie viele Datenpunkte im Sprachsignal pro Sekunde gemessen werden.
-
-### Resample
-
-Für dieses Tutorial werden Sie das Modell [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) verwenden. Wie Sie aus der Modellkarte ersehen können, ist das Wav2Vec2-Modell auf 16kHz abgetastetes Sprachaudio vortrainiert. Es ist wichtig, dass die Abtastrate Ihrer Audiodaten mit der Abtastrate des Datensatzes übereinstimmt, der für das Pre-Training des Modells verwendet wurde. Wenn die Abtastrate Ihrer Daten nicht dieselbe ist, müssen Sie Ihre Audiodaten neu abtasten. 
-
-Der Datensatz [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) hat zum Beispiel eine Abtastrate von 8000 kHz. Um das Wav2Vec2-Modell mit diesem Datensatz verwenden zu können, müssen Sie die Abtastrate auf 16 kHz erhöhen:
-
-```py
->>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
->>> dataset[0]["audio"]
-{'array': array([ 0.        ,  0.00024414, -0.00024414, ..., -0.00024414,
-         0.        ,  0.        ], dtype=float32),
- 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
- 'sampling_rate': 8000}
-```
-
-1. Verwenden Sie die Methode [~datasets.Dataset.cast_column] von 🤗 Datasets, um die Abtastrate auf 16kHz zu erhöhen:
-
-```py
->>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
-```
-
-2. Laden Sie die Audiodatei:
-
-```py
->>> dataset[0]["audio"]
-{'array': array([ 2.3443763e-05,  2.1729663e-04,  2.2145823e-04, ...,
-         3.8356509e-05, -7.3497440e-06, -2.1754686e-05], dtype=float32),
- 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
- 'sampling_rate': 16000}
-```
-
-Wie Sie sehen können, ist die Abtastrate jetzt 16kHz!
-
-### Merkmalsextraktor
-
-Der nächste Schritt ist das Laden eines Merkmalsextraktors, um die Eingabe zu normalisieren und aufzufüllen. Beim Auffüllen von Textdaten wird für kürzere Sequenzen ein `0` hinzugefügt. Die gleiche Idee gilt für Audiodaten, und der Audio-Feature-Extraktor fügt eine `0` - interpretiert als Stille - zu `array` hinzu.
-
-Laden Sie den Merkmalsextraktor mit [`AutoFeatureExtractor.from_pretrained`]:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
-```
-
-Übergeben Sie das Audio-"Array" an den Feature-Extraktor. Wir empfehlen auch, das Argument `sampling_rate` im Feature Extractor hinzuzufügen, um eventuell auftretende stille Fehler besser zu beheben.
-
-```py
->>> audio_input = [dataset[0]["audio"]["array"]]
->>> feature_extractor(audio_input, sampling_rate=16000)
-{'input_values': [array([ 3.8106556e-04,  2.7506407e-03,  2.8015103e-03, ...,
-        5.6335266e-04,  4.6588284e-06, -1.7142107e-04], dtype=float32)]}
-```
-
-### Auffüllen und Kürzen
-
-Genau wie beim Tokenizer können Sie variable Sequenzen in einem Stapel durch Auffüllen oder Abschneiden behandeln. Werfen Sie einen Blick auf die Sequenzlänge dieser beiden Audiobeispiele:
-
-```py
->>> dataset[0]["audio"]["array"].shape
-(173398,)
-
->>> dataset[1]["audio"]["array"].shape
-(106496,)
-```
-
-Wie Sie sehen können, hat das erste Beispiel eine längere Sequenz als das zweite Beispiel. Lassen Sie uns eine Funktion erstellen, die den Datensatz vorverarbeitet. Geben Sie eine maximale Länge der Probe an, und der Feature-Extraktor wird die Sequenzen entweder auffüllen oder abschneiden, damit sie dieser Länge entsprechen:
-
-```py
->>> def preprocess_function(examples):
-...     audio_arrays = [x["array"] for x in examples["audio"]]
-...     inputs = feature_extractor(
-...         audio_arrays,
-...         sampling_rate=16000,
-...         padding=True,
-...         max_length=100000,
-...         truncation=True,
-...     )
-...     return inputs
-```
-
-Wenden Sie die Funktion auf die ersten paar Beispiele im Datensatz an:
-
-```py
->>> processed_dataset = preprocess_function(dataset[:5])
-```
-
-Schauen Sie sich nun noch einmal die verarbeiteten Beispiel-Längen an:
-
-```py
->>> processed_dataset["input_values"][0].shape
-(100000,)
-
->>> processed_dataset["input_values"][1].shape
-(100000,)
-```
-
-Die Länge der ersten beiden Beispiele entspricht nun der von Ihnen angegebenen Maximallänge.
-
-## Bildverarbeitung
-
-Ein Merkmalsextraktor wird auch verwendet, um Bilder für Bildverarbeitungsaufgaben zu verarbeiten. Auch hier besteht das Ziel darin, das Rohbild in eine Reihe von Tensoren als Eingabe zu konvertieren.
-
-Laden wir den [food101](https://huggingface.co/datasets/food101) Datensatz für dieses Tutorial. Verwenden Sie den Parameter 🤗 Datasets `split`, um nur eine kleine Stichprobe aus dem Trainingssplit zu laden, da der Datensatz recht groß ist:
-
-```py
->>> from datasets import load_dataset
-
->>> dataset = load_dataset("food101", split="train[:100]")
-```
-
-Als Nächstes sehen Sie sich das Bild mit dem Merkmal 🤗 Datensätze [Bild] (https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) an:
-
-```py
->>> dataset[0]["image"]
-```
-
-![vision-preprocess-tutorial.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png)
-
-### Merkmalsextraktor
-
-Laden Sie den Merkmalsextraktor mit [`AutoFeatureExtractor.from_pretrained`]:
-
-```py
->>> from transformers import AutoFeatureExtractor
-
->>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
-```
-
-### Datenerweiterung
-
-Bei Bildverarbeitungsaufgaben ist es üblich, den Bildern als Teil der Vorverarbeitung eine Art von Datenerweiterung hinzuzufügen. Sie können Erweiterungen mit jeder beliebigen Bibliothek hinzufügen, aber in diesem Tutorial werden Sie das Modul [`transforms`](https://pytorch.org/vision/stable/transforms.html) von torchvision verwenden.
-
-1. Normalisieren Sie das Bild und verwenden Sie [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html), um einige Transformationen - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) und [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - miteinander zu verknüpfen:
-
-```py
->>> from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ColorJitter, ToTensor
-
->>> normalize = Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
->>> _transforms = Compose(
-...     [RandomResizedCrop(feature_extractor.size), ColorJitter(brightness=0.5, hue=0.5), ToTensor(), normalize]
-... )
-```
-
-2. Das Modell akzeptiert [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) als Eingabe. Dieser Wert wird vom Merkmalsextraktor erzeugt. Erstellen Sie eine Funktion, die `pixel_values` aus den Transformationen erzeugt:
-
-```py
->>> def transforms(examples):
-...     examples["pixel_values"] = [_transforms(image.convert("RGB")) for image in examples["image"]]
-...     return examples
-```
-
-3. Dann verwenden Sie 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform), um die Transformationen im laufenden Betrieb anzuwenden:
-
-```py
->>> dataset.set_transform(transforms)
-```
-
-4. Wenn Sie nun auf das Bild zugreifen, werden Sie feststellen, dass der Feature Extractor die Modelleingabe "pixel_values" hinzugefügt hat:
-
-```py
->>> dataset[0]["image"]
-{'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=384x512 at 0x7F1A7B0630D0>,
- 'label': 6,
- 'pixel_values': tensor([[[ 0.0353,  0.0745,  0.1216,  ..., -0.9922, -0.9922, -0.9922],
-          [-0.0196,  0.0667,  0.1294,  ..., -0.9765, -0.9843, -0.9922],
-          [ 0.0196,  0.0824,  0.1137,  ..., -0.9765, -0.9686, -0.8667],
-          ...,
-          [ 0.0275,  0.0745,  0.0510,  ..., -0.1137, -0.1216, -0.0824],
-          [ 0.0667,  0.0824,  0.0667,  ..., -0.0588, -0.0745, -0.0980],
-          [ 0.0353,  0.0353,  0.0431,  ..., -0.0039, -0.0039, -0.0588]],
- 
-         [[ 0.2078,  0.2471,  0.2863,  ..., -0.9451, -0.9373, -0.9451],
-          [ 0.1608,  0.2471,  0.3098,  ..., -0.9373, -0.9451, -0.9373],
-          [ 0.2078,  0.2706,  0.3020,  ..., -0.9608, -0.9373, -0.8275],
-          ...,
-          [-0.0353,  0.0118, -0.0039,  ..., -0.2392, -0.2471, -0.2078],
-          [ 0.0196,  0.0353,  0.0196,  ..., -0.1843, -0.2000, -0.2235],
-          [-0.0118, -0.0039, -0.0039,  ..., -0.0980, -0.0980, -0.1529]],
- 
-         [[ 0.3961,  0.4431,  0.4980,  ..., -0.9216, -0.9137, -0.9216],
-          [ 0.3569,  0.4510,  0.5216,  ..., -0.9059, -0.9137, -0.9137],
-          [ 0.4118,  0.4745,  0.5216,  ..., -0.9137, -0.8902, -0.7804],
-          ...,
-          [-0.2314, -0.1922, -0.2078,  ..., -0.4196, -0.4275, -0.3882],
-          [-0.1843, -0.1686, -0.2000,  ..., -0.3647, -0.3804, -0.4039],
-          [-0.1922, -0.1922, -0.1922,  ..., -0.2941, -0.2863, -0.3412]]])}
-```
-
-Hier sehen Sie, wie das Bild nach der Vorverarbeitung aussieht. Wie von den angewandten Transformationen zu erwarten, wurde das Bild willkürlich beschnitten und seine Farbeigenschaften sind anders.
-
-```py
->>> import numpy as np
->>> import matplotlib.pyplot as plt
-
->>> img = dataset[0]["pixel_values"]
->>> plt.imshow(img.permute(1, 2, 0))
-```
-
-![preprocessed_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png)
-
-## Multimodal
-
-Für multimodale Aufgaben werden Sie eine Kombination aus allem, was Sie bisher gelernt haben, verwenden und Ihre Fähigkeiten auf eine Aufgabe der automatischen Spracherkennung (ASR) anwenden. Dies bedeutet, dass Sie einen:
-
-* Feature Extractor zur Vorverarbeitung der Audiodaten.
-* Tokenizer, um den Text zu verarbeiten.
-
-Kehren wir zum [LJ Speech](https://huggingface.co/datasets/lj_speech) Datensatz zurück:
-
-```py
->>> from datasets import load_dataset
-
->>> lj_speech = load_dataset("lj_speech", split="train")
-```
-
-Da Sie hauptsächlich an den Spalten "Audio" und "Text" interessiert sind, entfernen Sie die anderen Spalten:
-
-```py
->>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
-```
-
-Schauen Sie sich nun die Spalten "Audio" und "Text" an:
-
-```py
->>> lj_speech[0]["audio"]
-{'array': array([-7.3242188e-04, -7.6293945e-04, -6.4086914e-04, ...,
-         7.3242188e-04,  2.1362305e-04,  6.1035156e-05], dtype=float32),
- 'path': '/root/.cache/huggingface/datasets/downloads/extracted/917ece08c95cf0c4115e45294e3cd0dee724a1165b7fc11798369308a465bd26/LJSpeech-1.1/wavs/LJ001-0001.wav',
- 'sampling_rate': 22050}
-
->>> lj_speech[0]["text"]
-'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
-```
-
-Erinnern Sie sich an den früheren Abschnitt über die Verarbeitung von Audiodaten: Sie sollten immer die Abtastrate Ihrer Audiodaten [resample](preprocessing#audio), damit sie mit der Abtastrate des Datensatzes übereinstimmt, der für das Vortraining eines Modells verwendet wird:
-
-```py
->>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
-```
-
-### Prozessor
-
-Ein Processor kombiniert einen Feature-Extraktor und einen Tokenizer. Laden Sie einen Processor mit [`AutoProcessor.from_pretrained]:
-
-```py
->>> from transformers import AutoProcessor
-
->>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
-```
-
-1. Erstellen Sie eine Funktion, die die Audiodaten zu `input_values` verarbeitet und den Text zu `labels` tokenisiert. Dies sind Ihre Eingaben für das Modell:
-
-```py
->>> def prepare_dataset(example):
-...     audio = example["audio"]
-
-...     example.update(processor(audio=audio["array"], text=example["text"], sampling_rate=16000))
-
-...     return example
-```
-
-2. Wenden Sie die Funktion "prepare_dataset" auf ein Beispiel an:
-
-```py
->>> prepare_dataset(lj_speech[0])
-```
-
-Beachten Sie, dass der Processor `input_values` und `labels` hinzugefügt hat. Auch die Abtastrate wurde korrekt auf 16kHz heruntergerechnet.
-
-Toll, Sie sollten jetzt in der Lage sein, Daten für jede Modalität vorzuverarbeiten und sogar verschiedene Modalitäten zu kombinieren! Im nächsten Kurs lernen Sie, wie Sie ein Modell mit Ihren neu aufbereiteten Daten feinabstimmen können.

docs/source/de/training.mdx

@@ -1,427 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Optimierung eines vortrainierten Modells
-
-[[open-in-colab]]
-
-Die Verwendung eines vorab trainierten Modells hat erhebliche Vorteile. Es reduziert die Rechenkosten und den CO2-Fußabdruck und ermöglicht Ihnen die Verwendung von Modellen, die dem neuesten Stand der Technik entsprechen, ohne dass Sie ein Modell von Grund auf neu trainieren müssen. Transformers bietet Zugang zu Tausenden von vortrainierten Modellen für eine Vielzahl von Aufgaben. Wenn Sie ein vorab trainiertes Modell verwenden, trainieren Sie es auf einem für Ihre Aufgabe spezifischen Datensatz. Dies wird als Feinabstimmung bezeichnet und ist eine unglaublich leistungsfähige Trainingstechnik. In diesem Tutorial werden Sie ein vortrainiertes Modell mit einem Deep-Learning-Framework Ihrer Wahl feinabstimmen:
-
-* Feinabstimmung eines vorab trainierten Modells mit 🤗 Transformers [`Trainer`].
-* Feinabstimmung eines vorab trainierten Modells in TensorFlow mit Keras.
-* Feinabstimmung eines vorab trainierten Modells in nativem PyTorch.
-
-<a id='data-processing'></a>
-
-## Vorbereitung eines Datensatzes
-
-<Youtube id="_BZearw7f0w"/>
-
-Bevor Sie die Feinabstimmung eines vortrainierten Modells vornehmen können, müssen Sie einen Datensatz herunterladen und für das Training vorbereiten. Im vorangegangenen Leitfaden haben Sie gelernt, wie man Daten für das Training aufbereitet, und jetzt haben Sie die Gelegenheit, diese Fähigkeiten zu testen!
-
-Laden Sie zunächst den Datensatz [Yelp Reviews](https://huggingface.co/datasets/yelp_review_full):
-
-```py
->>> from datasets import load_dataset
-
->>> dataset = load_dataset("yelp_review_full")
->>> dataset["train"][100]
-{'label': 0,
- 'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
-```
-
-Wie Sie nun wissen, benötigen Sie einen Tokenizer, um den Text zu verarbeiten und eine Auffüll- und Abschneidungsstrategie einzubauen, um mit variablen Sequenzlängen umzugehen. Um Ihren Datensatz in einem Schritt zu verarbeiten, verwenden Sie die 🤗 Methode Datasets [`map`](https://huggingface.co/docs/datasets/process.html#map), um eine Vorverarbeitungsfunktion auf den gesamten Datensatz anzuwenden:
-
-```py
->>> from transformers import AutoTokenizer
-
->>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-
-
->>> def tokenize_function(examples):
-...     return tokenizer(examples["text"], padding="max_length", truncation=True)
-
-
->>> tokenized_datasets = dataset.map(tokenize_function, batched=True)
-```
-
-Wenn Sie möchten, können Sie eine kleinere Teilmenge des gesamten Datensatzes für die Feinabstimmung erstellen, um den Zeitaufwand zu verringern:
-
-```py
->>> small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
->>> small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
-```
-
-<a id='trainer'></a>
-
-## Training
-
-An dieser Stelle sollten Sie dem Abschnitt folgen, der dem Rahmen entspricht, den Sie verwenden möchten. Sie können über die Links
-in der rechten Seitenleiste können Sie zu dem gewünschten Abschnitt springen - und wenn Sie den gesamten Inhalt eines bestimmten Frameworks ausblenden möchten,
-klicken Sie einfach auf die Schaltfläche oben rechts im Block des jeweiligen Frameworks!
-
-<frameworkcontent>
-<pt>
-<Youtube id="nvBXf7s7vTI"/>
-
-## Trainieren mit PyTorch Trainer
-
-🤗 Transformers bietet eine [`Trainer`]-Klasse, die für das Training von 🤗 Transformers-Modellen optimiert ist und es einfacher macht, mit dem Training zu beginnen, ohne manuell eine eigene Trainingsschleife zu schreiben. Die [`Trainer`]-API unterstützt eine breite Palette von Trainingsoptionen und Funktionen wie Logging, Gradientenakkumulation und gemischte Präzision.
-
-Beginnen Sie mit dem Laden Ihres Modells und geben Sie die Anzahl der erwarteten Labels an. Aus dem Yelp Review [dataset card](https://huggingface.co/datasets/yelp_review_full#data-fields) wissen Sie, dass es fünf Labels gibt:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
-```
-
-<Tip>
-
-Es wird eine Warnung angezeigt, dass einige der trainierten Parameter nicht verwendet werden und einige Parameter zufällig
-initialisiert werden. Machen Sie sich keine Sorgen, das ist völlig normal! Der vorher trainierte Kopf des BERT-Modells wird verworfen und durch einen zufällig initialisierten Klassifikationskopf ersetzt. Sie werden diesen neuen Modellkopf in Ihrer Sequenzklassifizierungsaufgabe feinabstimmen, indem Sie das Wissen des vortrainierten Modells auf ihn übertragen.
-
-</Tip>
-
-### Hyperparameter für das Training
-
-Als Nächstes erstellen Sie eine Klasse [`TrainingArguments`], die alle Hyperparameter enthält, die Sie einstellen können, sowie Flags zur Aktivierung verschiedener Trainingsoptionen. Für dieses Lernprogramm können Sie mit den Standard- [Hyperparametern](https://huggingface.co/docs/transformers/main_classes/trainer#transformers.TrainingArguments) beginnen, aber Sie können mit diesen experimentieren, um Ihre optimalen Einstellungen zu finden.
-
-Geben Sie an, wo die Kontrollpunkte Ihres Trainings gespeichert werden sollen:
-
-```py
->>> from transformers import TrainingArguments
-
->>> training_args = TrainingArguments(output_dir="test_trainer")
-```
-
-### Auswerten
-
-Der [`Trainer`] wertet die Leistung des Modells während des Trainings nicht automatisch aus. Sie müssen [`Trainer`] eine Funktion übergeben, um Metriken zu berechnen und zu berichten. Die [🤗 Evaluate](https://huggingface.co/docs/evaluate/index) Bibliothek bietet eine einfache [`accuracy`](https://huggingface.co/spaces/evaluate-metric/accuracy) Funktion, die Sie mit der [`evaluate.load`] Funktion laden können (siehe diese [quicktour](https://huggingface.co/docs/evaluate/a_quick_tour) für weitere Informationen):
-
-```py
->>> import numpy as np
->>> import evaluate
-
->>> metric = evaluate.load("accuracy")
-```
-
-Rufen Sie [`~evaluate.compute`] auf `metric` auf, um die Genauigkeit Ihrer Vorhersagen zu berechnen. Bevor Sie Ihre Vorhersagen an `compute` übergeben, müssen Sie die Vorhersagen in Logits umwandeln (denken Sie daran, dass alle 🤗 Transformers-Modelle Logits zurückgeben):
-
-```py
->>> def compute_metrics(eval_pred):
-...     logits, labels = eval_pred
-...     predictions = np.argmax(logits, axis=-1)
-...     return metric.compute(predictions=predictions, references=labels)
-```
-
-Wenn Sie Ihre Bewertungsmetriken während der Feinabstimmung überwachen möchten, geben Sie den Parameter `evaluation_strategy` in Ihren Trainingsargumenten an, um die Bewertungsmetrik am Ende jeder Epoche zu ermitteln:
-
-```py
->>> from transformers import TrainingArguments, Trainer
-
->>> training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
-```
-
-### Trainer
-
-Erstellen Sie ein [`Trainer`]-Objekt mit Ihrem Modell, Trainingsargumenten, Trainings- und Testdatensätzen und einer Evaluierungsfunktion:
-
-```py
->>> trainer = Trainer(
-...     model=model,
-...     args=training_args,
-...     train_dataset=small_train_dataset,
-...     eval_dataset=small_eval_dataset,
-...     compute_metrics=compute_metrics,
-... )
-```
-
-Anschließend können Sie Ihr Modell durch den Aufruf von [`~transformers.Trainer.train`] optimieren:
-
-```py
->>> trainer.train()
-```
-</pt>
-<tf>
-<a id='keras'></a>
-
-<Youtube id="rnTGBy2ax1c"/>
-
-## Trainieren Sie ein TensorFlow-Modell mit Keras
-
-Sie können auch 🤗 Transformers Modelle in TensorFlow mit der Keras API trainieren!
-
-### Laden von Daten für Keras
-
-Wenn Sie ein 🤗 Transformers Modell mit der Keras API trainieren wollen, müssen Sie Ihren Datensatz in ein Format konvertieren, das
-Keras versteht. Wenn Ihr Datensatz klein ist, können Sie das Ganze einfach in NumPy-Arrays konvertieren und an Keras übergeben.
-Probieren wir das zuerst aus, bevor wir etwas Komplizierteres tun.
-
-Laden Sie zunächst ein Dataset. Wir werden den CoLA-Datensatz aus dem [GLUE-Benchmark](https://huggingface.co/datasets/glue) verwenden,
-da es sich um eine einfache Aufgabe zur Klassifizierung von binärem Text handelt, und nehmen vorerst nur den Trainingssplit.
-
-```py
-from datasets import load_dataset
-
-dataset = load_dataset("glue", "cola")
-dataset = dataset["train"]  # Just take the training split for now
-```
-
-Als nächstes laden Sie einen Tokenizer und tokenisieren die Daten als NumPy-Arrays. Beachten Sie, dass die Beschriftungen bereits eine Liste von 0 und 1en sind,
-Wir können sie also ohne Tokenisierung direkt in ein NumPy-Array konvertieren!
-
-```py
-from transformers import AutoTokenizer
-
-tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
-tokenized_data = tokenizer(dataset["text"], return_tensors="np", padding=True)
-
-labels = np.array(dataset["label"])  # Label is already an array of 0 and 1
-```
-
-Schließlich laden, [`compile`](https://keras.io/api/models/model_training_apis/#compile-method) und [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) Sie das Modell:
-
-```py
-from transformers import TFAutoModelForSequenceClassification
-from tensorflow.keras.optimizers import Adam
-
-# Load and compile our model
-model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased")
-# Lower learning rates are often better for fine-tuning transformers
-model.compile(optimizer=Adam(3e-5))
-
-model.fit(tokenized_data, labels)
-```
-
-<Tip>
-
-Sie müssen Ihren Modellen kein Verlustargument übergeben, wenn Sie sie `compile()`! Hugging-Face-Modelle wählen automatisch
-einen Loss, der für ihre Aufgabe und Modellarchitektur geeignet ist, wenn dieses Argument leer gelassen wird. Sie können jederzeit außer Kraft setzen, indem Sie selbst einen Loss angeben, wenn Sie das möchten!
-
-</Tip>
-
-Dieser Ansatz eignet sich hervorragend für kleinere Datensätze, aber bei größeren Datensätzen kann er zu einem Problem werden. Warum?
-Weil das tokenisierte Array und die Beschriftungen vollständig in den Speicher geladen werden müssten, und weil NumPy nicht mit
-"gezackte" Arrays nicht verarbeiten kann, so dass jedes tokenisierte Sample auf die Länge des längsten Samples im gesamten Datensatz aufgefüllt werden müsste.
-Datensatzes aufgefüllt werden. Dadurch wird das Array noch größer, und all die aufgefüllten Token verlangsamen auch das Training!
-
-### Laden von Daten als tf.data.Dataset
-
-Wenn Sie eine Verlangsamung des Trainings vermeiden wollen, können Sie Ihre Daten stattdessen als `tf.data.Dataset` laden. Sie können zwar Ihre eigene
-tf.data"-Pipeline schreiben können, wenn Sie wollen, haben wir zwei bequeme Methoden, um dies zu tun:
-
-- [`~TFPreTrainedModel.prepare_tf_dataset`]: Dies ist die Methode, die wir in den meisten Fällen empfehlen. Da es sich um eine Methode
-Ihres Modells ist, kann sie das Modell inspizieren, um automatisch herauszufinden, welche Spalten als Modelleingaben verwendet werden können, und
-verwirft die anderen, um einen einfacheren, leistungsfähigeren Datensatz zu erstellen.
-- [~datasets.Dataset.to_tf_dataset`]: Diese Methode ist eher auf niedriger Ebene angesiedelt und ist nützlich, wenn Sie genau kontrollieren wollen, wie
-Dataset erstellt wird, indem man genau angibt, welche `columns` und `label_cols` einbezogen werden sollen.
-
-Bevor Sie [~TFPreTrainedModel.prepare_tf_dataset`] verwenden können, müssen Sie die Tokenizer-Ausgaben als Spalten zu Ihrem Datensatz hinzufügen, wie in
-dem folgenden Codebeispiel:
-
-```py
-def tokenize_dataset(data):
-    # Keys of the returned dictionary will be added to the dataset as columns
-    return tokenizer(data["text"])
-
-
-dataset = dataset.map(tokenize_dataset)
-```
-
-Denken Sie daran, dass Hugging Face-Datensätze standardmäßig auf der Festplatte gespeichert werden, so dass dies nicht zu einem erhöhten Arbeitsspeicherbedarf führen wird! Sobald die
-Spalten hinzugefügt wurden, können Sie Batches aus dem Datensatz streamen und zu jedem Batch Auffüllungen hinzufügen, was die Anzahl der Auffüllungs-Token im Vergleich zum Auffüllen des gesamten Datensatzes reduziert.
-
-
-```py
->>> tf_dataset = model.prepare_tf_dataset(dataset, batch_size=16, shuffle=True, tokenizer=tokenizer)
-```
-
-Beachten Sie, dass Sie im obigen Codebeispiel den Tokenizer an `prepare_tf_dataset` übergeben müssen, damit die Stapel beim Laden korrekt aufgefüllt werden können.
-Wenn alle Stichproben in Ihrem Datensatz die gleiche Länge haben und kein Auffüllen erforderlich ist, können Sie dieses Argument weglassen.
-Wenn Sie etwas Komplexeres als nur das Auffüllen von Stichproben benötigen (z. B. das Korrumpieren von Token für die maskierte Sprachmodellierung), können Sie das Argument
-Modellierung), können Sie stattdessen das Argument `collate_fn` verwenden, um eine Funktion zu übergeben, die aufgerufen wird, um die
-Liste von Stichproben in einen Stapel umwandelt und alle gewünschten Vorverarbeitungen vornimmt. Siehe unsere
-[examples](https://github.com/huggingface/transformers/tree/main/examples) oder
-[notebooks](https://huggingface.co/docs/transformers/notebooks), um diesen Ansatz in Aktion zu sehen.
-
-Sobald Sie einen `tf.data.Dataset` erstellt haben, können Sie das Modell wie zuvor kompilieren und anpassen:
-
-```py
-model.compile(optimizer=Adam(3e-5))
-
-model.fit(tf_dataset)
-```
-
-</tf>
-</frameworkcontent>
-
-<a id='pytorch_native'></a>
-
-## Trainieren in nativem PyTorch
-
-<frameworkcontent>
-<pt>
-<Youtube id="Dh9CL8fyG80"/>
-
-[`Trainer`] kümmert sich um die Trainingsschleife und ermöglicht die Feinabstimmung eines Modells in einer einzigen Codezeile. Für Benutzer, die es vorziehen, ihre eigene Trainingsschleife zu schreiben, können Sie auch eine Feinabstimmung eines 🤗 Transformers-Modells in nativem PyTorch vornehmen.
-
-An diesem Punkt müssen Sie möglicherweise Ihr Notebook neu starten oder den folgenden Code ausführen, um etwas Speicher freizugeben:
-
-```py
-del model
-del pytorch_model
-del trainer
-torch.cuda.empty_cache()
-```
-
-Als Nächstes müssen Sie den Datensatz `tokenized_dataset` manuell nachbearbeiten, um ihn für das Training vorzubereiten.
-
-1. Entfernen Sie die Spalte "Text", da das Modell keinen Rohtext als Eingabe akzeptiert:
-
-    ```py
-    >>> tokenized_datasets = tokenized_datasets.remove_columns(["text"])
-    ```
-
-2. Benennen Sie die Spalte "Label" in "Labels" um, da das Modell erwartet, dass das Argument "Labels" genannt wird:
-
-    ```py
-    >>> tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
-    ```
-
-3. Stellen Sie das Format des Datensatzes so ein, dass PyTorch-Tensoren anstelle von Listen zurückgegeben werden:
-
-    ```py
-    >>> tokenized_datasets.set_format("torch")
-    ```
-
-Erstellen Sie dann eine kleinere Teilmenge des Datensatzes, wie zuvor gezeigt, um die Feinabstimmung zu beschleunigen:
-
-```py
->>> small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
->>> small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
-```
-
-### DataLoader
-
-Erstellen Sie einen `DataLoader` für Ihre Trainings- und Testdatensätze, damit Sie über die Datenstapel iterieren können:
-
-```py
->>> from torch.utils.data import DataLoader
-
->>> train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
->>> eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)
-```
-
-Laden Sie Ihr Modell mit der Anzahl der erwarteten Kennzeichnungen:
-
-```py
->>> from transformers import AutoModelForSequenceClassification
-
->>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=5)
-```
-
-### Optimierer und Lernratensteuerung
-
-Erstellen Sie einen Optimierer und einen Scheduler für die Lernrate, um das Modell fein abzustimmen. Wir verwenden den Optimierer [`AdamW`](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html) aus PyTorch:
-
-```py
->>> from torch.optim import AdamW
-
->>> optimizer = AdamW(model.parameters(), lr=5e-5)
-```
-
-Erstellen Sie den Standard-Lernratenplaner aus [`Trainer`]:
-
-```py
->>> from transformers import get_scheduler
-
->>> num_epochs = 3
->>> num_training_steps = num_epochs * len(train_dataloader)
->>> lr_scheduler = get_scheduler(
-...     name="linear", optimizer=optimizer, num_warmup_steps=0, num_training_steps=num_training_steps
-... )
-```
-
-Geben Sie schließlich `device` an, um einen Grafikprozessor zu verwenden, wenn Sie Zugang zu einem solchen haben. Andernfalls kann das Training auf einer CPU mehrere Stunden statt ein paar Minuten dauern.
-
-```py
->>> import torch
-
->>> device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
->>> model.to(device)
-```
-
-<Tip>
-
-Holen Sie sich mit einem gehosteten Notebook wie [Colaboratory](https://colab.research.google.com/) oder [SageMaker StudioLab](https://studiolab.sagemaker.aws/) kostenlosen Zugang zu einem Cloud-GPU, wenn Sie noch keinen haben.
-
-</Tip>
-
-Großartig, Sie sind bereit für das Training! 🥳 
-
-### Trainingsschleife
-
-Um Ihren Trainingsfortschritt zu verfolgen, verwenden Sie die [tqdm](https://tqdm.github.io/) Bibliothek, um einen Fortschrittsbalken über die Anzahl der Trainingsschritte hinzuzufügen:
-
-```py
->>> from tqdm.auto import tqdm
-
->>> progress_bar = tqdm(range(num_training_steps))
-
->>> model.train()
->>> for epoch in range(num_epochs):
-...     for batch in train_dataloader:
-...         batch = {k: v.to(device) for k, v in batch.items()}
-...         outputs = model(**batch)
-...         loss = outputs.loss
-...         loss.backward()
-
-...         optimizer.step()
-...         lr_scheduler.step()
-...         optimizer.zero_grad()
-...         progress_bar.update(1)
-```
-
-### Auswertung
-
-Genauso wie Sie eine Bewertungsfunktion zu [`Trainer`] hinzugefügt haben, müssen Sie dasselbe tun, wenn Sie Ihre eigene Trainingsschleife schreiben. Aber anstatt die Metrik am Ende jeder Epoche zu berechnen und zu melden, werden Sie dieses Mal alle Stapel mit [`~evaluate.add_batch`] akkumulieren und die Metrik ganz am Ende berechnen.
-
-```py
->>> import evaluate
-
->>> metric = evaluate.load("accuracy")
->>> model.eval()
->>> for batch in eval_dataloader:
-...     batch = {k: v.to(device) for k, v in batch.items()}
-...     with torch.no_grad():
-...         outputs = model(**batch)
-
-...     logits = outputs.logits
-...     predictions = torch.argmax(logits, dim=-1)
-...     metric.add_batch(predictions=predictions, references=batch["labels"])
-
->>> metric.compute()
-```
-</pt>
-</frameworkcontent>
-
-<a id='additional-resources'></a>
-
-## Zusätzliche Ressourcen
-
-Weitere Beispiele für die Feinabstimmung finden Sie unter:
-
-- [🤗 Transformers Examples](https://github.com/huggingface/transformers/tree/main/examples) enthält Skripte
-  um gängige NLP-Aufgaben in PyTorch und TensorFlow zu trainieren.
-
-- [🤗 Transformers Notebooks](notebooks) enthält verschiedene Notebooks zur Feinabstimmung eines Modells für bestimmte Aufgaben in PyTorch und TensorFlow.

docs/source/en/_toctree.yml

@@ -31,11 +31,9 @@
     - local: sagemaker
       title: Run training on Amazon SageMaker
     - local: converting_tensorflow_models
-      title: Converting from TensorFlow checkpoints
+      title: Converting TensorFlow Checkpoints
     - local: serialization
-      title: Export to ONNX
-    - local: torchscript
-      title: Export to TorchScript
+      title: Export 🤗 Transformers models
     - local: troubleshooting
       title: Troubleshoot
     title: General usage
@@ -103,16 +101,12 @@
       title: Instantiating a big model
     - local: debugging
       title: Debugging
-    - local: hpo_train
-      title: Hyperparameter Search using Trainer API
     title: Performance and scalability
   - sections:
     - local: contributing
       title: How to contribute to transformers?
     - local: add_new_model
       title: How to add a model to 🤗 Transformers?
-    - local: add_tensorflow_model
-      title: How to convert a 🤗 Transformers model to TensorFlow?
     - local: add_new_pipeline
       title: How to add a pipeline to 🤗 Transformers?
     - local: testing
@@ -149,8 +143,6 @@
   title: Conceptual guides
 - sections:
   - sections:
-    - local: model_doc/auto
-      title: Auto Classes
     - local: main_classes/callback
       title: Callbacks
     - local: main_classes/configuration
@@ -185,6 +177,8 @@
       title: Feature Extractor
     title: Main Classes
   - sections:
+    - local: model_doc/auto
+      title: Auto Classes
     - isExpanded: false
       sections:
       - local: model_doc/albert
@@ -245,8 +239,6 @@
         title: Encoder Decoder Models
       - local: model_doc/ernie
         title: ERNIE
-      - local: model_doc/esm
-        title: ESM
       - local: model_doc/flaubert
         title: FlauBERT
       - local: model_doc/fnet
@@ -261,8 +253,6 @@
         title: GPT Neo
       - local: model_doc/gpt_neox
         title: GPT NeoX
-      - local: model_doc/gpt_neox_japanese
-        title: GPT NeoX Japanese
       - local: model_doc/gptj
         title: GPT-J
       - local: model_doc/gpt2
@@ -275,8 +265,6 @@
         title: LayoutLM
       - local: model_doc/led
         title: LED
-      - local: model_doc/lilt
-        title: LiLT
       - local: model_doc/longformer
         title: Longformer
       - local: model_doc/longt5
@@ -287,8 +275,6 @@
         title: M2M100
       - local: model_doc/marian
         title: MarianMT
-      - local: model_doc/markuplm
-        title: MarkupLM
       - local: model_doc/mbart
         title: MBart and MBart-50
       - local: model_doc/megatron-bert
@@ -374,14 +360,10 @@
       sections:
       - local: model_doc/beit
         title: BEiT
-      - local: model_doc/conditional_detr
-        title: Conditional DETR
       - local: model_doc/convnext
         title: ConvNeXT
       - local: model_doc/cvt
         title: CvT
-      - local: model_doc/deformable_detr
-        title: Deformable DETR
       - local: model_doc/deit
         title: DeiT
       - local: model_doc/detr
@@ -420,8 +402,6 @@
         title: Vision Transformer (ViT)
       - local: model_doc/vit_mae
         title: ViTMAE
-      - local: model_doc/vit_msn
-        title: ViTMSN
       - local: model_doc/yolos
         title: YOLOS
       title: Vision models
@@ -451,8 +431,6 @@
         title: Wav2Vec2Phoneme
       - local: model_doc/wavlm
         title: WavLM
-      - local: model_doc/whisper
-        title: Whisper
       - local: model_doc/xls_r
         title: XLS-R
       - local: model_doc/xlsr_wav2vec2
@@ -504,11 +482,6 @@
       - local: model_doc/trajectory_transformer
         title: Trajectory Transformer
       title: Reinforcement learning models
-    - isExpanded: false
-      sections:
-      - local: model_doc/time_series_transformer
-        title: Time Series Transformer
-      title: Time series models
     title: Models
   - sections:
     - local: internal/modeling_utils
@@ -521,9 +494,7 @@
       title: Utilities for Trainer
     - local: internal/generation_utils
       title: Utilities for Generation
-    - local: internal/image_processing_utils
-      title: Utilities for Image Processors
     - local: internal/file_utils
       title: General Utilities
     title: Internal Helpers
-  title: API
+  title: API

docs/source/en/add_new_model.mdx

@@ -106,7 +106,7 @@ own regarding how code should be written :-)
    for a good example).
 2. The code should be fully understandable, even by a non-native English speaker. This means you should pick
    descriptive variable names and avoid abbreviations. As an example, `activation` is preferred to `act`.
-   One-letter variable names are strongly discouraged unless it's an index in a for loop.
+   One-letter variable names are strongly discouraged unless it's an index in a for loop. 
 3. More generally we prefer longer explicit code to short magical one.
 4. Avoid subclassing `nn.Sequential` in PyTorch but subclass `nn.Module` and write the forward pass, so that anyone
    using your code can quickly debug it by adding print statements or breaking points.
@@ -222,7 +222,7 @@ cd ..
 5. To port *brand_new_bert*, you will also need access to its original repository:
 
 ```bash
-git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git
+git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git 
 cd brand_new_bert
 pip install -e .
 ```
@@ -683,11 +683,10 @@ work left to be done should be a cakewalk 😊.
 At this point, you have successfully added a new model. However, it is very much possible that the model does not yet
 fully comply with the required design. To make sure, the implementation is fully compatible with 🤗 Transformers, all
 common tests should pass. The Cookiecutter should have automatically added a test file for your model, probably under
-the same `tests/models/brand_new_bert/test_modeling_brand_new_bert.py`. Run this test file to verify that all common
-tests pass:
+the same `tests/test_modeling_brand_new_bert.py`. Run this test file to verify that all common tests pass:
 
 ```bash
-pytest tests/models/brand_new_bert/test_modeling_brand_new_bert.py
+pytest tests/test_modeling_brand_new_bert.py
 ```
 
 Having fixed all common tests, it is now crucial to ensure that all the nice work you have done is well tested, so that
@@ -701,7 +700,7 @@ Cookiecutter, called `BrandNewBertModelIntegrationTests` and only has to be fill
 tests are passing, run
 
 ```bash
-RUN_SLOW=1 pytest -sv tests/models/brand_new_bert/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
+RUN_SLOW=1 pytest -sv tests/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
 ```
 
 <Tip>
@@ -759,8 +758,7 @@ contain a couple of hard-coded integration tests.
 **10. Run End-to-end integration tests**
 
 Having added the tokenizer, you should also add a couple of end-to-end integration tests using both the model and the
-tokenizer to `tests/models/brand_new_bert/test_modeling_brand_new_bert.py` in 🤗 Transformers.
-Such a test should show on a meaningful
+tokenizer to `tests/test_modeling_brand_new_bert.py` in 🤗 Transformers. Such a test should show on a meaningful
 text-to-text sample that the 🤗 Transformers implementation works as expected. A meaningful text-to-text sample can
 include *e.g.* a source-to-target-translation pair, an article-to-summary pair, a question-to-answer pair, etc… If none
 of the ported checkpoints has been fine-tuned on a downstream task it is enough to simply rely on the model tests. In a

docs/source/en/add_tensorflow_model.mdx

@@ -1,346 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
--->
-
-# How to convert a 🤗 Transformers model to TensorFlow?
-
-Having multiple frameworks available to use with 🤗 Transformers gives you flexibility to play their strengths when
-designing your application, but it implies that compatibility must be added on a per-model basis. The good news is that
-adding TensorFlow compatibility to an existing model is simpler than [adding a new model from scratch](add_new_model)!
-Whether you wish to have a deeper understanding of large TensorFlow models, make a major open-source contribution, or
-enable TensorFlow for your model of choice, this guide is for you.
-
-This guide empowers you, a member of our community, to contribute TensorFlow model weights and/or
-architectures to be used in 🤗 Transformers, with minimal supervision from the Hugging Face team. Writing a new model
-is no small feat, but hopefully this guide will make it less of a rollercoaster 🎢 and more of a walk in the park 🚶.
-Harnessing our collective experiences is absolutely critical to make this process increasingly easier, and thus we
-highly encourage that you suggest improvements to this guide!
-
-Before you dive deeper, it is recommended that you check the following resources if you're new to 🤗 Transformers:
-- [General overview of 🤗 Transformers](add_new_model#general-overview-of-transformers)
-- [Hugging Face's TensorFlow Philosophy](https://huggingface.co/blog/tensorflow-philosophy)
-
-In the remainder of this guide, you will learn what's needed to add a new TensorFlow model architecture, the
-procedure to convert PyTorch into TensorFlow model weights, and how to efficiently debug mismatches across ML
-frameworks. Let's get started!
-
-<Tip>
-
-Are you unsure whether the model you wish to use already has a corresponding TensorFlow architecture?
-
-&nbsp;
-
-Check the `model_type` field of the `config.json` of your model of choice
-([example](https://huggingface.co/bert-base-uncased/blob/main/config.json#L14)). If the corresponding model folder in
-🤗 Transformers has a file whose name starts with "modeling_tf", it means that it has a corresponding TensorFlow
-architecture ([example](https://github.com/huggingface/transformers/tree/main/src/transformers/models/bert)).
-
-</Tip>
-
-
-## Step-by-step guide to add TensorFlow model architecture code
-
-There are many ways to design a large model architecture, and multiple ways of implementing said design. However,
-you might recall from our [general overview of 🤗 Transformers](add_new_model#general-overview-of-transformers)
-that we are an opinionated bunch - the ease of use of 🤗 Transformers relies on consistent design choices. From
-experience, we can tell you a few important things about adding TensorFlow models:
-
-- Don't reinvent the wheel! More often that not, there are at least two reference implementations you should check: the
-PyTorch equivalent of the model you are implementing and other TensorFlow models for the same class of problems.
-- Great model implementations survive the test of time. This doesn't happen because the code is pretty, but rather
-because the code is clear, easy to debug and build upon. If you make the life of the maintainers easy with your
-TensorFlow implementation, by replicating the same patterns as in other TensorFlow models and minimizing the mismatch
-to the PyTorch implementation, you ensure your contribution will be long lived.
-- Ask for help when you're stuck! The 🤗 Transformers team is here to help, and we've probably found solutions to the same
-problems you're facing.
-
-Here's an overview of the steps needed to add a TensorFlow model architecture:
-1. Select the model you wish to convert
-2. Prepare transformers dev environment
-3. (Optional) Understand theoretical aspects and the existing implementation
-4. Implement the model architecture
-5. Implement model tests
-6. Submit the pull request
-7. (Optional) Build demos and share with the world
-
-### 1.-3. Prepare your model contribution
-
-**1. Select the model you wish to convert**
-
-Let's start off with the basics: the first thing you need to know is the architecture you want to convert. If you
-don't have your eyes set on a specific architecture, asking the 🤗 Transformers team for suggestions is a great way to
-maximize your impact - we will guide you towards the most prominent architectures that are missing on the TensorFlow
-side. If the specific model you want to use with TensorFlow already has a TensorFlow architecture implementation in
-🤗 Transformers but is lacking weights, feel free to jump straight into the
-[weight conversion section](#adding-tensorflow-weights-to-hub)
-of this page.
-
-For simplicity, the remainder of this guide assumes you've decided to contribute with the TensorFlow version of
-*BrandNewBert* (the same example as in the [guide](add_new_model) to add a new model from scratch).
-
-<Tip>
-
-Before starting the work on a TensorFlow model architecture, double-check that there is no ongoing effort to do so.
-You can search for `BrandNewBert` on the
-[pull request GitHub page](https://github.com/huggingface/transformers/pulls?q=is%3Apr) to confirm that there is no
-TensorFlow-related pull request.
-
-</Tip>
-
-
-**2. Prepare transformers dev environment**
-
-Having selected the model architecture, open an draft PR to signal your intention to work on it. Follow the
-instructions below to set up your environment and open a draft PR.
-
-1. Fork the [repository](https://github.com/huggingface/transformers) by clicking on the 'Fork' button on the
-   repository's page. This creates a copy of the code under your GitHub user account.
-
-2. Clone your `transformers` fork to your local disk, and add the base repository as a remote:
-
-```bash
-git clone https://github.com/[your Github handle]/transformers.git
-cd transformers
-git remote add upstream https://github.com/huggingface/transformers.git
-```
-
-3. Set up a development environment, for instance by running the following command:
-
-```bash
-python -m venv .env
-source .env/bin/activate
-pip install -e ".[dev]"
-```
-
-**Note:** You don't need to have CUDA installed. Making the new model work on CPU is sufficient.
-
-4. Create a branch with a descriptive name from your main branch
-
-```bash
-git checkout -b add_tf_brand_new_bert
-```
-
-5. Fetch and rebase to current main
-
-```bash
-git fetch upstream
-git rebase upstream/main
-```
-
-6. Add an empty `.py` file in `transformers/src/models/brandnewbert/` named `modeling_tf_brandnewbert.py`. This will
-be your TensorFlow model file.
-
-7. Push the changes to your account using:
-
-```bash
-git add .
-git commit -m "initial commit"
-git push -u origin add_tf_brand_new_bert
-```
-
-8. Once you are satisfied, go to the webpage of your fork on GitHub. Click on “Pull request”. Make sure to add the
-   GitHub handle of some members of the Hugging Face team as reviewers, so that the Hugging Face team gets notified for
-   future changes.
-
-9. Change the PR into a draft by clicking on “Convert to draft” on the right of the GitHub pull request web page.
-
-
-Now you have set up a development environment to port *BrandNewBert* to TensorFlow in 🤗 Transformers.
-
-
-**3. (Optional) Understand theoretical aspects and the existing implementation**
-
-You should take some time to read *BrandNewBert's* paper, if such descriptive work exists. There might be large
-sections of the paper that are difficult to understand. If this is the case, this is fine - don't worry! The goal is
-not to get a deep theoretical understanding of the paper, but to extract the necessary information required to
-effectively re-implement the model in 🤗 Transformers using TensorFlow. That being said, you don't have to spend too
-much time on the theoretical aspects, but rather focus on the practical ones, namely the existing model documentation
-page (e.g. [model docs for BERT](model_doc/bert)).
-
-After you've grasped the basics of the models you are about to implement, it's important to understand the existing
-implementation. This is a great chance to confirm that a working implementation matches your expectations for the
-model, as well as to foresee technical challenges on the TensorFlow side.
-
-It's perfectly natural that you feel overwhelmed with the amount of information that you've just absorbed. It is
-definitely not a requirement that you understand all facets of the model at this stage. Nevertheless, we highly
-encourage you to clear any pressing questions in our [forum](https://discuss.huggingface.co/).
-
-
-### 4. Model implementation
-
-Now it's time to finally start coding. Our suggested starting point is the PyTorch file itself: copy the contents of
-`modeling_brand_new_bert.py` inside `src/transformers/models/brand_new_bert/` into
-`modeling_tf_brand_new_bert.py`. The goal of this section is to modify the file and update the import structure of
-🤗 Transformers such that you can import `TFBrandNewBert` and
-`TFBrandNewBert.from_pretrained(model_repo, from_pt=True)` sucessfully loads a working TensorFlow *BrandNewBert* model.
-
-Sadly, there is no prescription to convert a PyTorch model into TensorFlow. You can, however, follow our selection of
-tips to make the process as smooth as possible:
-- Prepend `TF` to the name of all classes (e.g. `BrandNewBert` becomes `TFBrandNewBert`).
-- Most PyTorch operations have a direct TensorFlow replacement. For example, `torch.nn.Linear` corresponds to
-  `tf.keras.layers.Dense`, `torch.nn.Dropout` corresponds to `tf.keras.layers.Dropout`, etc. If you're not sure
-  about a specific operation, you can use the [TensorFlow documentation](https://www.tensorflow.org/api_docs/python/tf)
-  or the [PyTorch documentation](https://pytorch.org/docs/stable/).
-- Look for patterns in the 🤗 Transformers codebase. If you come across a certain operation that doesn't have a direct
-   replacement, the odds are that someone else already had the same problem.
-- By default, keep the same variable names and structure as in PyTorch. This will make it easier to debug, track
-   issues, and add fixes down the line.
-- Some layers have different default values in each framework. A notable example is the batch normalization layer's
-   epsilon (`1e-5` in [PyTorch](https://pytorch.org/docs/stable/generated/torch.nn.BatchNorm2d.html#torch.nn.BatchNorm2d)
-   and `1e-3` in [TensorFlow](https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization)).
-   Double-check the documentation!
-- PyTorch's `nn.Parameter` variables typically need to be initialized within TF Layer's `build()`. See the following
-   example: [PyTorch](https://github.com/huggingface/transformers/blob/655f72a6896c0533b1bdee519ed65a059c2425ac/src/transformers/models/vit_mae/modeling_vit_mae.py#L212) /
-   [TensorFlow](https://github.com/huggingface/transformers/blob/655f72a6896c0533b1bdee519ed65a059c2425ac/src/transformers/models/vit_mae/modeling_tf_vit_mae.py#L220)
-- If the PyTorch model has a `#copied from ...` on top of a function, the odds are that your TensorFlow model can also
-   borrow that function from the architecture it was copied from, assuming it has a TensorFlow architecture.
-- Assigning the `name` attribute correctly in TensorFlow functions is critical to do the `from_pt=True` weight
-   cross-loading. `name` is almost always the name of the corresponding variable in the PyTorch code. If `name` is not
-   properly set, you will see it in the error message when loading the model weights.
-- The logic of the base model class, `BrandNewBertModel`, will actually reside in `TFBrandNewBertMainLayer`, a Keras
-   layer subclass ([example](https://github.com/huggingface/transformers/blob/4fd32a1f499e45f009c2c0dea4d81c321cba7e02/src/transformers/models/bert/modeling_tf_bert.py#L719)).
-   `TFBrandNewBertModel` will simply be a wrapper around this layer.
-- Keras models need to be built in order to load pretrained weights. For that reason, `TFBrandNewBertPreTrainedModel`
-   will need to hold an example of inputs to the model, the `dummy_inputs`
-   ([example](https://github.com/huggingface/transformers/blob/4fd32a1f499e45f009c2c0dea4d81c321cba7e02/src/transformers/models/bert/modeling_tf_bert.py#L916)).
-- If you get stuck, ask for help - we're here to help you! 🤗
-
-In addition to the model file itself, you will also need to add the pointers to the model classes and related
-documentation pages. You can complete this part entirely following the patterns in other PRs
-([example](https://github.com/huggingface/transformers/pull/18020/files)). Here's a list of the needed manual
-changes:
-- Include all public classes of *BrandNewBert* in `src/transformers/__init__.py`
-- Add *BrandNewBert* classes to the corresponing Auto classes in `src/transformers/models/auto/modeling_tf_auto.py`
-- Include the modeling file in the documentation test file list in `utils/documentation_tests.txt`
-- Add the lazy loading classes related to *BrandNewBert* in `src/transformers/utils/dummy_tf_objects.py`
-- Update the import structures for the public classes in `src/transformers/models/brand_new_bert/__init__.py`
-- Add the documentation pointers to the public methods of *BrandNewBert* in `docs/source/en/model_doc/brand_new_bert.mdx`
-- Add yourself to the list of contributors to *BrandNewBert* in `docs/source/en/model_doc/brand_new_bert.mdx`
-- Finally, add a green tick ✅ to the TensorFlow column of *BrandNewBert* in `docs/source/en/index.mdx`
-
-When you're happy with your implementation, run the following checklist to confirm that your model architecture is
-ready:
-1. All layers that behave differently at train time (e.g. Dropout) are called with a `training` argument, which is
-propagated all the way from the top-level classes
-2. You have used `#copied from ...` whenever possible
-3. `TFBrandNewBertMainLayer` and all classes that use it have their `call` function decorated with `@unpack_inputs`
-4. `TFBrandNewBertMainLayer` is decorated with `@keras_serializable`
-5. A TensorFlow model can be loaded from PyTorch weights using `TFBrandNewBert.from_pretrained(model_repo, from_pt=True)`
-6. You can call the TensorFlow model using the expected input format
-
-
-### 5. Add model tests
-
-Hurray, you've implemented a TensorFlow model! Now it's time to add tests to make sure that your model behaves as
-expected. As in the previous section, we suggest you start by copying the `test_modeling_brand_new_bert.py` file in
-`tests/models/brand_new_bert/` into `test_modeling_tf_brand_new_bert.py`, and continue by making the necessary
-TensorFlow replacements. For now, in all `.from_pretrained()` calls, you should use the `from_pt=True` flag to load
-the existing PyTorch weights.
-
-After you're done, it's time for the moment of truth: run the tests! 😬
-
-```bash
-NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
-py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
-```
-
-The most likely outcome is that you'll see a bunch of errors. Don't worry, this is expected! Debugging ML models is
-notoriously hard, and the key ingredient to success is patience (and `breakpoint()`). In our experience, the hardest
-problems arise from subtle mismatches between ML frameworks, for which we have a few pointers at the end of this guide.
-In other cases, a general test might not be directly applicable to your model, in which case we suggest an override
-at the model test class level. Regardless of the issue, don't hesitate to ask for help in your draft pull request if
-you're stuck.
-
-When all tests pass, congratulations, your model is nearly ready to be added to the 🤗 Transformers library! 🎉
-
-### 6.-7. Ensure everyone can use your model
-
-**6. Submit the pull request**
-
-Once you're done with the implementation and the tests, it's time to submit a pull request. Before pushing your code,
-run our code formatting utility, `make fixup` 🪄. This will automatically fix any formatting issues, which would cause
-our automatic checks to fail.
-
-It's now time to convert your draft pull request into a real pull request. To do so, click on the "Ready for
-review" button and add Joao (`@gante`) and Matt (`@Rocketknight1`) as reviewers. A model pull request will need
-at least 3 reviewers, but they will take care of finding appropriate additional reviewers for your model.
-
-After all reviewers are happy with the state of your PR, the final action point is to remove the `from_pt=True` flag in
-`.from_pretrained()` calls. Since there are no TensorFlow weights, you will have to add them! Check the section
-below for instructions on how to do it.
-
-Finally, when the TensorFlow weights get merged, you have at least 3 reviewer approvals, and all CI checks are
-green, double-check the tests locally one last time
-
-```bash
-NVIDIA_TF32_OVERRIDE=0 RUN_SLOW=1 RUN_PT_TF_CROSS_TESTS=1 \
-py.test -vv tests/models/brand_new_bert/test_modeling_tf_brand_new_bert.py
-```
-
-and we will merge your PR! Congratulations on the milestone 🎉
-
-**7. (Optional) Build demos and share with the world**
-
-One of the hardest parts about open-source is discovery. How can the other users learn about the existence of your
-fabulous TensorFlow contribution? With proper communication, of course! 📣
-
-There are two main ways to share your model with the community:
-- Build demos. These include Gradio demos, notebooks, and other fun ways to show off your model. We highly
-   encourage you to add a notebook to our [community-driven demos](https://huggingface.co/docs/transformers/community).
-- Share stories on social media like Twitter and LinkedIn. You should be proud of your work and share
-   your achievement with the community - your model can now be used by thousands of engineers and researchers around
-   the world 🌍! We will be happy to retweet your posts and help you share your work with the community.
-
-
-## Adding TensorFlow weights to 🤗 Hub
-
-Assuming that the TensorFlow model architecture is available in 🤗 Transformers, converting PyTorch weights into
-TensorFlow weights is a breeze!
-
-Here's how to do it:
-1. Make sure you are logged into your Hugging Face account in your terminal. You can log in using the command
-   `huggingface-cli login` (you can find your access tokens [here](https://huggingface.co/settings/tokens))
-2. Run `transformers-cli pt-to-tf --model-name foo/bar`, where `foo/bar` is the name of the model repository
-   containing the PyTorch weights you want to convert
-3. Tag `@joaogante` and `@Rocketknight1` in the 🤗 Hub PR the command above has just created
-
-That's it! 🎉
-
-
-## Debugging mismatches across ML frameworks 🐛
-
-At some point, when adding a new architecture or when creating TensorFlow weights for an existing architecture, you
-might come across errors compaining about mismatches between PyTorch and TensorFlow. You might even decide to open the
-model architecture code for the two frameworks, and find that they look identical. What's going on? 🤔
-
-First of all, let's talk about why understanding these mismatches matters. Many community members will use 🤗
-Transformers models out of the box, and trust that our models behave as expected. When there is a large mismatch
-between the two frameworks, it implies that the model is not following the reference implementation for at least one
-of the frameworks. This might lead to silent failures, in which the model runs but has poor performance. This is
-arguably worse than a model that fails to run at all! To that end, we aim at having a framework mismatch smaller than
-`1e-5` at all stages of the model.
-
-As in other numerical problems, the devil is in the details. And as in any detail-oriented craft, the secret
-ingredient here is patience. Here is our suggested workflow for when you come across this type of issues:
-1. Locate the source of mismatches. The model you're converting probably has near identical inner variables up to a
-   certain point. Place `breakpoint()` statements in the two frameworks' architectures, and compare the values of the
-   numerical variables in a top-down fashion until you find the source of the problems.
-2. Now that you've pinpointed the source of the issue, get in touch with the 🤗 Transformers team. It is possible
-   that we've seen a similar problem before and can promptly provide a solution. As a fallback, scan popular pages
-   like StackOverflow and GitHub issues.
-3. If there is no solution in sight, it means you'll have to go deeper. The good news is that you've located the
-   issue, so you can focus on the problematic instruction, abstracting away the rest of the model! The bad news is
-   that you'll have to venture into the source implementation of said instruction. In some cases, you might find an
-   issue with a reference implementation - don't abstain from opening an issue in the upstream repository.
-
-In some cases, in dicussion with the 🤗 Transformers team, we might find that the fixing the mismatch is infeasible.
-When the mismatch is very small in the output layers of the model (but potentially large in the hidden states), we
-might decide to ignore it in favor of distributing the model. The `pt-to-tf` CLI mentioned above has a `--max-error`
-flag to override the error message at weight conversion time.

docs/source/en/converting_tensorflow_models.mdx

@@ -10,7 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Converting From Tensorflow Checkpoints
+# Converting Tensorflow Checkpoints
 
 A command-line interface is provided to convert original Bert/GPT/GPT-2/Transformer-XL/XLNet/XLM checkpoints to models
 that can be loaded using the `from_pretrained` methods of the library.

docs/source/en/debugging.mdx

@@ -77,7 +77,7 @@ to the normal command line arguments, or pass `debug="underflow_overflow"` when
 If you're using your own training loop or another Trainer you can accomplish the same with:
 
 ```python
-from transformers.debug_utils import DebugUnderflowOverflow
+from .debug_utils import DebugUnderflowOverflow
 
 debug_overflow = DebugUnderflowOverflow(model)
 ```
@@ -271,7 +271,7 @@ Additionally, if you're instantiating the debugger in your own code, you can adj
 its default, e.g.:
 
 ```python
-from transformers.debug_utils import DebugUnderflowOverflow
+from .debug_utils import DebugUnderflowOverflow
 
 debug_overflow = DebugUnderflowOverflow(model, max_frames_to_save=100)
 ```

docs/source/en/hpo_train.mdx

@@ -1,120 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
--->
-
-# Hyperparameter Search using Trainer API
-
-🤗 Transformers provides a [`Trainer`] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [`Trainer`] provides API for hyperparameter search. This doc shows how to enable it in example. 
-
-## Hyperparameter Search backend
-
-[`Trainer`] supports four hyperparameter search backends currently:
-[optuna](https://optuna.org/), [sigopt](https://sigopt.com/), [raytune](https://docs.ray.io/en/latest/tune/index.html) and [wandb](https://wandb.ai/site/sweeps).
-
-you should install them before using them as the hyperparameter search backend
-```bash
-pip install optuna/sigopt/wandb/ray[tune] 
-```
-
-## How to enable Hyperparameter search in example
-
-Define the hyperparameter search space, different backends need different format.
-
-For sigopt, see sigopt [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter), it's like following:
-```py
->>> def sigopt_hp_space(trial):
-...     return [
-...         {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
-...         {
-...             "categorical_values": ["16", "32", "64", "128"],
-...             "name": "per_device_train_batch_size",
-...             "type": "categorical",
-...         },
-...     ]
-```
-
-For optuna, see optuna [object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py), it's like following:
-
-```py
->>> def optuna_hp_space(trial):
-...     return {
-...         "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
-...         "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]),
-...     }
-```
-
-For raytune, see raytune [object_parameter](https://docs.ray.io/en/latest/tune/api_docs/search_space.html), it's like following:
-
-```py
->>> def ray_hp_space(trial):
-...     return {
-...         "learning_rate": tune.loguniform(1e-6, 1e-4),
-...         "per_device_train_batch_size": tune.choice([16, 32, 64, 128]),
-...     }
-```
-
-For wandb, see wandb [object_parameter](https://docs.wandb.ai/guides/sweeps/configuration), it's like following:
-
-```py
->>> def wandb_hp_space(trial):
-...     return {
-...         "method": "random",
-...         "metric": {"name": "objective", "goal": "minimize"},
-...         "parameters": {
-...             "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
-...             "per_device_train_batch_size": {"values": [16, 32, 64, 128]},
-...         },
-...     }
-```
-
-Define a `model_init` function and pass it to the [`Trainer`], as an example:
-```py
->>> def model_init(trial):
-...     return AutoModelForSequenceClassification.from_pretrained(
-...         model_args.model_name_or_path,
-...         from_tf=bool(".ckpt" in model_args.model_name_or_path),
-...         config=config,
-...         cache_dir=model_args.cache_dir,
-...         revision=model_args.model_revision,
-...         use_auth_token=True if model_args.use_auth_token else None,
-...     )
-```
-
-Create a [`Trainer`] with your `model_init` function, training arguments, training and test datasets, and evaluation function:
-
-```py
->>> trainer = Trainer(
-...     model=None,
-...     args=training_args,
-...     train_dataset=small_train_dataset,
-...     eval_dataset=small_eval_dataset,
-...     compute_metrics=compute_metrics,
-...     tokenizer=tokenizer,
-...     model_init=model_init,
-...     data_collator=data_collator,
-... )
-```
-
-Call hyperparameter search, get the best trial parameters, backend could be `"optuna"`/`"sigopt"`/`"wandb"`/`"ray"`. direction can be`"minimize"` or `"maximize"`, which indicates whether to optimize greater or lower objective.
-
-You could define your own compute_objective function, if not defined, the default compute_objective will be called, and the sum of eval metric like f1 is returned as objective value.
-
-```py
->>> best_trial = trainer.hyperparameter_search(
-...     direction="maximize",
-...     backend="optuna",
-...     hp_space=optuna_hp_space,
-...     n_trials=20,
-...     compute_objective=compute_objective,
-... )
-```
-
-## Hyperparameter search For DDP finetune
-Currently, Hyperparameter search for DDP is enabled for optuna and sigopt. Only the rank-zero process will generate the search trial and pass the argument to other ranks.

docs/source/en/index.mdx

@@ -68,7 +68,6 @@ The documentation is organized into five sections:
 1. **[CANINE](model_doc/canine)** (from Google Research) released with the paper [CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://arxiv.org/abs/2103.06874) by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
 1. **[CLIP](model_doc/clip)** (from OpenAI) released with the paper [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020) by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
 1. **[CodeGen](model_doc/codegen)** (from Salesforce) released with the paper [A Conversational Paradigm for Program Synthesis](https://arxiv.org/abs/2203.13474) by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong.
-1. **[Conditional DETR](model_doc/conditional_detr)** (from Microsoft Research Asia) released with the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang.
 1. **[ConvBERT](model_doc/convbert)** (from YituTech) released with the paper [ConvBERT: Improving BERT with Span-based Dynamic Convolution](https://arxiv.org/abs/2008.02496) by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
 1. **[ConvNeXT](model_doc/convnext)** (from Facebook AI) released with the paper [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545) by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
 1. **[CPM](model_doc/cpm)** (from Tsinghua University) released with the paper [CPM: A Large-scale Generative Chinese Pre-trained Language Model](https://arxiv.org/abs/2012.00413) by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
@@ -78,7 +77,6 @@ The documentation is organized into five sections:
 1. **[DeBERTa](model_doc/deberta)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[DeBERTa-v2](model_doc/deberta-v2)** (from Microsoft) released with the paper [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://arxiv.org/abs/2006.03654) by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
 1. **[Decision Transformer](model_doc/decision_transformer)** (from Berkeley/Facebook/Google) released with the paper [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://arxiv.org/abs/2106.01345) by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
-1. **[Deformable DETR](model_doc/deformable_detr)** (from SenseTime Research) released with the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
 1. **[DeiT](model_doc/deit)** (from Facebook) released with the paper [Training data-efficient image transformers & distillation through attention](https://arxiv.org/abs/2012.12877) by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
 1. **[DETR](model_doc/detr)** (from Facebook) released with the paper [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872) by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
 1. **[DialoGPT](model_doc/dialogpt)** (from Microsoft Research) released with the paper [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://arxiv.org/abs/1911.00536) by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
@@ -90,7 +88,6 @@ The documentation is organized into five sections:
 1. **[ELECTRA](model_doc/electra)** (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
 1. **[EncoderDecoder](model_doc/encoder-decoder)** (from Google Research) released with the paper [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
 1. **[ERNIE](model_doc/ernie)** (from Baidu) released with the paper [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) by Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu.
-1. **[ESM](model_doc/esm)** (from Meta AI) are transformer protein language models.  **ESM-1b** was released with the paper [Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus. **ESM-1v** was released with the paper [Language models enable zero-shot prediction of the effects of mutations on protein function](https://doi.org/10.1101/2021.07.09.450648) by Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu and Alexander Rives. **ESM-2** was released with the paper [Language models of protein sequences at the scale of evolution enable accurate structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, Alexander Rives.
 1. **[FlauBERT](model_doc/flaubert)** (from CNRS) released with the paper [FlauBERT: Unsupervised Language Model Pre-training for French](https://arxiv.org/abs/1912.05372) by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
 1. **[FLAVA](model_doc/flava)** (from Facebook AI) released with the paper [FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482) by Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela.
 1. **[FNet](model_doc/fnet)** (from Google Research) released with the paper [FNet: Mixing Tokens with Fourier Transforms](https://arxiv.org/abs/2105.03824) by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
@@ -99,7 +96,6 @@ The documentation is organized into five sections:
 1. **[GPT](model_doc/openai-gpt)** (from OpenAI) released with the paper [Improving Language Understanding by Generative Pre-Training](https://blog.openai.com/language-unsupervised/) by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
 1. **[GPT Neo](model_doc/gpt_neo)** (from EleutherAI) released in the repository [EleutherAI/gpt-neo](https://github.com/EleutherAI/gpt-neo) by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
 1. **[GPT NeoX](model_doc/gpt_neox)** (from EleutherAI) released with the paper [GPT-NeoX-20B: An Open-Source Autoregressive Language Model](https://arxiv.org/abs/2204.06745) by Sid Black, Stella Biderman, Eric Hallahan, Quentin Anthony, Leo Gao, Laurence Golding, Horace He, Connor Leahy, Kyle McDonell, Jason Phang, Michael Pieler, USVSN Sai Prashanth, Shivanshu Purohit, Laria Reynolds, Jonathan Tow, Ben Wang, Samuel Weinbach
-1. **[GPT NeoX Japanese](model_doc/gpt_neox_japanese)** (from ABEJA) released by Shinya Otani, Takayoshi Makabe, Anuj Arora, and Kyo Hattori.
 1. **[GPT-2](model_doc/gpt2)** (from OpenAI) released with the paper [Language Models are Unsupervised Multitask Learners](https://blog.openai.com/better-language-models/) by Alec Radford*, Jeffrey Wu*, Rewon Child, David Luan, Dario Amodei** and Ilya Sutskever**.
 1. **[GPT-J](model_doc/gptj)** (from EleutherAI) released in the repository [kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax/) by Ben Wang and Aran Komatsuzaki.
 1. **[GroupViT](model_doc/groupvit)** (from UCSD, NVIDIA) released with the paper [GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094) by Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, Xiaolong Wang.
@@ -112,7 +108,6 @@ The documentation is organized into five sections:
 1. **[LayoutXLM](model_doc/layoutxlm)** (from Microsoft Research Asia) released with the paper [LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding](https://arxiv.org/abs/2104.08836) by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
 1. **[LED](model_doc/led)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LeViT](model_doc/levit)** (from Meta AI) released with the paper [LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference](https://arxiv.org/abs/2104.01136) by Ben Graham, Alaaeldin El-Nouby, Hugo Touvron, Pierre Stock, Armand Joulin, Hervé Jégou, Matthijs Douze.
-1. **[LiLT](model_doc/lilt)** (from South China University of Technology) released with the paper [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
 1. **[Longformer](model_doc/longformer)** (from AllenAI) released with the paper [Longformer: The Long-Document Transformer](https://arxiv.org/abs/2004.05150) by Iz Beltagy, Matthew E. Peters, Arman Cohan.
 1. **[LongT5](model_doc/longt5)** (from Google AI) released with the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/abs/2112.07916) by Mandy Guo, Joshua Ainslie, David Uthus, Santiago Ontanon, Jianmo Ni, Yun-Hsuan Sung, Yinfei Yang.
 1. **[LUKE](model_doc/luke)** (from Studio Ousia) released with the paper [LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention](https://arxiv.org/abs/2010.01057) by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
@@ -120,7 +115,6 @@ The documentation is organized into five sections:
 1. **[M-CTC-T](model_doc/mctct)** (from Facebook) released with the paper [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert.
 1. **[M2M100](model_doc/m2m_100)** (from Facebook) released with the paper [Beyond English-Centric Multilingual Machine Translation](https://arxiv.org/abs/2010.11125) by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
 1. **[MarianMT](model_doc/marian)** Machine translation models trained using [OPUS](http://opus.nlpl.eu/) data by Jörg Tiedemann. The [Marian Framework](https://marian-nmt.github.io/) is being developed by the Microsoft Translator Team.
-1. **[MarkupLM](model_doc/markuplm)** (from Microsoft Research Asia) released with the paper [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei.
 1. **[MaskFormer](model_doc/maskformer)** (from Meta and UIUC) released with the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
 1. **[mBART](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Denoising Pre-training for Neural Machine Translation](https://arxiv.org/abs/2001.08210) by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
 1. **[mBART-50](model_doc/mbart)** (from Facebook) released with the paper [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://arxiv.org/abs/2008.00401) by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
@@ -166,7 +160,6 @@ The documentation is organized into five sections:
 1. **[T5v1.1](model_doc/t5v1.1)** (from Google AI) released in the repository [google-research/text-to-text-transfer-transformer](https://github.com/google-research/text-to-text-transfer-transformer/blob/main/released_checkpoints.md#t511) by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
 1. **[TAPAS](model_doc/tapas)** (from Google AI) released with the paper [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://arxiv.org/abs/2004.02349) by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
 1. **[TAPEX](model_doc/tapex)** (from Microsoft Research) released with the paper [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
-1. **[Time Series Transformer](model_doc/time_series_transformer)**  (from HuggingFace).
 1. **[Trajectory Transformer](model_doc/trajectory_transformers)** (from the University of California at Berkeley) released with the paper [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039) by Michael Janner, Qiyang Li, Sergey Levine
 1. **[Transformer-XL](model_doc/transfo-xl)** (from Google/CMU) released with the paper [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://arxiv.org/abs/1901.02860) by Zihang Dai*, Zhilin Yang*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
 1. **[TrOCR](model_doc/trocr)** (from Microsoft), released together with the paper [TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models](https://arxiv.org/abs/2109.10282) by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
@@ -179,12 +172,10 @@ The documentation is organized into five sections:
 1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
 1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
 1. **[ViTMAE](model_doc/vit_mae)** (from Meta AI) released with the paper [Masked Autoencoders Are Scalable Vision Learners](https://arxiv.org/abs/2111.06377) by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
-1. **[ViTMSN](model_doc/vit_msn)** (from Meta AI) released with the paper [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas.
 1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
 1. **[Wav2Vec2-Conformer](model_doc/wav2vec2-conformer)** (from Facebook AI) released with the paper [FAIRSEQ S2T: Fast Speech-to-Text Modeling with FAIRSEQ](https://arxiv.org/abs/2010.05171) by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Sravya Popuri, Dmytro Okhonko, Juan Pino.
 1. **[Wav2Vec2Phoneme](model_doc/wav2vec2_phoneme)** (from Facebook AI) released with the paper [Simple and Effective Zero-shot Cross-lingual Phoneme Recognition](https://arxiv.org/abs/2109.11680) by Qiantong Xu, Alexei Baevski, Michael Auli.
 1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
-1. **[Whisper](model_doc/whisper)** (from OpenAI) released with the paper [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
 1. **[X-CLIP](model_doc/xclip)** (from Microsoft Research) released with the paper [Expanding Language-Image Pretrained Models for General Video Recognition](https://arxiv.org/abs/2208.02816) by Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, Haibin Ling.
 1. **[XGLM](model_doc/xglm)** (From Facebook AI) released with the paper [Few-shot Learning with Multilingual Language Models](https://arxiv.org/abs/2112.10668) by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O'Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
 1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
@@ -222,18 +213,16 @@ Flax), PyTorch, and/or TensorFlow.
 |           CANINE            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            CLIP             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |           CodeGen           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|      Conditional DETR       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |          ConvNeXT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|             CvT             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             CvT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        Data2VecAudio        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        Data2VecText         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |       Data2VecVision        |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |         DeBERTa-v2          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |    Decision Transformer     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|       Deformable DETR       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            DeiT             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            DETR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         DistilBERT          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
@@ -243,7 +232,6 @@ Flax), PyTorch, and/or TensorFlow.
 |           ELECTRA           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |       Encoder decoder       |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |            ERNIE            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|             ESM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 | FairSeq Machine-Translation |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          FlauBERT           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            FLAVA            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -252,9 +240,8 @@ Flax), PyTorch, and/or TensorFlow.
 |            GLPN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           GPT Neo           |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |          GPT NeoX           |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
-|      GPT NeoX Japanese      |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            GPT-J            |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
-|          GroupViT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|          GroupViT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           Hubert            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           I-BERT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          ImageGPT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -263,7 +250,6 @@ Flax), PyTorch, and/or TensorFlow.
 |         LayoutLMv3          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |             LED             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |            LeViT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|            LiLT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |         Longformer          |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           LongT5            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |            LUKE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -271,7 +257,6 @@ Flax), PyTorch, and/or TensorFlow.
 |           M-CTC-T           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           M2M100            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           Marian            |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
-|          MarkupLM           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |         MaskFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            mBART            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |        Megatron-BERT        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -314,7 +299,6 @@ Flax), PyTorch, and/or TensorFlow.
 |     Swin Transformer V2     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             T5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |            TAPAS            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
-|   Time Series Transformer   |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |   Trajectory Transformer    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |       Transformer-XL        |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            TrOCR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -328,11 +312,9 @@ Flax), PyTorch, and/or TensorFlow.
 |         VisualBERT          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             ViT             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |           ViTMAE            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
-|           ViTMSN            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          Wav2Vec2           |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
 |     Wav2Vec2-Conformer      |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            WavLM            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|           Whisper           |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           X-CLIP            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            XGLM             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             XLM             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |

docs/source/en/internal/image_processing_utils.mdx

@@ -1,32 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Utilities for Image Processors
-
-This page lists all the utility functions used by the image processors, mainly the functional
-transformations used to process the images.
-
-Most of those are only useful if you are studying the code of the image processors in the library.
-
-## Image Transformations
-
-[[autodoc]] image_transforms.normalize
-
-[[autodoc]] image_transforms.rescale
-
-[[autodoc]] image_transforms.resize
-
-[[autodoc]] image_transforms.to_pil_image
-
-## ImageProcessorMixin
-
-[[autodoc]] image_processing_utils.ImageProcessorMixin

docs/source/en/main_classes/deepspeed.mdx

@@ -49,7 +49,7 @@ Inference:
 
 1. DeepSpeed ZeRO Inference supports ZeRO stage 3 with ZeRO-Infinity. It uses the same ZeRO protocol as training, but
    it doesn't use an optimizer and a lr scheduler and only stage 3 is relevant. For more details see:
-   [zero-inference](#zero-inference).
+   [deepspeed-zero-inference](#deepspeed-zero-inference).
 
 There is also DeepSpeed Inference - this is a totally different technology which uses Tensor Parallelism instead of
 ZeRO (coming soon).
@@ -81,7 +81,7 @@ pip install transformers[deepspeed]
 or find more details on [the DeepSpeed's GitHub page](https://github.com/microsoft/deepspeed#installation) and
 [advanced install](https://www.deepspeed.ai/tutorials/advanced-install/).
 
-If you're still struggling with the build, first make sure to read [CUDA Extension Installation Notes](trainer#cuda-extension-installation-notes).
+If you're still struggling with the build, first make sure to read [zero-install-notes](#zero-install-notes).
 
 If you don't prebuild the extensions and rely on them to be built at run time and you tried all of the above solutions
 to no avail, the next thing to try is to pre-build the modules before installing them.

docs/source/en/main_classes/pipelines.mdx

@@ -25,7 +25,6 @@ There are two categories of pipeline abstractions to be aware about:
   - [`AudioClassificationPipeline`]
   - [`AutomaticSpeechRecognitionPipeline`]
   - [`ConversationalPipeline`]
-  - [`DepthEstimationPipeline`]
   - [`DocumentQuestionAnsweringPipeline`]
   - [`FeatureExtractionPipeline`]
   - [`FillMaskPipeline`]
@@ -44,7 +43,6 @@ There are two categories of pipeline abstractions to be aware about:
   - [`VisualQuestionAnsweringPipeline`]
   - [`ZeroShotClassificationPipeline`]
   - [`ZeroShotImageClassificationPipeline`]
-  - [`ZeroShotObjectDetectionPipeline`]
 
 ## The pipeline abstraction
 
@@ -345,16 +343,12 @@ That should enable you to do all the custom code you want.
     - __call__
     - all
 
-### DepthEstimationPipeline
-[[autodoc]] DepthEstimationPipeline
-    - __call__
-    - all 
-
 ### DocumentQuestionAnsweringPipeline
 
 [[autodoc]] DocumentQuestionAnsweringPipeline
     - __call__
     - all
+
 ### FeatureExtractionPipeline
 
 [[autodoc]] FeatureExtractionPipeline
@@ -462,12 +456,6 @@ See [`TokenClassificationPipeline`] for all details.
     - __call__
     - all
 
-### ZeroShotObjectDetectionPipeline
-
-[[autodoc]] ZeroShotObjectDetectionPipeline
-    - __call__
-    - all
-
 ## Parent class: `Pipeline`
 
 [[autodoc]] Pipeline

docs/source/en/model_doc/auto.mdx

@@ -82,10 +82,6 @@ Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
 
 [[autodoc]] AutoModelForCausalLM
 
-## AutoModelForDepthEstimation
-
-[[autodoc]] AutoModelForDepthEstimation
-
 ## AutoModelForMaskedLM
 
 [[autodoc]] AutoModelForMaskedLM
@@ -178,10 +174,6 @@ Likewise, if your `NewModel` is a subclass of [`PreTrainedModel`], make sure its
 
 [[autodoc]] AutoModelForInstanceSegmentation
 
-## AutoModelForZeroShotObjectDetection
-
-[[autodoc]] AutoModelForZeroShotObjectDetection
-
 ## TFAutoModel
 
 [[autodoc]] TFAutoModel

docs/source/en/model_doc/beit.mdx

@@ -59,11 +59,6 @@ Tips:
   `use_relative_position_bias` attribute of [`BeitConfig`] to `True` in order to add
   position embeddings.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/beit_architecture.jpg"
-alt="drawing" width="600"/> 
-
-<small> BEiT pre-training. Taken from the <a href="https://arxiv.org/abs/2106.08254">original paper.</a> </small>
-
 This model was contributed by [nielsr](https://huggingface.co/nielsr). The JAX/FLAX version of this model was
 contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/beit).
 
@@ -82,7 +77,6 @@ contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code
 
 [[autodoc]] BeitFeatureExtractor
     - __call__
-    - post_process_semantic_segmentation
 
 ## BeitModel
 

docs/source/en/model_doc/bloom.mdx

@@ -55,8 +55,3 @@ Several smaller versions of the models have been trained on the same dataset. BL
 
 [[autodoc]] BloomForTokenClassification
     - forward
-
-## BloomForQuestionAnswering
-
-[[autodoc]] BloomForQuestionAnswering
-    - forward

docs/source/en/model_doc/conditional_detr.mdx

@@ -1,57 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Conditional DETR
-
-## Overview
-
-The Conditional DETR model was proposed in [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Depu Meng, Xiaokang Chen, Zejia Fan, Gang Zeng, Houqiang Li, Yuhui Yuan, Lei Sun, Jingdong Wang. Conditional DETR presents a conditional cross-attention mechanism for fast DETR training. Conditional DETR converges 6.7× to 10× faster than DETR.
-
-The abstract from the paper is the following:
-
-*The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. Code is available at https://github.com/Atten4Vis/ConditionalDETR.*
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg"
-alt="drawing" width="600"/> 
-
-<small> Conditional DETR shows much faster convergence compared to the original DETR. Taken from the <a href="https://arxiv.org/abs/2108.06152">original paper</a>.</small>
-
-This model was contributed by [DepuMeng](https://huggingface.co/DepuMeng). The original code can be found [here](https://github.com/Atten4Vis/ConditionalDETR).
-
-
-## ConditionalDetrConfig
-
-[[autodoc]] ConditionalDetrConfig
-
-## ConditionalDetrFeatureExtractor
-
-[[autodoc]] ConditionalDetrFeatureExtractor
-    - __call__
-    - pad_and_create_pixel_mask
-    - post_process
-    - post_process_segmentation
-    - post_process_panoptic
-
-## ConditionalDetrModel
-
-[[autodoc]] ConditionalDetrModel
-    - forward
-
-## ConditionalDetrForObjectDetection
-
-[[autodoc]] ConditionalDetrForObjectDetection
-    - forward
-
-## ConditionalDetrForSegmentation
-
-[[autodoc]] ConditionalDetrForSegmentation
-    - forward

docs/source/en/model_doc/cvt.mdx

@@ -51,14 +51,3 @@ This model was contributed by [anugunj](https://huggingface.co/anugunj). The ori
 
 [[autodoc]] CvtForImageClassification
     - forward
-
-## TFCvtModel
-
-[[autodoc]] TFCvtModel
-    - call
-
-## TFCvtForImageClassification
-
-[[autodoc]] TFCvtForImageClassification
-    - call
-

docs/source/en/model_doc/deformable_detr.mdx

@@ -1,60 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Deformable DETR
-
-## Overview
-
-The Deformable DETR model was proposed in [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, Jifeng Dai.
-Deformable DETR mitigates the slow convergence issues and limited feature spatial resolution of the original [DETR](detr) by leveraging a new deformable attention module which only attends to a small set of key sampling points around a reference.
-
-The abstract from the paper is the following:
-
-*DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach.*
-
-Tips:
-
-- One can use the [`AutoFeatureExtractor`] API to prepare images (and optional targets) for the model. This will instantiate a [`DetrFeatureExtractor`] behind the scenes.
-- Training Deformable DETR is equivalent to training the original [DETR](detr) model. Demo notebooks can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/DETR).
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png"
-alt="drawing" width="600"/> 
-
-<small> Deformable DETR architecture. Taken from the <a href="https://arxiv.org/abs/2010.04159">original paper</a>.</small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/fundamentalvision/Deformable-DETR).
-
-## DeformableDetrFeatureExtractor
-
-[[autodoc]] DeformableDetrFeatureExtractor
-    - __call__
-    - pad_and_create_pixel_mask
-    - post_process
-    - post_process_segmentation
-    - post_process_panoptic
-
-
-## DeformableDetrConfig
-
-[[autodoc]] DeformableDetrConfig
-
-
-## DeformableDetrModel
-
-[[autodoc]] DeformableDetrModel
-    - forward
-
-
-## DeformableDetrForObjectDetection
-
-[[autodoc]] DeformableDetrForObjectDetection
-    - forward

docs/source/en/model_doc/detr.mdx

@@ -171,10 +171,9 @@ mean Average Precision (mAP) and Panoptic Quality (PQ). The latter objects are i
 [[autodoc]] DetrFeatureExtractor
     - __call__
     - pad_and_create_pixel_mask
-    - post_process_object_detection
-    - post_process_semantic_segmentation
-    - post_process_instance_segmentation
-    - post_process_panoptic_segmentation
+    - post_process
+    - post_process_segmentation
+    - post_process_panoptic
 
 ## DetrModel
 

docs/source/en/model_doc/dpt.mdx

@@ -37,7 +37,6 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 
 [[autodoc]] DPTFeatureExtractor
     - __call__
-    - post_process_semantic_segmentation
 
 
 ## DPTModel

docs/source/en/model_doc/esm.mdx

@@ -1,129 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# ESM
-
-## Overview
-This page provides code and pre-trained weights for Transformer protein language models from Meta AI's Fundamental 
-AI Research Team, providing the state-of-the-art ESM-2, and the previously released ESM-1b and ESM-1v. Transformer 
-protein language models were introduced in the paper [Biological structure and function emerge from scaling 
-unsupervised learning to 250 million protein sequences](https://www.pnas.org/content/118/15/e2016239118) by 
-Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, 
-C. Lawrence Zitnick, Jerry Ma, and Rob Fergus.
-The first version of this paper was [preprinted in 2019](https://www.biorxiv.org/content/10.1101/622803v1?versioned=true).
-
-ESM-2 outperforms all tested single-sequence protein language models across a range of structure prediction tasks,
-and enables atomic resolution structure prediction.
-It was released with the paper [Language models of protein sequences at the scale of evolution enable accurate
-structure prediction](https://doi.org/10.1101/2022.07.20.500902) by Zeming Lin, Halil Akin, Roshan Rao, Brian Hie,
-Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido and Alexander Rives.
-
-
-The abstract from 
-"Biological structure and function emerge from scaling unsupervised learning to 250 
-million protein sequences" is
-
-
-*In the field of artificial intelligence, a combination of scale in data and model capacity enabled by unsupervised
-learning has led to major advances in representation learning and statistical generation. In the life sciences, the
-anticipated growth of sequencing promises unprecedented data on natural sequence diversity. Protein language modeling
-at the scale of evolution is a logical step toward predictive and generative artificial intelligence for biology. To
-this end, we use unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250
-million protein sequences spanning evolutionary diversity. The resulting model contains information about biological
-properties in its representations. The representations are learned from sequence data alone. The learned representation
-space has a multiscale organization reflecting structure from the level of biochemical properties of amino acids to
-remote homology of proteins. Information about secondary and tertiary structure is encoded in the representations and
-can be identified by linear projections. Representation learning produces features that generalize across a range of
-applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and
-improving state-of-the-art features for long-range contact prediction.*
-
-
-The abstract from
-"Language models of protein sequences at the scale of evolution enable accurate structure prediction" is
-
-*Large language models have recently been shown to develop emergent capabilities with scale, going beyond
-simple pattern matching to perform higher level reasoning and generate lifelike images and text. While
-language models trained on protein sequences have been studied at a smaller scale, little is known about
-what they learn about biology as they are scaled up. In this work we train models up to 15 billion parameters,
-the largest language models of proteins to be evaluated to date. We find that as models are scaled they learn
-information enabling the prediction of the three-dimensional structure of a protein at the resolution of
-individual atoms. We present ESMFold for high accuracy end-to-end atomic level structure prediction directly
-from the individual sequence of a protein. ESMFold has similar accuracy to AlphaFold2 and RoseTTAFold for
-sequences with low perplexity that are well understood by the language model. ESMFold inference is an
-order of magnitude faster than AlphaFold2, enabling exploration of the structural space of metagenomic
-proteins in practical timescales.*
-
-
-
-
-Tips:
-
-- ESM models are trained with a masked language modeling (MLM) objective.
-
-The original code can be found [here](https://github.com/facebookresearch/esm) and was
-was developed by the Fundamental AI Research team at Meta AI.
-This model was contributed to huggingface by [jasonliu](https://huggingface.co/jasonliu) 
-and [Matt](https://huggingface.co/Rocketknight1).
-
-## EsmConfig
-
-[[autodoc]] EsmConfig
-    - all
-
-## EsmTokenizer
-
-[[autodoc]] EsmTokenizer
-    - build_inputs_with_special_tokens
-    - get_special_tokens_mask
-    - create_token_type_ids_from_sequences
-    - save_vocabulary
-
-
-## EsmModel
-
-[[autodoc]] EsmModel
-    - forward
-
-## EsmForMaskedLM
-
-[[autodoc]] EsmForMaskedLM
-    - forward
-
-## EsmForSequenceClassification
-
-[[autodoc]] EsmForSequenceClassification
-    - forward
-
-## EsmForTokenClassification
-
-[[autodoc]] EsmForTokenClassification
-    - forward
-
-## TFEsmModel
-
-[[autodoc]] TFEsmModel
-    - call
-
-## TFEsmForMaskedLM
-
-[[autodoc]] TFEsmForMaskedLM
-    - call
-
-## TFEsmForSequenceClassification
-
-[[autodoc]] TFEsmForSequenceClassification
-    - call
-
-## TFEsmForTokenClassification
-
-[[autodoc]] TFEsmForTokenClassification
-    - call

docs/source/en/model_doc/gpt_neox_japanese.mdx

@@ -1,66 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# GPT-NeoX-Japanese
-
-## Overview
-
-We introduce GPT-NeoX-Japanese, which is an autoregressive language model for Japanese, trained on top of [https://github.com/EleutherAI/gpt-neox](https://github.com/EleutherAI/gpt-neox).
-Japanese is a unique language with its large vocabulary and a combination of hiragana, katakana, and kanji writing scripts.
-To address this distinct structure of the Japanese language, we use a [special sub-word tokenizer](https://github.com/tanreinama/Japanese-BPEEncoder_V2). We are very grateful to *tanreinama* for open-sourcing this incredibly helpful tokenizer.
-Following the recommendations from Google's research on [PaLM](https://ai.googleblog.com/2022/04/pathways-language-model-palm-scaling-to.html), we have removed bias parameters from transformer blocks, achieving better model performance. Please refer [this article](https://medium.com/ml-abeja/training-a-better-gpt-2-93b157662ae4) in detail.
-
-Development of the model was led by [Shinya Otani](https://github.com/SO0529), [Takayoshi Makabe](https://github.com/spider-man-tm), [Anuj Arora](https://github.com/Anuj040), and [Kyo Hattori](https://github.com/go5paopao) from [ABEJA, Inc.](https://www.abejainc.com/). For more information on this model-building activity, please refer [here (ja)](https://tech-blog.abeja.asia/entry/abeja-gpt-project-202207).
-
-### Generation
-
-The `generate()` method can be used to generate text using GPT NeoX Japanese model.
-
-```python
->>> from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseTokenizer
-
->>> model = GPTNeoXJapaneseForCausalLM.from_pretrained("abeja/gpt-neox-japanese-2.7b")
->>> tokenizer = GPTNeoXJapaneseTokenizer.from_pretrained("abeja/gpt-neox-japanese-2.7b")
-
->>> prompt = "人とAIが協調するためには、"
-
->>> input_ids = tokenizer(prompt, return_tensors="pt").input_ids
-
->>> gen_tokens = model.generate(
-...     input_ids,
-...     do_sample=True,
-...     temperature=0.9,
-...     max_length=100,
-... )
->>> gen_text = tokenizer.batch_decode(gen_tokens, skip_special_tokens=True)[0]
-
->>> print(gen_text)
-人とAIが協調するためには、AIと人が共存し、AIを正しく理解する必要があります。
-```
-
-## GPTNeoXJapaneseConfig
-
-[[autodoc]] GPTNeoXJapaneseConfig
-
-## GPTNeoXJapaneseTokenizer
-
-[[autodoc]] GPTNeoXJapaneseTokenizer
-
-## GPTNeoXJapaneseModel
-
-[[autodoc]] GPTNeoXJapaneseModel
-    - forward
-
-## GPTNeoXJapaneseForCausalLM
-
-[[autodoc]] GPTNeoXJapaneseForCausalLM
-    - forward

docs/source/en/model_doc/groupvit.mdx

@@ -26,7 +26,7 @@ Tips:
 - You may specify `output_segmentation=True` in the forward of `GroupViTModel` to get the segmentation logits of input texts. 
 - The quickest way to get started with GroupViT is by checking the [example notebooks](https://github.com/xvjiarui/GroupViT/blob/main/demo/GroupViT_hf_inference_notebook.ipynb) (which showcase zero-shot segmentation inference). One can also check out the [HuggingFace Spaces demo](https://huggingface.co/spaces/xvjiarui/GroupViT) to play with GroupViT. 
 
-This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui). The TensorFlow version was contributed by [ariG23498](https://huggingface.co/ariG23498) with the help of [Yih-Dar SHIEH](https://huggingface.co/ydshieh), [Amy Roberts](https://huggingface.co/amyeroberts), and [Joao Gante](https://huggingface.co/joaogante).
+This model was contributed by [xvjiarui](https://huggingface.co/xvjiarui).
 The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
 
@@ -59,20 +59,3 @@ The original code can be found [here](https://github.com/NVlabs/GroupViT).
 
 [[autodoc]] GroupViTVisionModel
     - forward
-
-## TFGroupViTModel
-
-[[autodoc]] TFGroupViTModel
-    - call
-    - get_text_features
-    - get_image_features
-
-## TFGroupViTTextModel
-
-[[autodoc]] TFGroupViTTextModel
-    - call
-
-## TFGroupViTVisionModel
-
-[[autodoc]] TFGroupViTVisionModel
-    - call

docs/source/en/model_doc/lilt.mdx

@@ -1,73 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# LiLT
-
-## Overview
-
-The LiLT model was proposed in [LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding](https://arxiv.org/abs/2202.13669) by Jiapeng Wang, Lianwen Jin, Kai Ding.
-LiLT allows to combine any pre-trained RoBERTa text encoder with a lightweight Layout Transformer, to enable [LayoutLM](layoutlm)-like document understanding for many
-languages.
-
-The abstract from the paper is the following:
-
-*Structured document understanding has attracted considerable attention and made significant progress recently, owing to its crucial role in intelligent document processing. However, most existing related models can only deal with the document data of specific language(s) (typically English) included in the pre-training collection, which is extremely limited. To address this issue, we propose a simple yet effective Language-independent Layout Transformer (LiLT) for structured document understanding. LiLT can be pre-trained on the structured documents of a single language and then directly fine-tuned on other languages with the corresponding off-the-shelf monolingual/multilingual pre-trained textual models. Experimental results on eight languages have shown that LiLT can achieve competitive or even superior performance on diverse widely-used downstream benchmarks, which enables language-independent benefit from the pre-training of document layout structure.*
-
-Tips:
-
-- To combine the Language-Independent Layout Transformer with a new RoBERTa checkpoint from the [hub](https://huggingface.co/models?search=roberta), refer to [this guide](https://github.com/jpWang/LiLT#or-generate-your-own-checkpoint-optional).
-The script will result in `config.json` and `pytorch_model.bin` files being stored locally. After doing this, one can do the following (assuming you're logged in with your HuggingFace account):
-
-```
-from transformers import LiltModel
-
-model = LiltModel.from_pretrained("path_to_your_files")
-model.push_to_hub("name_of_repo_on_the_hub")
-```
-
-- When preparing data for the model, make sure to use the token vocabulary that corresponds to the RoBERTa checkpoint you combined with the Layout Transformer.
-- As (lilt-roberta-en-base)[https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base] uses the same vocabulary as [LayoutLMv3](layoutlmv3), one can use [`LayoutLMv3TokenizerFast`] to prepare data for the model.
-The same is true for (lilt-roberta-en-base)[https://huggingface.co/SCUT-DLVCLab/lilt-infoxlm-base]: one can use [`LayoutXLMTokenizerFast`] for that model.
-- Demo notebooks for LiLT can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/LiLT).
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/lilt_architecture.jpg"
-alt="drawing" width="600"/>
-
-<small> LiLT architecture. Taken from the <a href="https://arxiv.org/abs/2202.13669">original paper</a>. </small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr).
-The original code can be found [here](https://github.com/jpwang/lilt).
-
-
-## LiltConfig
-
-[[autodoc]] LiltConfig
-
-## LiltModel
-
-[[autodoc]] LiltModel
-    - forward
-
-## LiltForSequenceClassification
-
-[[autodoc]] LiltForSequenceClassification
-    - forward
-
-## LiltForTokenClassification
-
-[[autodoc]] LiltForTokenClassification
-    - forward
-
-## LiltForQuestionAnswering
-
-[[autodoc]] LiltForQuestionAnswering
-    - forward

docs/source/en/model_doc/m2m_100.mdx

@@ -57,7 +57,7 @@ tgt_text = "La vie est comme une boîte de chocolat."
 
 model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
 
-loss = model(**model_inputs).loss  # forward pass
+loss = model(**model_inputs, labels=labels)  # forward pass
 ```
 
 - Generation

docs/source/en/model_doc/markuplm.mdx

@@ -1,246 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# MarkupLM
-
-## Overview
-
-The MarkupLM model was proposed in [MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document
-Understanding](https://arxiv.org/abs/2110.08518) by Junlong Li, Yiheng Xu, Lei Cui, Furu Wei. MarkupLM is BERT, but
-applied to HTML pages instead of raw text documents. The model incorporates additional embedding layers to improve
-performance, similar to [LayoutLM](layoutlm).
-
-The model can be used for tasks like question answering on web pages or information extraction from web pages. It obtains
-state-of-the-art results on 2 important benchmarks:
-- [WebSRC](https://x-lance.github.io/WebSRC/), a dataset for Web-Based Structual Reading Comprehension (a bit like SQuAD but for web pages)
-- [SWDE](https://www.researchgate.net/publication/221299838_From_one_tree_to_a_forest_a_unified_solution_for_structured_web_data_extraction), a dataset
-for information extraction from web pages (basically named-entity recogntion on web pages)
-
-The abstract from the paper is the following:
-
-*Multimodal pre-training with text, layout, and image has made significant progress for Visually-rich Document
-Understanding (VrDU), especially the fixed-layout documents such as scanned document images. While, there are still a
-large number of digital documents where the layout information is not fixed and needs to be interactively and
-dynamically rendered for visualization, making existing layout-based pre-training approaches not easy to apply. In this
-paper, we propose MarkupLM for document understanding tasks with markup languages as the backbone such as
-HTML/XML-based documents, where text and markup information is jointly pre-trained. Experiment results show that the
-pre-trained MarkupLM significantly outperforms the existing strong baseline models on several document understanding
-tasks. The pre-trained model and code will be publicly available.*
-
-Tips:
-- In addition to `input_ids`, [`~MarkupLMModel.forward`] expects 2 additional inputs, namely `xpath_tags_seq` and `xpath_subs_seq`.
-These are the XPATH tags and subscripts respectively for each token in the input sequence.
-- One can use [`MarkupLMProcessor`] to prepare all data for the model. Refer to the [usage guide](#usage-markuplmprocessor) for more info.
-- Demo notebooks can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/MarkupLM).
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/markuplm_architecture.jpg"
-alt="drawing" width="600"/> 
-
-<small> MarkupLM architecture. Taken from the <a href="https://arxiv.org/abs/2110.08518">original paper.</a> </small>
-
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/microsoft/unilm/tree/master/markuplm).
-
-## Usage: MarkupLMProcessor
-
-The easiest way to prepare data for the model is to use [`MarkupLMProcessor`], which internally combines a feature extractor
-([`MarkupLMFeatureExtractor`]) and a tokenizer ([`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`]). The feature extractor is
-used to extract all nodes and xpaths from the HTML strings, which are then provided to the tokenizer, which turns them into the
-token-level inputs of the model (`input_ids` etc.). Note that you can still use the feature extractor and tokenizer separately,
-if you only want to handle one of the two tasks.
-
-```python
-from transformers import MarkupLMFeatureExtractor, MarkupLMTokenizerFast, MarkupLMProcessor
-
-feature_extractor = MarkupLMFeatureExtractor()
-tokenizer = MarkupLMTokenizerFast.from_pretrained("microsoft/markuplm-base")
-processor = MarkupLMProcessor(feature_extractor, tokenizer)
-```
-
-In short, one can provide HTML strings (and possibly additional data) to [`MarkupLMProcessor`],
-and it will create the inputs expected by the model. Internally, the processor first uses
-[`MarkupLMFeatureExtractor`] to get a list of nodes and corresponding xpaths. The nodes and
-xpaths are then provided to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`], which converts them
-to token-level `input_ids`, `attention_mask`, `token_type_ids`, `xpath_subs_seq`, `xpath_tags_seq`.
-Optionally, one can provide node labels to the processor, which are turned into token-level `labels`.
-
-[`MarkupLMFeatureExtractor`] uses [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/bs4/doc/), a Python library for
-pulling data out of HTML and XML files, under the hood. Note that you can still use your own parsing solution of
-choice, and provide the nodes and xpaths yourself to [`MarkupLMTokenizer`] or [`MarkupLMTokenizerFast`].
-
-In total, there are 5 use cases that are supported by the processor. Below, we list them all. Note that each of these
-use cases work for both batched and non-batched inputs (we illustrate them for non-batched inputs).
-
-**Use case 1: web page classification (training, inference) + token classification (inference), parse_html = True**
-
-This is the simplest case, in which the processor will use the feature extractor to get all nodes and xpaths from the HTML.
-
-```python
->>> from transformers import MarkupLMProcessor
-
->>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
-
->>> html_string = """
-...  <!DOCTYPE html>
-...  <html>
-...  <head>
-...  <title>Hello world</title>
-...  </head>
-...  <body>
-
-...  <h1>Welcome</h1>
-...  <p>Here is my website.</p>
-
-...  </body>
-...  </html>"""
-
->>> # note that you can also add provide all tokenizer parameters here such as padding, truncation
->>> encoding = processor(html_string, return_tensors="pt")
->>> print(encoding.keys())
-dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])
-```
-
-**Use case 2: web page classification (training, inference) + token classification (inference), parse_html=False**
-
-In case one already has obtained all nodes and xpaths, one doesn't need the feature extractor. In that case, one should
-provide the nodes and corresponding xpaths themselves to the processor, and make sure to set `parse_html` to `False`.
-
-```python
->>> from transformers import MarkupLMProcessor
-
->>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
->>> processor.parse_html = False
-
->>> nodes = ["hello", "world", "how", "are"]
->>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]
->>> encoding = processor(nodes=nodes, xpaths=xpaths, return_tensors="pt")
->>> print(encoding.keys())
-dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])
-```
-
-**Use case 3: token classification (training), parse_html=False**
-
-For token classification tasks (such as [SWDE](https://paperswithcode.com/dataset/swde)), one can also provide the
-corresponding node labels in order to train a model. The processor will then convert these into token-level `labels`.
-By default, it will only label the first wordpiece of a word, and label the remaining wordpieces with -100, which is the
-`ignore_index` of PyTorch's CrossEntropyLoss. In case you want all wordpieces of a word to be labeled, you can
-initialize the tokenizer with `only_label_first_subword` set to `False`.
-
-```python
->>> from transformers import MarkupLMProcessor
-
->>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
->>> processor.parse_html = False
-
->>> nodes = ["hello", "world", "how", "are"]
->>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]
->>> node_labels = [1, 2, 2, 1]
->>> encoding = processor(nodes=nodes, xpaths=xpaths, node_labels=node_labels, return_tensors="pt")
->>> print(encoding.keys())
-dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq', 'labels'])
-```
-
-**Use case 4: web page question answering (inference), parse_html=True**
-
-For question answering tasks on web pages, you can provide a question to the processor. By default, the
-processor will use the feature extractor to get all nodes and xpaths, and create [CLS] question tokens [SEP] word tokens [SEP].
-
-```python
->>> from transformers import MarkupLMProcessor
-
->>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
-
->>> html_string = """
-...  <!DOCTYPE html>
-...  <html>
-...  <head>
-...  <title>Hello world</title>
-...  </head>
-...  <body>
-
-...  <h1>Welcome</h1>
-...  <p>My name is Niels.</p>
-
-...  </body>
-...  </html>"""
-
->>> question = "What's his name?"
->>> encoding = processor(html_string, questions=question, return_tensors="pt")
->>> print(encoding.keys())
-dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])
-```
-
-**Use case 5: web page question answering (inference), parse_html=False**
-
-For question answering tasks (such as WebSRC), you can provide a question to the processor. If you have extracted
-all nodes and xpaths yourself, you can provide them directly to the processor. Make sure to set `parse_html` to `False`.
-
-```python
->>> from transformers import MarkupLMProcessor
-
->>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
->>> processor.parse_html = False
-
->>> nodes = ["hello", "world", "how", "are"]
->>> xpaths = ["/html/body/div/li[1]/div/span", "/html/body/div/li[1]/div/span", "html/body", "html/body/div"]
->>> question = "What's his name?"
->>> encoding = processor(nodes=nodes, xpaths=xpaths, questions=question, return_tensors="pt")
->>> print(encoding.keys())
-dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'xpath_tags_seq', 'xpath_subs_seq'])
-```
-
-## MarkupLMConfig
-
-[[autodoc]] MarkupLMConfig
-    - all
-
-## MarkupLMFeatureExtractor
-
-[[autodoc]] MarkupLMFeatureExtractor
-    - __call__
-
-## MarkupLMTokenizer
-
-[[autodoc]] MarkupLMTokenizer
-    - build_inputs_with_special_tokens
-    - get_special_tokens_mask
-    - create_token_type_ids_from_sequences
-    - save_vocabulary
-
-## MarkupLMTokenizerFast
-
-[[autodoc]] MarkupLMTokenizerFast
-    - all
-
-## MarkupLMProcessor
-
-[[autodoc]] MarkupLMProcessor
-    - __call__
-
-## MarkupLMModel
-
-[[autodoc]] MarkupLMModel
-    - forward
-
-## MarkupLMForSequenceClassification
-
-[[autodoc]] MarkupLMForSequenceClassification
-    - forward
-
-## MarkupLMForTokenClassification
-
-[[autodoc]] MarkupLMForTokenClassification
-    - forward
-
-## MarkupLMForQuestionAnswering
-
-[[autodoc]] MarkupLMForQuestionAnswering
-    - forward

docs/source/en/model_doc/maskformer.mdx

@@ -58,7 +58,6 @@ This model was contributed by [francesco](https://huggingface.co/francesco). The
     - encode_inputs
     - post_process_segmentation
     - post_process_semantic_segmentation
-    - post_process_instance_segmentation
     - post_process_panoptic_segmentation
 
 ## MaskFormerModel

docs/source/en/model_doc/mobilevit.mdx

@@ -66,7 +66,6 @@ This model was contributed by [matthijs](https://huggingface.co/Matthijs). The T
 
 [[autodoc]] MobileViTFeatureExtractor
     - __call__
-    - post_process_semantic_segmentation
 
 ## MobileViTModel
 

docs/source/en/model_doc/opt.mdx

@@ -59,11 +59,6 @@ The original code can be found [here](https://github.com/facebookresearch/metase
 [[autodoc]] OPTForSequenceClassification
     - forward
 
-## OPTForQuestionAnswering
-
-[[autodoc]] OPTForQuestionAnswering
-    - forward
-
 ## FlaxOPTModel
 
 [[autodoc]] FlaxOPTModel

docs/source/en/model_doc/segformer.mdx

@@ -93,7 +93,6 @@ SegFormer's results on the segmentation datasets like ADE20k, refer to the [pape
 
 [[autodoc]] SegformerFeatureExtractor
     - __call__
-    - post_process_semantic_segmentation
 
 ## SegformerModel
 

docs/source/en/model_doc/time_series_transformer.mdx

@@ -1,73 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Time Series Transformer
-
-<Tip>
-
-This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
-breaking changes to fix it in the future. If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).
-
-</Tip>
-
-## Overview
-
-The Time Series Transformer model is a vanilla encoder-decoder Transformer for time series forecasting.
-
-Tips:
-
-- Similar to other models in the library, [`TimeSeriesTransformerModel`] is the raw Transformer without any head on top, and [`TimeSeriesTransformerForPrediction`]
-adds a distribution head on top of the former, which can be used for time-series forecasting. Note that this is a so-called probabilistic forecasting model, not a
-point forecasting model. This means that the model learns a distribution, from which one can sample. The model doesn't directly output values.
-- [`TimeSeriesTransformerForPrediction`] consists of 2 blocks: an encoder, which takes a `context_length` of time series values as input (called `past_values`),
-and a decoder, which predicts a `prediction_length` of time series values into the future (called `future_values`). During training, one needs to provide
-pairs of (`past_values` and `future_values`) to the model.
-- In addition to the raw (`past_values` and `future_values`), one typically provides additional features to the model. These can be the following:
-    - `past_time_features`: temporal features which the model will add to `past_values`. These serve as "positional encodings" for the Transformer encoder.
-    Examples are "day of the month", "month of the year", etc. as scalar values (and then stacked together as a vector).
-    e.g. if a given time-series value was obtained on the 11th of August, then one could have [11, 8] as time feature vector (11 being "day of the month", 8 being "month of the year").
-    - `future_time_features`: temporal features which the model will add to `future_values`. These serve as "positional encodings" for the Transformer decoder.
-    Examples are "day of the month", "month of the year", etc. as scalar values (and then stacked together as a vector).
-    e.g. if a given time-series value was obtained on the 11th of August, then one could have [11, 8] as time feature vector (11 being "day of the month", 8 being "month of the year").
-    - `static_categorical_features`: categorical features which are static over time (i.e., have the same value for all `past_values` and `future_values`).
-    An example here is the store ID or region ID that identifies a given time-series.
-    Note that these features need to be known for ALL data points (also those in the future).
-    - `static_real_features`: real-valued features which are static over time (i.e., have the same value for all `past_values` and `future_values`).
-    An example here is the image representation of the product for which you have the time-series values (like the [ResNet](resnet) embedding of a "shoe" picture,
-    if your time-series is about the sales of shoes).
-    Note that these features need to be known for ALL data points (also those in the future).
-- The model is trained using "teacher-forcing", similar to how a Transformer is trained for machine translation. This means that, during training, one shifts the
-`future_values` one position to the right as input to the decoder, prepended by the last value of `past_values`. At each time step, the model needs to predict the
-next target. So the set-up of training is similar to a GPT model for language, except that there's no notion of `decoder_start_token_id` (we just use the last value
-of the context as initial input for the decoder).
-- At inference time, we give the final value of the `past_values` as input to the decoder. Next, we can sample from the model to make a prediction at the next time step,
-which is then fed to the decoder in order to make the next prediction (also called autoregressive generation).
-
-
-This model was contributed by [kashif](<https://huggingface.co/kashif).
-
-
-## TimeSeriesTransformerConfig
-
-[[autodoc]] TimeSeriesTransformerConfig
-
-
-## TimeSeriesTransformerModel
-
-[[autodoc]] TimeSeriesTransformerModel
-    - forward
-
-
-## TimeSeriesTransformerForPrediction
-
-[[autodoc]] TimeSeriesTransformerForPrediction
-    - forward

docs/source/en/model_doc/vit.mdx

@@ -12,6 +12,13 @@ specific language governing permissions and limitations under the License.
 
 # Vision Transformer (ViT)
 
+<Tip>
+
+This is a recently introduced model so the API hasn't been tested extensively. There may be some bugs or slight
+breaking changes to fix it in the future. If you see something strange, file a [Github Issue](https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title).
+
+</Tip>
+
 ## Overview
 
 The Vision Transformer (ViT) model was proposed in [An Image is Worth 16x16 Words: Transformers for Image Recognition
@@ -56,11 +63,6 @@ Tips:
   language modeling). With this approach, the smaller ViT-B/16 model achieves 79.9% accuracy on ImageNet, a significant
   improvement of 2% to training from scratch, but still 4% behind supervised pre-training.
 
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vit_architecture.jpg"
-alt="drawing" width="600"/> 
-
-<small> ViT architecture. Taken from the <a href="https://arxiv.org/abs/2010.11929">original paper.</a> </small>
-
 Following the original Vision Transformer, some follow-up works have been made:
 
 - [DeiT](deit) (Data-efficient Image Transformers) by Facebook AI. DeiT models are distilled vision transformers.

docs/source/en/model_doc/vit_msn.mdx

@@ -1,64 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# ViTMSN
-
-## Overview
-
-The ViTMSN model was proposed in [Masked Siamese Networks for Label-Efficient Learning](https://arxiv.org/abs/2204.07141) by Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes,
-Pascal Vincent, Armand Joulin, Michael Rabbat, Nicolas Ballas. The paper presents a joint-embedding architecture to match the prototypes
-of masked patches with that of the unmasked patches. With this setup, their method yields excellent performance in the low-shot and extreme low-shot
-regimes.
-
-The abstract from the paper is the following:
-
-*We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations. Our
-approach matches the representation of an image view containing randomly masked patches to the representation of the original
-unmasked image. This self-supervised pre-training strategy is particularly scalable when applied to Vision Transformers since only the
-unmasked patches are processed by the network. As a result, MSNs improve the scalability of joint-embedding architectures,
-while producing representations of a high semantic level that perform competitively on low-shot image classification. For instance,
-on ImageNet-1K, with only 5,000 annotated images, our base MSN model achieves 72.4% top-1 accuracy,
-and with 1% of ImageNet-1K labels, we achieve 75.7% top-1 accuracy, setting a new state-of-the-art for self-supervised learning on this benchmark.*
-
-Tips:
-
-- MSN (masked siamese networks) is a method for self-supervised pre-training of Vision Transformers (ViTs). The pre-training
-objective is to match the prototypes assigned to the unmasked views of the images to that of the masked views of the same images.
-- The authors have only released pre-trained weights of the backbone (ImageNet-1k pre-training). So, to use that on your own image classification dataset,
-use the [`ViTMSNForImageClassification`] class which is initialized from [`ViTMSNModel`]. Follow
-[this notebook](https://github.com/huggingface/notebooks/blob/main/examples/image_classification.ipynb) for a detailed tutorial on fine-tuning.
-- MSN is particularly useful in the low-shot and extreme low-shot regimes. Notably, it achieves 75.7% top-1 accuracy with only 1% of ImageNet-1K
-labels when fine-tuned.
-
-
-<img src="https://i.ibb.co/W6PQMdC/Screenshot-2022-09-13-at-9-08-40-AM.png" alt="drawing" width="600"/> 
-
-<small> MSN architecture. Taken from the <a href="https://arxiv.org/abs/2204.07141">original paper.</a> </small>
-
-This model was contributed by [sayakpaul](https://huggingface.co/sayakpaul). The original code can be found [here](https://github.com/facebookresearch/msn). 
-
-
-## ViTMSNConfig
-
-[[autodoc]] ViTMSNConfig
-
-
-## ViTMSNModel
-
-[[autodoc]] ViTMSNModel
-    - forward
-
-
-## ViTMSNForImageClassification
-
-[[autodoc]] ViTMSNForImageClassification
-    - forward

docs/source/en/model_doc/whisper.mdx

@@ -1,80 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Whisper
-
-## Overview
-
-The Whisper model was proposed in [Robust Speech Recognition via Large-Scale Weak Supervision](https://cdn.openai.com/papers/whisper.pdf) by Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, Ilya Sutskever.
-
-The abstract from the paper is the following:
-
-*We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zeroshot transfer setting without the need for any finetuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.*
-
-
-Tips:
-
-- The model usually performs well without requiring any finetuning.
-- The architecture follows a classic encoder-decoder architecture, which means that it relies on the [`~generation_utils.GenerationMixin.generate`] function for inference.
-- Inference is currently only implemented for short-form i.e. audio is pre-segmented into <=30s segments. Long-form (including timestamps) will be implemented in a future release.
-- One can use [`WhisperProcessor`] to prepare audio for the model, and decode the predicted ID's back into text.
-
-This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ). The Tensorflow version of this model was contributed by [amyeroberts](https://huggingface.co/amyeroberts).
-The original code can be found [here](https://github.com/openai/whisper).
-
-
-## WhisperConfig
-
-[[autodoc]] WhisperConfig
-
-## WhisperTokenizer
-
-[[autodoc]] WhisperTokenizer
-    - build_inputs_with_special_tokens
-    - get_special_tokens_mask
-    - create_token_type_ids_from_sequences
-    - save_vocabulary
-
-## WhisperFeatureExtractor
-
-[[autodoc]] WhisperFeatureExtractor
-    - __call__
-
-## WhisperProcessor
-
-[[autodoc]] WhisperProcessor
-    - __call__
-    - from_pretrained
-    - save_pretrained
-    - batch_decode
-    - decode
-
-## WhisperModel
-
-[[autodoc]] WhisperModel
-    - forward
-
-## WhisperForConditionalGeneration
-
-[[autodoc]] WhisperForConditionalGeneration
-    - forward
-
-
-## TFWhisperModel
-
-[[autodoc]] TFWhisperModel
-    - call
-
-## TFWhisperForConditionalGeneration
-
-[[autodoc]] TFWhisperForConditionalGeneration
-    - call

docs/source/en/model_doc/xclip.mdx

@@ -23,8 +23,7 @@ The abstract from the paper is the following:
 
 Tips:
 
-- Usage of X-CLIP is identical to [CLIP](clip).
-- Demo notebooks for X-CLIP can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/X-CLIP).
+- Usage of X-CLIP is identical to CLIP.
 
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/xclip_architecture.png"
 alt="drawing" width="600"/> 

docs/source/en/perf_train_cpu_many.mdx

@@ -27,7 +27,6 @@ Wheel files are available for the following Python versions:
 
 | Extension Version | Python 3.6 | Python 3.7 | Python 3.8 | Python 3.9 | Python 3.10 |
 | :---------------: | :--------: | :--------: | :--------: | :--------: | :---------: |
-| 1.12.100          |            | √          | √          | √          | √           |
 | 1.12.0            |            | √          | √          | √          | √           |
 | 1.11.0            |            | √          | √          | √          | √           |
 | 1.10.0            | √          | √          | √          | √          |             |
@@ -39,31 +38,16 @@ where `{pytorch_version}` should be your PyTorch version, for instance 1.12.0.
 Check more approaches for [oneccl_bind_pt installation](https://github.com/intel/torch-ccl).
 Versions of oneCCL and PyTorch must match.
 
-<Tip warning={true}>
-
-oneccl_bindings_for_pytorch 1.12.0 prebuilt wheel does not work with PyTorch 1.12.1 (it is for PyTorch 1.12.0)
-PyTorch 1.12.1 should work with oneccl_bindings_for_pytorch 1.12.100
-
-</Tip>
-
 ## Intel® MPI library
 Use this standards-based MPI implementation to deliver flexible, efficient, scalable cluster messaging on Intel® architecture. This component is part of the Intel® oneAPI HPC Toolkit.
+It can be installed via [MPI](https://www.intel.com/content/www/us/en/developer/articles/tool/oneapi-standalone-components.html#mpi).
 
-oneccl_bindings_for_pytorch is installed along with the MPI tool set. Need to source the environment before using it.
+Please set the environment by following command before using it.
 
-for Intel® oneCCL >= 1.12.0
 ```
-oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
-source $oneccl_bindings_for_pytorch_path/env/setvars.sh
+source /opt/intel/oneapi/setvars.sh
 ```
 
-for Intel® oneCCL whose version < 1.12.0
-```
-torch_ccl_path=$(python -c "import torch; import torch_ccl; import os;  print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))")
-source $torch_ccl_path/env/setvars.sh
-```
-
-
 The following "Usage in Trainer" takes mpirun in Intel® MPI library as an example.
 
 

docs/source/en/perf_train_gpu_one.mdx

@@ -25,7 +25,7 @@ In this section we have a look at a few tricks to reduce the memory footprint an
 | DataLoader | Yes | No |
 | DeepSpeed Zero | No | Yes |
 
-A bracket means that it might not be strictly the case but is usually either not a main concern or negligible. Before we start make sure you have installed the following libraries:
+A bracket means that it might not be strictly the case but is usually either not a main concern or negligable. Before we start make sure you have installed the following libraries:
 
 ```bash
 pip install transformers datasets accelerate nvidia-ml-py3
@@ -732,4 +732,4 @@ TrainingArguments(torchdynamo="fx2trt-f16") #enable tensorRT fp16
 This feature involves 3 different libraries. To install them, please follow the instructions below:  
 - [Torchdynamo installation](https://github.com/pytorch/torchdynamo#requirements-and-setup)  
 - [Functorch installation](https://github.com/pytorch/functorch#install)  
-- [Torch-TensorRT(FX) installation](https://github.com/pytorch/TensorRT/blob/master/docsrc/tutorials/getting_started_with_fx_path.rst#installation)  
+- [Torch-TensorRT(FX) installation](https://github.com/pytorch/TensorRT/blob/master/docsrc/tutorials/getting_started_with_fx_path.rst#installation)  

docs/source/en/preprocessing.mdx

@@ -14,29 +14,17 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-Before you can train a model on a dataset, it needs to be preprocessed into the expected model input format. Whether your data is text, images, or audio, they need to be converted and assembled into batches of tensors. 🤗 Transformers provides a set of preprocessing classes to help prepare your data for the model. In this tutorial, you'll learn that for:
+Before you can use your data in a model, the data needs to be processed into an acceptable format for the model. A model does not understand raw text, images or audio. These inputs need to be converted into numbers and assembled into tensors. In this tutorial, you will:
 
-* Text, use a [Tokenizer](./main_classes/tokenizer) to convert text into a sequence of tokens, create a numerical representation of the tokens, and assemble them into tensors.
-* Computer vision and speech, use a [Feature extractor](./main_classes/feature_extractor) to extract sequential features from audio waveforms and images and convert them into tensors.
-* Multimodal inputs, use a [Processor](./main_classes/processors) to combine a tokenizer and a feature extractor.
+* Preprocess textual data with a tokenizer.
+* Preprocess image or audio data with a feature extractor.
+* Preprocess data for a multimodal task with a processor.
 
-<Tip>
-
-`AutoProcessor` **always** works and automatically chooses the correct class for the model you're using, whether you're using a tokenizer, feature extractor or processor.
-
-</Tip>
-
-Before you begin, install 🤗 Datasets so you can load some datasets to experiment with:
-
-```bash
-pip install datasets
-```
-
-## Natural Language Processing
+## NLP
 
 <Youtube id="Yffk5aydLzg"/>
 
-The main tool for preprocessing textual data is a [tokenizer](main_classes/tokenizer). A tokenizer splits text into *tokens* according to a set of rules. The tokens are converted into numbers and then tensors, which become the model inputs. Any additional inputs required by the model are added by the tokenizer.
+The main tool for processing textual data is a [tokenizer](main_classes/tokenizer). A tokenizer starts by splitting text into *tokens* according to a set of rules. The tokens are converted into numbers, which are used to build tensors as input to a model. Any additional inputs required by a model are also added by the tokenizer.
 
 <Tip>
 
@@ -44,7 +32,11 @@ If you plan on using a pretrained model, it's important to use the associated pr
 
 </Tip>
 
-Get started by loading a pretrained tokenizer with the [`AutoTokenizer.from_pretrained`] method. This downloads the *vocab* a model was pretrained with:
+Get started quickly by loading a pretrained tokenizer with the [`AutoTokenizer`] class. This downloads the *vocab* used when a model is pretrained.
+
+### Tokenize
+
+Load a pretrained tokenizer with [`AutoTokenizer.from_pretrained`]:
 
 ```py
 >>> from transformers import AutoTokenizer
@@ -52,7 +44,7 @@ Get started by loading a pretrained tokenizer with the [`AutoTokenizer.from_pret
 >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
 ```
 
-Then pass your text to the tokenizer:
+Then pass your sentence to the tokenizer:
 
 ```py
 >>> encoded_input = tokenizer("Do not meddle in the affairs of wizards, for they are subtle and quick to anger.")
@@ -68,7 +60,7 @@ The tokenizer returns a dictionary with three important items:
 * [attention_mask](glossary#attention-mask) indicates whether a token should be attended to or not.
 * [token_type_ids](glossary#token-type-ids) identifies which sequence a token belongs to when there is more than one sequence.
 
-Return your input by decoding the `input_ids`:
+You can decode the `input_ids` to return the original input:
 
 ```py
 >>> tokenizer.decode(encoded_input["input_ids"])
@@ -76,9 +68,9 @@ Return your input by decoding the `input_ids`:
 ```
 
 As you can see, the tokenizer added two special tokens - `CLS` and `SEP` (classifier and separator) - to the sentence. Not all models need
-special tokens, but if they do, the tokenizer automatically adds them for you.
+special tokens, but if they do, the tokenizer will automatically add them for you.
 
-If there are several sentences you want to preprocess, pass them as a list to the tokenizer:
+If there are several sentences you want to process, pass the sentences as a list to the tokenizer:
 
 ```py
 >>> batch_sentences = [
@@ -101,7 +93,7 @@ If there are several sentences you want to preprocess, pass them as a list to th
 
 ### Pad
 
-Sentences aren't always the same length which can be an issue because tensors, the model inputs, need to have a uniform shape. Padding is a strategy for ensuring tensors are rectangular by adding a special *padding token* to shorter sentences.
+This brings us to an important topic. When you process a batch of sentences, they aren't always the same length. This is a problem because tensors, the input to the model, need to have a uniform shape. Padding is a strategy for ensuring tensors are rectangular by adding a special *padding token* to sentences with fewer tokens.
 
 Set the `padding` parameter to `True` to pad the shorter sequences in the batch to match the longest sequence:
 
@@ -124,11 +116,11 @@ Set the `padding` parameter to `True` to pad the shorter sequences in the batch
                     [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
 ```
 
-The first and third sentences are now padded with `0`'s because they are shorter.
+Notice the tokenizer padded the first and third sentences with a `0` because they are shorter!
 
 ### Truncation
 
-On the other end of the spectrum, sometimes a sequence may be too long for a model to handle. In this case, you'll need to truncate the sequence to a shorter length.
+On the other end of the spectrum, sometimes a sequence may be too long for a model to handle. In this case, you will need to truncate the sequence to a shorter length.
 
 Set the `truncation` parameter to `True` to truncate a sequence to the maximum length accepted by the model:
 
@@ -151,15 +143,9 @@ Set the `truncation` parameter to `True` to truncate a sequence to the maximum l
                     [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
 ```
 
-<Tip>
-
-Check out the [Padding and truncation](./pad_truncation) concept guide to learn more different padding and truncation arguments.
-
-</Tip>
-
 ### Build tensors
 
-Finally, you want the tokenizer to return the actual tensors that get fed to the model.
+Finally, you want the tokenizer to return the actual tensors that are fed to the model.
 
 Set the `return_tensors` parameter to either `pt` for PyTorch, or `tf` for TensorFlow:
 
@@ -213,9 +199,13 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 ## Audio
 
-For audio tasks, you'll need a [feature extractor](main_classes/feature_extractor) to prepare your dataset for the model. The feature extractor is designed to extract features from raw audio data, and convert them into tensors.
+Audio inputs are preprocessed differently than textual inputs, but the end goal remains the same: create numerical sequences the model can understand. A [feature extractor](main_classes/feature_extractor) is designed for the express purpose of extracting features from raw image or audio data and converting them into tensors. Before you begin, install 🤗 Datasets to load an audio dataset to experiment with:
 
-Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a feature extractor with audio datasets:
+```bash
+pip install datasets
+```
+
+Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset):
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -223,7 +213,7 @@ Load the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset (see
 >>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
 ```
 
-Access the first element of the `audio` column to take a look at the input. Calling the `audio` column automatically loads and resamples the audio file:
+Access the first element of the `audio` column to take a look at the input. Calling the `audio` column will automatically load and resample the audio file:
 
 ```py
 >>> dataset[0]["audio"]
@@ -239,7 +229,20 @@ This returns three items:
 * `path` points to the location of the audio file.
 * `sampling_rate` refers to how many data points in the speech signal are measured per second.
 
-For this tutorial, you'll use the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model. Take a look at the model card, and you'll learn Wav2Vec2 is pretrained on 16kHz sampled speech audio. It is important your audio data's sampling rate matches the sampling rate of the dataset used to pretrain the model. If your data's sampling rate isn't the same, then you need to resample your data. 
+### Resample
+
+For this tutorial, you will use the [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) model. As you can see from the model card, the Wav2Vec2 model is pretrained on 16kHz sampled speech audio. It is important your audio data's sampling rate matches the sampling rate of the dataset used to pretrain the model. If your data's sampling rate isn't the same, then you need to resample your audio data. 
+
+For example, the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset has a sampling rate of 8000kHz. In order to use the Wav2Vec2 model with this dataset, upsample the sampling rate to 16kHz:
+
+```py
+>>> dataset = load_dataset("PolyAI/minds14", name="en-US", split="train")
+>>> dataset[0]["audio"]
+{'array': array([ 0.        ,  0.00024414, -0.00024414, ..., -0.00024414,
+         0.        ,  0.        ], dtype=float32),
+ 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~JOINT_ACCOUNT/602ba55abb1e6d0fbce92065.wav',
+ 'sampling_rate': 8000}
+```
 
 1. Use 🤗 Datasets' [`~datasets.Dataset.cast_column`] method to upsample the sampling rate to 16kHz:
 
@@ -247,7 +250,7 @@ For this tutorial, you'll use the [Wav2Vec2](https://huggingface.co/facebook/wav
 >>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
 ```
 
-2. Call the `audio` column again to resample the audio file:
+2. Load the audio file:
 
 ```py
 >>> dataset[0]["audio"]
@@ -257,7 +260,11 @@ For this tutorial, you'll use the [Wav2Vec2](https://huggingface.co/facebook/wav
  'sampling_rate': 16000}
 ```
 
-Next, load a feature extractor to normalize and pad the input. When padding textual data, a `0` is added for shorter sequences. The same idea applies to audio data. The feature extractor adds a `0` - interpreted as silence - to `array`.
+As you can see, the `sampling_rate` is now 16kHz!
+
+### Feature extractor
+
+The next step is to load a feature extractor to normalize and pad the input. When padding textual data, a `0` is added for shorter sequences. The same idea applies to audio data, and the audio feature extractor will add a `0` - interpreted as silence - to `array`.
 
 Load the feature extractor with [`AutoFeatureExtractor.from_pretrained`]:
 
@@ -276,6 +283,8 @@ Pass the audio `array` to the feature extractor. We also recommend adding the `s
         5.6335266e-04,  4.6588284e-06, -1.7142107e-04], dtype=float32)]}
 ```
 
+### Pad and truncate
+
 Just like the tokenizer, you can apply padding or truncation to handle variable sequences in a batch. Take a look at the sequence length of these two audio samples:
 
 ```py
@@ -286,7 +295,7 @@ Just like the tokenizer, you can apply padding or truncation to handle variable
 (106496,)
 ```
 
-Create a function to preprocess the dataset so the audio samples are the same lengths. Specify a maximum sample length, and the feature extractor will either pad or truncate the sequences to match it:
+As you can see, the first sample has a longer sequence than the second sample. Let's create a function that will preprocess the dataset. Specify a maximum sample length, and the feature extractor will either pad or truncate the sequences to match it:
 
 ```py
 >>> def preprocess_function(examples):
@@ -301,13 +310,13 @@ Create a function to preprocess the dataset so the audio samples are the same le
 ...     return inputs
 ```
 
-Apply the `preprocess_function` to the the first few examples in the dataset:
+Apply the function to the the first few examples in the dataset:
 
 ```py
 >>> processed_dataset = preprocess_function(dataset[:5])
 ```
 
-The sample lengths are now the same and match the specified maximum length. You can pass your processed dataset to the model now!
+Now take another look at the processed sample lengths:
 
 ```py
 >>> processed_dataset["input_values"][0].shape
@@ -317,17 +326,13 @@ The sample lengths are now the same and match the specified maximum length. You
 (100000,)
 ```
 
-## Computer vision
+The lengths of the first two samples now match the maximum length you specified.
 
-For computer vision tasks, you'll need a [feature extractor](main_classes/feature_extractor) to prepare your dataset for the model. The feature extractor is designed to extract features from images, and convert them into tensors.
+## Vision
 
-Load the [food101](https://huggingface.co/datasets/food101) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a feature extractor with computer vision datasets: 
+A feature extractor is also used to process images for vision tasks. Once again, the goal is to convert the raw image into a batch of tensors as input.
 
-<Tip>
-
-Use 🤗 Datasets `split` parameter to only load a small sample from the training split since the dataset is quite large!
-
-</Tip>
+Let's load the [food101](https://huggingface.co/datasets/food101) dataset for this tutorial. Use 🤗 Datasets `split` parameter to only load a small sample from the training split since the dataset is quite large:
 
 ```py
 >>> from datasets import load_dataset
@@ -341,9 +346,9 @@ Next, take a look at the image with 🤗 Datasets [`Image`](https://huggingface.
 >>> dataset[0]["image"]
 ```
 
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png"/>
-</div>
+![vision-preprocess-tutorial.png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/vision-preprocess-tutorial.png)
+
+### Feature extractor
 
 Load the feature extractor with [`AutoFeatureExtractor.from_pretrained`]:
 
@@ -353,9 +358,11 @@ Load the feature extractor with [`AutoFeatureExtractor.from_pretrained`]:
 >>> feature_extractor = AutoFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
 ```
 
-For computer vision tasks, it is common to add some type of data augmentation to the images as a part of preprocessing. You can add augmentations with any library you'd like, but in this tutorial, you'll use torchvision's [`transforms`](https://pytorch.org/vision/stable/transforms.html) module. If you're interested in using another data augmentation library, learn how in the [Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) or [Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb).
+### Data augmentation
 
-1. Normalize the image with the feature extractor and use [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) to chain some transforms - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - together:
+For vision tasks, it is common to add some type of data augmentation to the images as a part of preprocessing. You can add augmentations with any library you'd like, but in this tutorial, you will use torchvision's [`transforms`](https://pytorch.org/vision/stable/transforms.html) module.
+
+1. Normalize the image and use [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) to chain some transforms - [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) - together:
 
 ```py
 >>> from torchvision.transforms import Compose, Normalize, RandomResizedCrop, ColorJitter, ToTensor
@@ -366,7 +373,7 @@ For computer vision tasks, it is common to add some type of data augmentation to
 ... )
 ```
 
-2. The model accepts [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) as its input, which is generated by the feature extractor. Create a function that generates `pixel_values` from the transforms:
+2. The model accepts [`pixel_values`](model_doc/visionencoderdecoder#transformers.VisionEncoderDecoderModel.forward.pixel_values) as it's input. This value is generated by the feature extractor. Create a function that generates `pixel_values` from the transforms:
 
 ```py
 >>> def transforms(examples):
@@ -374,13 +381,13 @@ For computer vision tasks, it is common to add some type of data augmentation to
 ...     return examples
 ```
 
-3. Then use 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform) to apply the transforms on the fly:
+3. Then use 🤗 Datasets [`set_transform`](https://huggingface.co/docs/datasets/process.html#format-transform) to apply the transforms on-the-fly:
 
 ```py
 >>> dataset.set_transform(transforms)
 ```
 
-4. Now when you access the image, you'll notice the feature extractor has added `pixel_values`. You can pass your processed dataset to the model now!
+4. Now when you access the image, you will notice the feature extractor has added the model input `pixel_values`:
 
 ```py
 >>> dataset[0]["image"]
@@ -411,7 +418,7 @@ For computer vision tasks, it is common to add some type of data augmentation to
           [-0.1922, -0.1922, -0.1922,  ..., -0.2941, -0.2863, -0.3412]]])}
 ```
 
-Here is what the image looks like after the transforms are applied. The image has been randomly cropped and it's color properties are different.
+Here is what the image looks like after you preprocess it. Just as you'd expect from the applied transforms, the image has been randomly cropped and it's color properties are different.
 
 ```py
 >>> import numpy as np
@@ -421,15 +428,16 @@ Here is what the image looks like after the transforms are applied. The image ha
 >>> plt.imshow(img.permute(1, 2, 0))
 ```
 
-<div class="flex justify-center">
-    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png"/>
-</div>
+![preprocessed_image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/preprocessed_image.png)
 
 ## Multimodal
 
-For tasks involving multimodal inputs, you'll need a [processor](main_classes/processors) to prepare your dataset for the model. A processor couples a tokenizer and feature extractor.
+For multimodal tasks. you will use a combination of everything you've learned so far and apply your skills to a automatic speech recognition (ASR) task. This means you will need a:
 
-Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the 🤗 [Datasets tutorial](https://huggingface.co/docs/datasets/load_hub.html) for more details on how to load a dataset) to see how you can use a processor for automatic speech recognition (ASR):
+* Feature extractor to preprocess the audio data.
+* Tokenizer to process the text.
+
+Let's return to the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset:
 
 ```py
 >>> from datasets import load_dataset
@@ -437,7 +445,7 @@ Load the [LJ Speech](https://huggingface.co/datasets/lj_speech) dataset (see the
 >>> lj_speech = load_dataset("lj_speech", split="train")
 ```
 
-For ASR, you're mainly focused on `audio` and `text` so you can remove the other columns:
+Since you are mainly interested in the `audio` and `text` column, remove the other columns:
 
 ```py
 >>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
@@ -456,13 +464,15 @@ Now take a look at the `audio` and `text` columns:
 'Printing, in the only sense with which we are at present concerned, differs from most if not from all the arts and crafts represented in the Exhibition'
 ```
 
-Remember you should always [resample](preprocessing#audio) your audio dataset's sampling rate to match the sampling rate of the dataset used to pretrain a model!
+Remember from the earlier section on processing audio data, you should always [resample](preprocessing#audio) your audio data's sampling rate to match the sampling rate of the dataset used to pretrain a model:
 
 ```py
 >>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
 ```
 
-Load a processor with [`AutoProcessor.from_pretrained`]:
+### Processor
+
+A processor combines a feature extractor and tokenizer. Load a processor with [`AutoProcessor.from_pretrained]:
 
 ```py
 >>> from transformers import AutoProcessor
@@ -470,7 +480,7 @@ Load a processor with [`AutoProcessor.from_pretrained`]:
 >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h")
 ```
 
-1. Create a function to process the audio data contained in `array` to `input_values`, and tokenize `text` to `labels`. These are the inputs to the model:
+1. Create a function to process the audio data to `input_values`, and tokenizes the text to `labels`. These are your inputs to the model:
 
 ```py
 >>> def prepare_dataset(example):
@@ -487,4 +497,6 @@ Load a processor with [`AutoProcessor.from_pretrained`]:
 >>> prepare_dataset(lj_speech[0])
 ```
 
-The processor has now added `input_values` and `labels`, and the sampling rate has also been correctly downsampled to 16kHz. You can pass your processed dataset to the model now!
+Notice the processor has added `input_values` and `labels`. The sampling rate has also been correctly downsampled to 16kHz.
+
+Awesome, you should now be able to preprocess data for any modality and even combine different modalities! In the next tutorial, learn how to fine-tune a model on your newly preprocessed data.

docs/source/en/quicktour.mdx

@@ -193,8 +193,8 @@ Pass your text to the tokenizer:
 
 The tokenizer returns a dictionary containing:
 
-* [input_ids](./glossary#input-ids): numerical representations of your tokens.
-* [attention_mask](.glossary#attention-mask): indicates which tokens should be attended to.
+* [input_ids](./glossary#input-ids): numerical representions of your tokens.
+* [atttention_mask](.glossary#attention-mask): indicates which tokens should be attended to.
 
 A tokenizer can also accept a list of inputs, and pad and truncate the text to return a batch with uniform length:
 
@@ -525,4 +525,4 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
 
 ## What's next?
 
-Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!
+Now that you've completed the 🤗 Transformers quick tour, check out our guides and learn how to do more specific things like writing a custom model, fine-tuning a model for a task, and how to train a model with a script. If you're interested in learning more about 🤗 Transformers core concepts, grab a cup of coffee and take a look at our Conceptual Guides!

docs/source/en/serialization.mdx

@@ -10,36 +10,36 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Export to ONNX
+# Export 🤗 Transformers Models
 
-If you need to deploy 🤗 Transformers models in production environments, we recommend
-exporting them to a serialized format that can be loaded and executed on specialized
-runtimes and hardware. In this guide, we'll show you how to export 🤗 Transformers
-models to [ONNX (Open Neural Network eXchange)](http://onnx.ai).
+If you need to deploy 🤗 Transformers models in production environments, we
+recommend exporting them to a serialized format that can be loaded and executed
+on specialized runtimes and hardware. In this guide, we'll show you how to
+export 🤗 Transformers models in two widely used formats: ONNX and TorchScript.
 
-<Tip>
-
-Once exported, a model can be optimized for inference via techniques such as
-quantization and pruning. If you are interested in optimizing your models to run with
-maximum efficiency, check out the [🤗 Optimum
+Once exported, a model can optimized for inference via techniques such as
+quantization and pruning. If you are interested in optimizing your models to run
+with maximum efficiency, check out the [🤗 Optimum
 library](https://github.com/huggingface/optimum).
 
-</Tip>
+## ONNX
 
-ONNX is an open standard that defines a common set of operators and a common file format
-to represent deep learning models in a wide variety of frameworks, including PyTorch and
-TensorFlow. When a model is exported to the ONNX format, these operators are used to
-construct a computational graph (often called an _intermediate representation_) which
-represents the flow of data through the neural network.
+The [ONNX (Open Neural Network eXchange)](http://onnx.ai) project is an open
+standard that defines a common set of operators and a common file format to
+represent deep learning models in a wide variety of frameworks, including
+PyTorch and TensorFlow. When a model is exported to the ONNX format, these
+operators are used to construct a computational graph (often called an
+_intermediate representation_) which represents the flow of data through the
+neural network.
 
-By exposing a graph with standardized operators and data types, ONNX makes it easy to
-switch between frameworks. For example, a model trained in PyTorch can be exported to
-ONNX format and then imported in TensorFlow (and vice versa).
+By exposing a graph with standardized operators and data types, ONNX makes it
+easy to switch between frameworks. For example, a model trained in PyTorch can
+be exported to ONNX format and then imported in TensorFlow (and vice versa).
 
-🤗 Transformers provides a [`transformers.onnx`](main_classes/onnx) package that enables
-you to convert model checkpoints to an ONNX graph by leveraging configuration objects.
-These configuration objects come ready made for a number of model architectures, and are
-designed to be easily extendable to other architectures.
+🤗 Transformers provides a `transformers.onnx` package that enables you to
+convert model checkpoints to an ONNX graph by leveraging configuration objects.
+These configuration objects come ready made for a number of model architectures,
+and are designed to be easily extendable to other architectures.
 
 Ready-made configurations include the following architectures:
 
@@ -57,7 +57,6 @@ Ready-made configurations include the following architectures:
 - CamemBERT
 - CLIP
 - CodeGen
-- Conditional DETR
 - ConvBERT
 - ConvNeXT
 - Data2VecText
@@ -94,9 +93,7 @@ Ready-made configurations include the following architectures:
 - RoFormer
 - SegFormer
 - SqueezeBERT
-- Swin Transformer
 - T5
-- Vision Encoder decoder
 - ViT
 - XLM
 - XLM-RoBERTa
@@ -108,10 +105,10 @@ In the next two sections, we'll show you how to:
 * Export a supported model using the `transformers.onnx` package.
 * Export a custom model for an unsupported architecture.
 
-## Exporting a model to ONNX
+### Exporting a model to ONNX
 
-To export a 🤗 Transformers model to ONNX, you'll first need to install some extra
-dependencies:
+To export a 🤗 Transformers model to ONNX, you'll first need to install some
+extra dependencies:
 
 ```bash
 pip install transformers[onnx]
@@ -143,7 +140,7 @@ Exporting a checkpoint using a ready-made configuration can be done as follows:
 python -m transformers.onnx --model=distilbert-base-uncased onnx/
 ```
 
-You should see the following logs:
+which should show the following logs:
 
 ```bash
 Validating ONNX model...
@@ -154,13 +151,13 @@ Validating ONNX model...
 All good, model saved at: onnx/model.onnx
 ```
 
-This exports an ONNX graph of the checkpoint defined by the `--model` argument. In this
-example, it is `distilbert-base-uncased`, but it can be any checkpoint on the Hugging
-Face Hub or one that's stored locally.
+This exports an ONNX graph of the checkpoint defined by the `--model` argument.
+In this example it is `distilbert-base-uncased`, but it can be any checkpoint on
+the Hugging Face Hub or one that's stored locally.
 
 The resulting `model.onnx` file can then be run on one of the [many
-accelerators](https://onnx.ai/supported-tools.html#deployModel) that support the ONNX
-standard. For example, we can load and run the model with [ONNX
+accelerators](https://onnx.ai/supported-tools.html#deployModel) that support the
+ONNX standard. For example, we can load and run the model with [ONNX
 Runtime](https://onnxruntime.ai/) as follows:
 
 ```python
@@ -174,8 +171,9 @@ Runtime](https://onnxruntime.ai/) as follows:
 >>> outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
 ```
 
-The required output names (like `["last_hidden_state"]`) can be obtained by taking a
-look at the ONNX configuration of each model. For example, for DistilBERT we have:
+The required output names (i.e. `["last_hidden_state"]`) can be obtained by
+taking a look at the ONNX configuration of each model. For example, for
+DistilBERT we have:
 
 ```python
 >>> from transformers.models.distilbert import DistilBertConfig, DistilBertOnnxConfig
@@ -186,19 +184,20 @@ look at the ONNX configuration of each model. For example, for DistilBERT we hav
 ["last_hidden_state"]
 ```
 
-The process is identical for TensorFlow checkpoints on the Hub. For example, we can
-export a pure TensorFlow checkpoint from the [Keras
+The process is identical for TensorFlow checkpoints on the Hub. For example, we
+can export a pure TensorFlow checkpoint from the [Keras
 organization](https://huggingface.co/keras-io) as follows:
 
 ```bash
 python -m transformers.onnx --model=keras-io/transformers-qa onnx/
 ```
 
-To export a model that's stored locally, you'll need to have the model's weights and
-tokenizer files stored in a directory. For example, we can load and save a checkpoint as
-follows:
+To export a model that's stored locally, you'll need to have the model's weights
+and tokenizer files stored in a directory. For example, we can load and save a
+checkpoint as follows:
 
-<frameworkcontent> <pt>
+<frameworkcontent>
+<pt>
 ```python
 >>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
@@ -216,7 +215,8 @@ argument of the `transformers.onnx` package to the desired directory:
 ```bash
 python -m transformers.onnx --model=local-pt-checkpoint onnx/
 ```
-</pt> <tf>
+</pt>
+<tf>
 ```python
 >>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
 
@@ -234,13 +234,14 @@ argument of the `transformers.onnx` package to the desired directory:
 ```bash
 python -m transformers.onnx --model=local-tf-checkpoint onnx/
 ```
-</tf> </frameworkcontent>
+</tf>
+</frameworkcontent>
 
-## Selecting features for different model tasks
+### Selecting features for different model topologies
 
-Each ready-made configuration comes with a set of _features_ that enable you to export
-models for different types of tasks. As shown in the table below, each feature is
-associated with a different `AutoClass`:
+Each ready-made configuration comes with a set of _features_ that enable you to
+export models for different types of topologies or tasks. As shown in the table
+below, each feature is associated with a different auto class:
 
 | Feature                              | Auto Class                           |
 | ------------------------------------ | ------------------------------------ |
@@ -253,7 +254,7 @@ associated with a different `AutoClass`:
 | `token-classification`               | `AutoModelForTokenClassification`    |
 
 For each configuration, you can find the list of supported features via the
-[`~transformers.onnx.FeaturesManager`]. For example, for DistilBERT we have:
+`FeaturesManager`. For example, for DistilBERT we have:
 
 ```python
 >>> from transformers.onnx.features import FeaturesManager
@@ -264,15 +265,15 @@ For each configuration, you can find the list of supported features via the
 ```
 
 You can then pass one of these features to the `--feature` argument in the
-`transformers.onnx` package. For example, to export a text-classification model we can
-pick a fine-tuned model from the Hub and run:
+`transformers.onnx` package. For example, to export a text-classification model
+we can pick a fine-tuned model from the Hub and run:
 
 ```bash
 python -m transformers.onnx --model=distilbert-base-uncased-finetuned-sst-2-english \
                             --feature=sequence-classification onnx/
 ```
 
-This displays the following logs:
+which will display the following logs:
 
 ```bash
 Validating ONNX model...
@@ -283,42 +284,37 @@ Validating ONNX model...
 All good, model saved at: onnx/model.onnx
 ```
 
-Notice that in this case, the output names from the fine-tuned model are `logits`
-instead of the `last_hidden_state` we saw with the `distilbert-base-uncased` checkpoint
-earlier. This is expected since the fine-tuned model has a sequence classification head.
+Notice that in this case, the output names from the fine-tuned model are
+`logits` instead of the `last_hidden_state` we saw with the
+`distilbert-base-uncased` checkpoint earlier. This is expected since the
+fine-tuned model has a sequence classification head.
 
 <Tip>
 
-The features that have a `with-past` suffix (like `causal-lm-with-past`) correspond to
-model classes with precomputed hidden states (key and values in the attention blocks)
-that can be used for fast autoregressive decoding.
-
-</Tip>
-
-<Tip>
-
-For `VisionEncoderDecoder` type models, the encoder and decoder parts are
-exported separately as two ONNX files named `encoder_model.onnx` and `decoder_model.onnx` respectively.
+The features that have a `with-past` suffix (e.g. `causal-lm-with-past`)
+correspond to model topologies with precomputed hidden states (key and values
+in the attention blocks) that can be used for fast autoregressive decoding.
 
 </Tip>
 
 
-## Exporting a model for an unsupported architecture
+### Exporting a model for an unsupported architecture
 
-If you wish to export a model whose architecture is not natively supported by the
-library, there are three main steps to follow:
+If you wish to export a model whose architecture is not natively supported by
+the library, there are three main steps to follow:
 
 1. Implement a custom ONNX configuration.
 2. Export the model to ONNX.
 3. Validate the outputs of the PyTorch and exported models.
 
-In this section, we'll look at how DistilBERT was implemented to show what's involved
-with each step.
+In this section, we'll look at how DistilBERT was implemented to show what's
+involved with each step.
 
-### Implementing a custom ONNX configuration
+#### Implementing a custom ONNX configuration
 
-Let's start with the ONNX configuration object. We provide three abstract classes that
-you should inherit from, depending on the type of model architecture you wish to export:
+Let's start with the ONNX configuration object. We provide three abstract
+classes that you should inherit from, depending on the type of model
+architecture you wish to export:
 
 * Encoder-based models inherit from [`~onnx.config.OnnxConfig`]
 * Decoder-based models inherit from [`~onnx.config.OnnxConfigWithPast`]
@@ -350,24 +346,25 @@ Since DistilBERT is an encoder-based model, its configuration inherits from
 ...         )
 ```
 
-Every configuration object must implement the `inputs` property and return a mapping,
-where each key corresponds to an expected input, and each value indicates the axis of
-that input. For DistilBERT, we can see that two inputs are required: `input_ids` and
-`attention_mask`. These inputs have the same shape of `(batch_size, sequence_length)`
-which is why we see the same axes used in the configuration.
+Every configuration object must implement the `inputs` property and return a
+mapping, where each key corresponds to an expected input, and each value
+indicates the axis of that input. For DistilBERT, we can see that two inputs are
+required: `input_ids` and `attention_mask`. These inputs have the same shape of
+`(batch_size, sequence_length)` which is why we see the same axes used in the
+configuration.
 
 <Tip>
 
-Notice that `inputs` property for `DistilBertOnnxConfig` returns an `OrderedDict`. This
-ensures that the inputs are matched with their relative position within the
-`PreTrainedModel.forward()` method when tracing the graph. We recommend using an
-`OrderedDict` for the `inputs` and `outputs` properties when implementing custom ONNX
-configurations.
+Notice that `inputs` property for `DistilBertOnnxConfig` returns an
+`OrderedDict`. This ensures that the inputs are matched with their relative
+position within the `PreTrainedModel.forward()` method when tracing the graph.
+We recommend using an `OrderedDict` for the `inputs` and `outputs` properties
+when implementing custom ONNX configurations.
 
 </Tip>
 
-Once you have implemented an ONNX configuration, you can instantiate it by providing the
-base model's configuration as follows:
+Once you have implemented an ONNX configuration, you can instantiate it by
+providing the base model's configuration as follows:
 
 ```python
 >>> from transformers import AutoConfig
@@ -376,8 +373,8 @@ base model's configuration as follows:
 >>> onnx_config = DistilBertOnnxConfig(config)
 ```
 
-The resulting object has several useful properties. For example, you can view the ONNX
-operator set that will be used during the export:
+The resulting object has several useful properties. For example you can view the
+ONNX operator set that will be used during the export:
 
 ```python
 >>> print(onnx_config.default_onnx_opset)
@@ -391,14 +388,15 @@ You can also view the outputs associated with the model as follows:
 OrderedDict([("last_hidden_state", {0: "batch", 1: "sequence"})])
 ```
 
-Notice that the outputs property follows the same structure as the inputs; it returns an
-`OrderedDict` of named outputs and their shapes. The output structure is linked to the
-choice of feature that the configuration is initialised with. By default, the ONNX
-configuration is initialized with the `default` feature that corresponds to exporting a
-model loaded with the `AutoModel` class. If you want to export a model for another task,
-just provide a different feature to the `task` argument when you initialize the ONNX
-configuration. For example, if we wished to export DistilBERT with a sequence
-classification head, we could use:
+Notice that the outputs property follows the same structure as the inputs; it
+returns an `OrderedDict` of named outputs and their shapes. The output structure
+is linked to the choice of feature that the configuration is initialised with.
+By default, the ONNX configuration is initialized with the `default` feature
+that corresponds to exporting a model loaded with the `AutoModel` class. If you
+want to export a different model topology, just provide a different feature to
+the `task` argument when you initialize the ONNX configuration. For example, if
+we wished to export DistilBERT with a sequence classification head, we could
+use:
 
 ```python
 >>> from transformers import AutoConfig
@@ -411,18 +409,18 @@ OrderedDict([('logits', {0: 'batch'})])
 
 <Tip>
 
-All of the base properties and methods associated with [`~onnx.config.OnnxConfig`] and
-the other configuration classes can be overriden if needed. Check out [`BartOnnxConfig`]
-for an advanced example.
+All of the base properties and methods associated with [`~onnx.config.OnnxConfig`] and the
+other configuration classes can be overriden if needed. Check out
+[`BartOnnxConfig`] for an advanced example.
 
 </Tip>
 
-### Exporting the model
+#### Exporting the model
 
-Once you have implemented the ONNX configuration, the next step is to export the model.
-Here we can use the `export()` function provided by the `transformers.onnx` package.
-This function expects the ONNX configuration, along with the base model and tokenizer,
-and the path to save the exported file:
+Once you have implemented the ONNX configuration, the next step is to export the
+model. Here we can use the `export()` function provided by the
+`transformers.onnx` package. This function expects the ONNX configuration, along
+with the base model and tokenizer, and the path to save the exported file:
 
 ```python
 >>> from pathlib import Path
@@ -437,9 +435,10 @@ and the path to save the exported file:
 >>> onnx_inputs, onnx_outputs = export(tokenizer, base_model, onnx_config, onnx_config.default_onnx_opset, onnx_path)
 ```
 
-The `onnx_inputs` and `onnx_outputs` returned by the `export()` function are lists of
-the keys defined in the `inputs` and `outputs` properties of the configuration. Once the
-model is exported, you can test that the model is well formed as follows:
+The `onnx_inputs` and `onnx_outputs` returned by the `export()` function are
+lists of the keys defined in the `inputs` and `outputs` properties of the
+configuration. Once the model is exported, you can test that the model is well
+formed as follows:
 
 ```python
 >>> import onnx
@@ -450,20 +449,21 @@ model is exported, you can test that the model is well formed as follows:
 
 <Tip>
 
-If your model is larger than 2GB, you will see that many additional files are created
-during the export. This is _expected_ because ONNX uses [Protocol
-Buffers](https://developers.google.com/protocol-buffers/) to store the model and these
-have a size limit of 2GB. See the [ONNX
-documentation](https://github.com/onnx/onnx/blob/master/docs/ExternalData.md) for
-instructions on how to load models with external data.
+If your model is larger than 2GB, you will see that many additional files are
+created during the export. This is _expected_ because ONNX uses [Protocol
+Buffers](https://developers.google.com/protocol-buffers/) to store the model and
+these have a size limit of 2GB. See the [ONNX
+documentation](https://github.com/onnx/onnx/blob/master/docs/ExternalData.md)
+for instructions on how to load models with external data.
 
 </Tip>
 
-### Validating the model outputs
+#### Validating the model outputs
 
-The final step is to validate that the outputs from the base and exported model agree
-within some absolute tolerance. Here we can use the `validate_model_outputs()` function
-provided by the `transformers.onnx` package as follows:
+The final step is to validate that the outputs from the base and exported model
+agree within some absolute tolerance. Here we can use the
+`validate_model_outputs()` function provided by the `transformers.onnx` package
+as follows:
 
 ```python
 >>> from transformers.onnx import validate_model_outputs
@@ -473,23 +473,220 @@ provided by the `transformers.onnx` package as follows:
 ... )
 ```
 
-This function uses the [`~transformers.onnx.OnnxConfig.generate_dummy_inputs`] method to
-generate inputs for the base and exported model, and the absolute tolerance can be
-defined in the configuration. We generally find numerical agreement in the 1e-6 to 1e-4
-range, although anything smaller than 1e-3 is likely to be OK.
+This function uses the `OnnxConfig.generate_dummy_inputs()` method to generate
+inputs for the base and exported model, and the absolute tolerance can be
+defined in the configuration. We generally find numerical agreement in the 1e-6
+to 1e-4 range, although anything smaller than 1e-3 is likely to be OK.
 
-## Contributing a new configuration to 🤗 Transformers
+### Contributing a new configuration to 🤗 Transformers
 
-We are looking to expand the set of ready-made configurations and welcome contributions
-from the community! If you would like to contribute your addition to the library, you
-will need to:
+We are looking to expand the set of ready-made configurations and welcome
+contributions from the community! If you would like to contribute your addition
+to the library, you will need to:
 
 * Implement the ONNX configuration in the corresponding `configuration_<model_name>.py`
 file
-* Include the model architecture and corresponding features in
-  [`~onnx.features.FeatureManager`]
+* Include the model architecture and corresponding features in [`~onnx.features.FeatureManager`]
 * Add your model architecture to the tests in `test_onnx_v2.py`
 
 Check out how the configuration for [IBERT was
-contributed](https://github.com/huggingface/transformers/pull/14868/files) to get an
-idea of what's involved.
+contributed](https://github.com/huggingface/transformers/pull/14868/files) to
+get an idea of what's involved.
+
+## TorchScript
+
+<Tip>
+
+This is the very beginning of our experiments with TorchScript and we are still exploring its capabilities with
+variable-input-size models. It is a focus of interest to us and we will deepen our analysis in upcoming releases,
+with more code examples, a more flexible implementation, and benchmarks comparing python-based codes with compiled
+TorchScript.
+
+</Tip>
+
+According to Pytorch's documentation: "TorchScript is a way to create serializable and optimizable models from PyTorch
+code". Pytorch's two modules [JIT and TRACE](https://pytorch.org/docs/stable/jit.html) allow the developer to export
+their model to be re-used in other programs, such as efficiency-oriented C++ programs.
+
+We have provided an interface that allows the export of 🤗 Transformers models to TorchScript so that they can be reused
+in a different environment than a Pytorch-based python program. Here we explain how to export and use our models using
+TorchScript.
+
+Exporting a model requires two things:
+
+- a forward pass with dummy inputs.
+- model instantiation with the `torchscript` flag.
+
+These necessities imply several things developers should be careful about. These are detailed below.
+
+### TorchScript flag and tied weights
+
+This flag is necessary because most of the language models in this repository have tied weights between their
+`Embedding` layer and their `Decoding` layer. TorchScript does not allow the export of models that have tied
+weights, therefore it is necessary to untie and clone the weights beforehand.
+
+This implies that models instantiated with the `torchscript` flag have their `Embedding` layer and `Decoding`
+layer separate, which means that they should not be trained down the line. Training would de-synchronize the two
+layers, leading to unexpected results.
+
+This is not the case for models that do not have a Language Model head, as those do not have tied weights. These models
+can be safely exported without the `torchscript` flag.
+
+### Dummy inputs and standard lengths
+
+The dummy inputs are used to do a model forward pass. While the inputs' values are propagating through the layers,
+Pytorch keeps track of the different operations executed on each tensor. These recorded operations are then used to
+create the "trace" of the model.
+
+The trace is created relatively to the inputs' dimensions. It is therefore constrained by the dimensions of the dummy
+input, and will not work for any other sequence length or batch size. When trying with a different size, an error such
+as:
+
+`The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2`
+
+will be raised. It is therefore recommended to trace the model with a dummy input size at least as large as the largest
+input that will be fed to the model during inference. Padding can be performed to fill the missing values. As the model
+will have been traced with a large input size however, the dimensions of the different matrix will be large as well,
+resulting in more calculations.
+
+It is recommended to be careful of the total number of operations done on each input and to follow performance closely
+when exporting varying sequence-length models.
+
+### Using TorchScript in Python
+
+Below is an example, showing how to save, load models as well as how to use the trace for inference.
+
+#### Saving a model
+
+This snippet shows how to use TorchScript to export a `BertModel`. Here the `BertModel` is instantiated according
+to a `BertConfig` class and then saved to disk under the filename `traced_bert.pt`
+
+```python
+from transformers import BertModel, BertTokenizer, BertConfig
+import torch
+
+enc = BertTokenizer.from_pretrained("bert-base-uncased")
+
+# Tokenizing input text
+text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
+tokenized_text = enc.tokenize(text)
+
+# Masking one of the input tokens
+masked_index = 8
+tokenized_text[masked_index] = "[MASK]"
+indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
+segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
+
+# Creating a dummy input
+tokens_tensor = torch.tensor([indexed_tokens])
+segments_tensors = torch.tensor([segments_ids])
+dummy_input = [tokens_tensor, segments_tensors]
+
+# Initializing the model with the torchscript flag
+# Flag set to True even though it is not necessary as this model does not have an LM Head.
+config = BertConfig(
+    vocab_size_or_config_json_file=32000,
+    hidden_size=768,
+    num_hidden_layers=12,
+    num_attention_heads=12,
+    intermediate_size=3072,
+    torchscript=True,
+)
+
+# Instantiating the model
+model = BertModel(config)
+
+# The model needs to be in evaluation mode
+model.eval()
+
+# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag
+model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
+
+# Creating the trace
+traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
+torch.jit.save(traced_model, "traced_bert.pt")
+```
+
+#### Loading a model
+
+This snippet shows how to load the `BertModel` that was previously saved to disk under the name `traced_bert.pt`.
+We are re-using the previously initialised `dummy_input`.
+
+```python
+loaded_model = torch.jit.load("traced_bert.pt")
+loaded_model.eval()
+
+all_encoder_layers, pooled_output = loaded_model(*dummy_input)
+```
+
+#### Using a traced model for inference
+
+Using the traced model for inference is as simple as using its `__call__` dunder method:
+
+```python
+traced_model(tokens_tensor, segments_tensors)
+```
+
+### Deploying HuggingFace TorchScript models on AWS using the Neuron SDK
+
+AWS introduced the [Amazon EC2 Inf1](https://aws.amazon.com/ec2/instance-types/inf1/)
+instance family for low cost, high performance machine learning inference in the cloud.
+The Inf1 instances are powered by the AWS Inferentia chip, a custom-built hardware accelerator,
+specializing in deep learning inferencing workloads.
+[AWS Neuron](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/#)
+is the SDK for Inferentia that supports tracing and optimizing transformers models for
+deployment on Inf1. The Neuron SDK provides:
+
+
+1. Easy-to-use API with one line of code change to trace and optimize a TorchScript model for inference in the cloud.
+2. Out of the box performance optimizations for [improved cost-performance](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/benchmark/>)
+3. Support for HuggingFace transformers models built with either [PyTorch](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/bert_tutorial/tutorial_pretrained_bert.html)
+   or [TensorFlow](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/tensorflow/huggingface_bert/huggingface_bert.html).
+
+#### Implications
+
+Transformers Models based on the [BERT (Bidirectional Encoder Representations from Transformers)](https://huggingface.co/docs/transformers/main/model_doc/bert)
+architecture, or its variants such as [distilBERT](https://huggingface.co/docs/transformers/main/model_doc/distilbert)
+ and [roBERTa](https://huggingface.co/docs/transformers/main/model_doc/roberta)
+ will run best on Inf1 for non-generative tasks such as Extractive Question Answering,
+ Sequence Classification, Token Classification. Alternatively, text generation
+tasks can be adapted to run on Inf1, according to this [AWS Neuron MarianMT tutorial](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/transformers-marianmt.html).
+More information about models that can be converted out of the box on Inferentia can be
+found in the [Model Architecture Fit section of the Neuron documentation](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/models/models-inferentia.html#models-inferentia).
+
+#### Dependencies
+
+Using AWS Neuron to convert models requires the following dependencies and environment:
+
+* A [Neuron SDK environment](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-frameworks/pytorch-neuron/index.html#installation-guide),
+  which comes pre-configured on [AWS Deep Learning AMI](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-inferentia-launching.html).
+
+#### Converting a Model for AWS Neuron
+
+Using the same script as in [Using TorchScript in Python](https://huggingface.co/docs/transformers/main/en/serialization#using-torchscript-in-python)
+to trace a "BertModel", you import `torch.neuron` framework extension to access
+the components of the Neuron SDK through a Python API.
+
+```python
+from transformers import BertModel, BertTokenizer, BertConfig
+import torch
+import torch.neuron
+```
+And only modify the tracing line of code
+
+from:
+
+```python
+torch.jit.trace(model, [tokens_tensor, segments_tensors])
+```
+
+to:
+
+```python
+torch.neuron.trace(model, [token_tensor, segments_tensors])
+```
+
+This change enables Neuron SDK to trace the model and optimize it to run in Inf1 instances.
+
+To learn more about AWS Neuron SDK features, tools, example tutorials and latest updates,
+please see the [AWS NeuronSDK documentation](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/index.html).

docs/source/en/tasks/semantic_segmentation.mdx

@@ -67,9 +67,9 @@ You'll also want to create a dictionary that maps a label id to a label class wh
 >>> import json
 >>> from huggingface_hub import cached_download, hf_hub_url
 
->>> repo_id = "huggingface/label-files"
+>>> repo_id = "datasets/huggingface/label-files"
 >>> filename = "ade20k-id2label.json"
->>> id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r"))
+>>> id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename)), "r"))
 >>> id2label = {int(k): v for k, v in id2label.items()}
 >>> label2id = {v: k for k, v in id2label.items()}
 >>> num_labels = len(id2label)

docs/source/en/testing.mdx

@@ -176,47 +176,6 @@ If you want to include only tests that include both patterns, `and` is to be use
 ```bash
 pytest -k "test and ada" tests/test_optimization.py
 ```
-### Run documentation tests 
-
-In order to test whether the documentation examples are correct, you should checkt that the `doctests` are passing. 
-As an example, let's use [`WhisperModel.forward`'s docstring](https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py#L1017-L1035): 
-
-```python 
-r"""
-Returns:
-
-Example:
-    ```python
-    >>> import torch
-    >>> from transformers import WhisperModel, WhisperFeatureExtractor
-    >>> from datasets import load_dataset
-
-    >>> model = WhisperModel.from_pretrained("openai/whisper-base")
-    >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base")
-    >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-    >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
-    >>> input_features = inputs.input_features
-    >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
-    >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
-    >>> list(last_hidden_state.shape)
-    [1, 2, 512]
-    ```"""
-
-```
-3 steps are required to debug the docstring examples : 
-1. In order to properly run the test, **an extra line has to be added** at the end of the docstring. This can be automatically done on any file using : 
-```bash 
-python utils/prepare_for_doc_test.py <path_to_file_or_dir>
-```
-
-2. Then, you can use the following line to automatically test every docstring example in the desired file : 
-```bash 
-pytest --doctest-modules <path_to_file_or_dir>
-```
-3. Once you are done debugging, you need to remove the extra line added in step **1.** by running the follwing : 
-```bash 
-python utils/prepare_for_doc_test.py <path_to_file_or_dir> --remove_new_line
-```
 
 ### Run only modified tests
 

docs/source/en/torchscript.mdx

@@ -1,225 +0,0 @@
-<!--Copyright 2022 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
-specific language governing permissions and limitations under the License.
--->
-
-# Export to TorchScript
-
-<Tip>
-
-This is the very beginning of our experiments with TorchScript and we are still
-exploring its capabilities with variable-input-size models. It is a focus of interest to
-us and we will deepen our analysis in upcoming releases, with more code examples, a more
-flexible implementation, and benchmarks comparing Python-based codes with compiled
-TorchScript.
-
-</Tip>
-
-According to the [TorchScript documentation](https://pytorch.org/docs/stable/jit.html):
-
-> TorchScript is a way to create serializable and optimizable models from PyTorch code.
-
-There are two PyTorch modules, [JIT and
-TRACE](https://pytorch.org/docs/stable/jit.html), that allow developers to export their
-models to be reused in other programs like efficiency-oriented C++ programs.
-
-We provide an interface that allows you to export 🤗 Transformers models to TorchScript
-so they can be reused in a different environment than PyTorch-based Python programs.
-Here, we explain how to export and use our models using TorchScript.
-
-Exporting a model requires two things:
-
-- model instantiation with the `torchscript` flag
-- a forward pass with dummy inputs
-
-These necessities imply several things developers should be careful about as detailed
-below.
-
-## TorchScript flag and tied weights
-
-The `torchscript` flag is necessary because most of the 🤗 Transformers language models
-have tied weights between their `Embedding` layer and their `Decoding` layer.
-TorchScript does not allow you to export models that have tied weights, so it is
-necessary to untie and clone the weights beforehand.
-
-Models instantiated with the `torchscript` flag have their `Embedding` layer and
-`Decoding` layer separated, which means that they should not be trained down the line.
-Training would desynchronize the two layers, leading to unexpected results.
-
-This is not the case for models that do not have a language model head, as those do not
-have tied weights. These models can be safely exported without the `torchscript` flag.
-
-## Dummy inputs and standard lengths
-
-The dummy inputs are used for a models forward pass. While the inputs' values are
-propagated through the layers, PyTorch keeps track of the different operations executed
-on each tensor. These recorded operations are then used to create the *trace* of the
-model.
-
-The trace is created relative to the inputs' dimensions. It is therefore constrained by
-the dimensions of the dummy input, and will not work for any other sequence length or
-batch size. When trying with a different size, the following error is raised:
-
-```
-`The expanded size of the tensor (3) must match the existing size (7) at non-singleton dimension 2`
-```
-
-We recommended you trace the model with a dummy input size at least as large as the
-largest input that will be fed to the model during inference. Padding can help fill the
-missing values. However, since the model is traced with a larger input size, the
-dimensions of the matrix will also be large, resulting in more calculations.
-
-Be careful of the total number of operations done on each input and follow the
-performance closely when exporting varying sequence-length models.
-
-## Using TorchScript in Python
-
-This section demonstrates how to save and load models as well as how to use the trace
-for inference.
-
-### Saving a model
-
-To export a `BertModel` with TorchScript, instantiate `BertModel` from the `BertConfig`
-class and then save it to disk under the filename `traced_bert.pt`:
-
-```python
-from transformers import BertModel, BertTokenizer, BertConfig
-import torch
-
-enc = BertTokenizer.from_pretrained("bert-base-uncased")
-
-# Tokenizing input text
-text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
-tokenized_text = enc.tokenize(text)
-
-# Masking one of the input tokens
-masked_index = 8
-tokenized_text[masked_index] = "[MASK]"
-indexed_tokens = enc.convert_tokens_to_ids(tokenized_text)
-segments_ids = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1]
-
-# Creating a dummy input
-tokens_tensor = torch.tensor([indexed_tokens])
-segments_tensors = torch.tensor([segments_ids])
-dummy_input = [tokens_tensor, segments_tensors]
-
-# Initializing the model with the torchscript flag
-# Flag set to True even though it is not necessary as this model does not have an LM Head.
-config = BertConfig(
-    vocab_size_or_config_json_file=32000,
-    hidden_size=768,
-    num_hidden_layers=12,
-    num_attention_heads=12,
-    intermediate_size=3072,
-    torchscript=True,
-)
-
-# Instantiating the model
-model = BertModel(config)
-
-# The model needs to be in evaluation mode
-model.eval()
-
-# If you are instantiating the model with *from_pretrained* you can also easily set the TorchScript flag
-model = BertModel.from_pretrained("bert-base-uncased", torchscript=True)
-
-# Creating the trace
-traced_model = torch.jit.trace(model, [tokens_tensor, segments_tensors])
-torch.jit.save(traced_model, "traced_bert.pt")
-```
-
-### Loading a model
-
-Now you can load the previously saved `BertModel`, `traced_bert.pt`, from disk and use
-it on the previously initialised `dummy_input`:
-
-```python
-loaded_model = torch.jit.load("traced_bert.pt")
-loaded_model.eval()
-
-all_encoder_layers, pooled_output = loaded_model(*dummy_input)
-```
-
-### Using a traced model for inference
-
-Use the traced model for inference by using its `__call__` dunder method:
-
-```python
-traced_model(tokens_tensor, segments_tensors)
-```
-
-## Deploy Hugging Face TorchScript models to AWS with the Neuron SDK
-
-AWS introduced the [Amazon EC2 Inf1](https://aws.amazon.com/ec2/instance-types/inf1/)
-instance family for low cost, high performance machine learning inference in the cloud.
-The Inf1 instances are powered by the AWS Inferentia chip, a custom-built hardware
-accelerator, specializing in deep learning inferencing workloads. [AWS
-Neuron](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/#) is the SDK for
-Inferentia that supports tracing and optimizing transformers models for deployment on
-Inf1. The Neuron SDK provides:
-
-
-1. Easy-to-use API with one line of code change to trace and optimize a TorchScript
-   model for inference in the cloud.
-2. Out of the box performance optimizations for [improved
-   cost-performance](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/benchmark/>).
-3. Support for Hugging Face transformers models built with either
-   [PyTorch](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/bert_tutorial/tutorial_pretrained_bert.html)
-   or
-   [TensorFlow](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/tensorflow/huggingface_bert/huggingface_bert.html).
-
-### Implications
-
-Transformers models based on the [BERT (Bidirectional Encoder Representations from
-Transformers)](https://huggingface.co/docs/transformers/main/model_doc/bert)
-architecture, or its variants such as
-[distilBERT](https://huggingface.co/docs/transformers/main/model_doc/distilbert) and
-[roBERTa](https://huggingface.co/docs/transformers/main/model_doc/roberta) run best on
-Inf1 for non-generative tasks such as extractive question answering, sequence
-classification, and token classification. However, text generation tasks can still be
-adapted to run on Inf1 according to this [AWS Neuron MarianMT
-tutorial](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/src/examples/pytorch/transformers-marianmt.html).
-More information about models that can be converted out of the box on Inferentia can be
-found in the [Model Architecture
-Fit](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/models/models-inferentia.html#models-inferentia)
-section of the Neuron documentation.
-
-### Dependencies
-
-Using AWS Neuron to convert models requires a [Neuron SDK
-environment](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/neuron-guide/neuron-frameworks/pytorch-neuron/index.html#installation-guide)
-which comes preconfigured on [AWS Deep Learning
-AMI](https://docs.aws.amazon.com/dlami/latest/devguide/tutorial-inferentia-launching.html).
-
-### Converting a model for AWS Neuron
-
-Convert a model for AWS NEURON using the same code from [Using TorchScript in
-Python](serialization#using-torchscript-in-python) to trace a `BertModel`. Import the
-`torch.neuron` framework extension to access the components of the Neuron SDK through a
-Python API:
-
-```python
-from transformers import BertModel, BertTokenizer, BertConfig
-import torch
-import torch.neuron
-```
-
-You only need to modify the following line:
-
-```diff
-- torch.jit.trace(model, [tokens_tensor, segments_tensors])
-+ torch.neuron.trace(model, [token_tensor, segments_tensors])
-```
-
-This enables the Neuron SDK to trace the model and optimize it for Inf1 instances.
-
-To learn more about AWS Neuron SDK features, tools, example tutorials and latest
-updates, please see the [AWS NeuronSDK
-documentation](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/index.html).

docs/source/es/create_a_model.mdx

@@ -119,7 +119,7 @@ Carga los atributos de tu configuración personalizada en el modelo de la siguie
 >>> model = DistilBertModel(my_config)
 ```
   
-Esto crea un modelo con valores aleatorios, en lugar de crearlo con los pesos del preentrenamiento, por lo que no serás capaz de usar este modelo para nada útil hasta que no lo entrenes. El entrenamiento es un proceso costoso, tanto en cuestión de recursos como de tiempo, por lo que generalmente es mejor usar un modelo preentrenado para obtener mejores resultados más rápido, consumiendo una fracción de los recursos que un entrenamiento completo hubiera requerido. 
+Esto crea un modelo con valores aleatorios, en lugar de crearlo con los pesos del preentramiento, por lo que no serás capaz de usar este modelo para nada útil hasta que no lo entrenes. El entrenamiento es un proceso costoso, tanto en cuestión de recursos como de tiempo, por lo que generalmente es mejor usar un modelo preentrenado para obtener mejores resultados más rápido, consumiendo una fracción de los recursos que un entrenamiento completo hubiera requerido. 
 
 Puedes crear un modelo preentrenado con [`~PreTrainedModel.from_pretrained`]:
 
@@ -127,7 +127,7 @@ Puedes crear un modelo preentrenado con [`~PreTrainedModel.from_pretrained`]:
 >>> model = DistilBertModel.from_pretrained("distilbert-base-uncased")
 ```
 
-Cuando cargues tus pesos del preentrenamiento, el modelo por defecto se carga automáticamente si nos lo proporciona 🤗 Transformers. Sin embargo, siempre puedes reemplazar (todos o algunos de) los atributos del modelo por defecto por los tuyos:
+Cuando cargues tus pesos del preentramiento, el modelo por defecto se carga automáticamente si nos lo proporciona 🤗 Transformers. Sin embargo, siempre puedes reemplazar (todos o algunos de) los atributos del modelo por defecto por los tuyos:
 
 ```py
 >>> model = DistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
@@ -144,7 +144,7 @@ Carga los atributos de tu configuración personalizada en el modelo de la siguie
 >>> tf_model = TFDistilBertModel(my_config)
 ```
 
-Esto crea un modelo con valores aleatorios, en lugar de crearlo con los pesos del preentrenamiento, por lo que no serás capaz de usar este modelo para nada útil hasta que no lo entrenes. El entrenamiento es un proceso costoso, tanto en cuestión de recursos como de tiempo, por lo que generalmente es mejor usar un modelo preentrenado para obtener mejores resultados más rápido, consumiendo solo una fracción de los recursos que un entrenamiento completo hubiera requerido. 
+Esto crea un modelo con valores aleatorios, en lugar de crearlo con los pesos del preentramiento, por lo que no serás capaz de usar este modelo para nada útil hasta que no lo entrenes. El entrenamiento es un proceso costoso, tanto en cuestión de recursos como de tiempo, por lo que generalmente es mejor usar un modelo preentrenado para obtener mejores resultados más rápido, consumiendo solo una fracción de los recursos que un entrenamiento completo hubiera requerido. 
 
 Puedes crear un modelo preentrenado con [`~TFPreTrainedModel.from_pretrained`]:
 
@@ -152,7 +152,7 @@ Puedes crear un modelo preentrenado con [`~TFPreTrainedModel.from_pretrained`]:
 >>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased")
 ```
 
-Cuando cargues tus pesos del preentrenamiento, el modelo por defecto se carga automáticamente si este nos lo proporciona 🤗 Transformers. Sin embargo, siempre puedes reemplazar (todos o algunos de) los atributos del modelo por defecto por los tuyos:
+Cuando cargues tus pesos del preentramiento, el modelo por defecto se carga automáticamente si este nos lo proporciona 🤗 Transformers. Sin embargo, siempre puedes reemplazar (todos o algunos de) los atributos del modelo por defecto por los tuyos:
 
 ```py
 >>> tf_model = TFDistilBertModel.from_pretrained("distilbert-base-uncased", config=my_config)
@@ -217,7 +217,7 @@ Ambos *tokenizers* son compatibles con los métodos comunes, como los de encodif
 
 <Tip warning={true}>
 
-No todos los modelos son compatibles con un *tokenizer* rápido. Échale un vistazo a esta [tabla](index#supported-frameworks) para comprobar si un modelo específico es compatible con un *tokenizer* rápido.
+No todos los modelos son compatibles con un *tokenizer* rápido. Échale un vistazo a esta [tabla](index#supported-frameworks) para comprobar si un modelo en específico es compatible con un *tokenizer* rápido.
 
 </Tip>
 
@@ -229,7 +229,7 @@ Si has entrenado tu propio *tokenizer*, puedes crear uno desde tu archivo de “
 >>> my_tokenizer = DistilBertTokenizer(vocab_file="my_vocab_file.txt", do_lower_case=False, padding_side="left")
 ```
 
-Es importante recordar que los vocabularios que provienen de un *tokenizer* personalizado serán diferentes a los vocabularios generados por el *tokenizer* de un modelo preentrenado. Debes usar el vocabulario de un *tokenizer* preentrenado si vas a usar un modelo preentrenado, de lo contrario las entradas no tendrán sentido. Crea un *tokenizer* con el vocabulario de un modelo preentrenado usando la clase [`DistilBertTokenizer`]:
+Es importante recordar que los vocabularios que provienen de un *tokenizer* personalizado serán diferentes a los vocabularios generados por el *tokenizer* de un modelo preentrenado. Debes usar el vocabulario de un *tokenizer* preentrenado si vas a usar un modelo preentrenado, de lo contrario las entradas no tendrán sentido. Crea un *tokenizer* con el vocabulario de un modelo preentrenado usado la clase [`DistilBertTokenizer`]:
 
 
 ```py
@@ -249,7 +249,7 @@ Crea un *tokenizer* rápido con la clase [`DistilBertTokenizerFast`]:
 
 <Tip>
 
-Por defecto, el [`AutoTokenizer`] intentará cargar un *tokenizer* rápido. Puedes desactivar este comportamiento cambiando el parámetro `use_fast=False` de `from_pretrained`.
+Por defecto, el [`AutoTokenizer`] intentará cargar un *tokenizer* rápido. Puedes desactivar este compartimiento cambiando el parámetro `use_fast=False` de `from_pretrained`.
 
 
 </Tip>
@@ -258,7 +258,7 @@ Por defecto, el [`AutoTokenizer`] intentará cargar un *tokenizer* rápido. Pued
 
 Un extractor de características procesa entradas de audio e imagen. Hereda de la clase base [`~feature_extraction_utils.FeatureExtractionMixin`] y también puede heredar de la clase [`ImageFeatureExtractionMixin`] para el procesamiento de características de las imágenes o de la clase [`SequenceFeatureExtractor`] para el procesamiento de entradas de audio.
 
-Dependiendo de si trabajas en una tarea de audio o de video, puedes crear un extractor de características asociado al modelo que estés usando. Por ejemplo, podrías crear un [`ViTFeatureExtractor`] por defecto si estás usando [ViT](model_doc/vit) para clasificación de imágenes:
+Dependiendo de si trabajas en una tarea de audio o de video, puedes crear un extractor de características asociado al modelo que estes usando. Por ejemplo, podrías crear un [`ViTFeatureExtractor`] por defecto si estas usando [ViT](model_doc/vit) para clasificación de imágenes:
 
 ```py
 >>> from transformers import ViTFeatureExtractor

docs/source/es/pipeline_tutorial.mdx

@@ -65,7 +65,7 @@ Cualquier parámetro adicional para tu tarea también se puede incluir en el [`p
 
 ### Selecciona un modelo y un tokenizador
 
-El [`pipeline`] acepta cualquier modelo del [Model Hub](https://huggingface.co/models). Hay etiquetas en el Model Hub que te permiten filtrar por el modelo que te gustaría utilizar para tu tarea. Una vez que hayas elegido un modelo apropiado, cárgalo con la clase `AutoModelFor` y [`AutoTokenizer`] correspondientes. Por ejemplo, carga la clase [`AutoModelForCausalLM`] para una tarea de modelado de lenguaje causal:
+El [`pipeline`] acepta cualquier modelo del [Model Hub](https://huggingface.co/models). Hay etiquetas en el Model Hub que te permiten filtrar por el modelo que te gustaría utilizar para tu tarea. Una vez que hayas elegido un modelo apropiado, cárgalo con la clase `AutoModelFor` y [`AutoTokenizer'] correspondientes. Por ejemplo, carga la clase [`AutoModelForCausalLM`] para una tarea de modelado de lenguaje causal:
 
 ```py
 >>> from transformers import AutoTokenizer, AutoModelForCausalLM

docs/source/es/preprocessing.mdx

@@ -494,7 +494,7 @@ tres argumentos que necesitas conocer para ello son `padding`, `truncation` y `m
 
 - `padding` controla el aplicarme padding al texto. Puede ser un booleano o una cadena que debe ser:
 
-  - `True` o `'longest'` para aplicar el pad hasta la secuencia más larga del batch (no apliques el padding si sólo le proporcionas 
+  - `True` o `'longest'` para aplicar el pad hasta la secuencia más larga del batch (no apliques el padding si sólo se proporcionas 
   una sola secuencia).
   - `'max_length'` para aplicar el pad hasta la longitud especificada por el argumento `max_length` o la longitud máxima aceptada 
   por el modelo si no le proporcionas `longitud_máxima` (`longitud_máxima=None`). Si sólo le proporcionas una única secuencia 
@@ -523,7 +523,7 @@ padding/truncamiento a `longitud_máxima` se desactiva.
 
 A continuación te mostramos en una tabla que resume la forma recomendada de configurar el padding y el truncamiento. Si utilizas un par de secuencias de entrada en 
 algunos de los siguientes ejemplos, puedes sustituir `truncation=True` por una `STRATEGY` seleccionada en 
-`['only_first', 'only_second', 'longest_first']`, es decir, `truncation='only_second'` o `truncation= 'longest_first'` para controlar cómo se truncan ambas secuencias del par como se ha detallado anteriormente.
+`['only_first', 'only_second', 'longest_first']`, es decir, `truncation='only_second'` o `truncation= 'longest_first'` para controlar cómo se trunquen ambas secuencias del par como lo has detallado anteriormente.
 
 | Truncation                           | Padding                           | Instrucciones                                                                               |
 |--------------------------------------|-----------------------------------|---------------------------------------------------------------------------------------------|
@@ -539,7 +539,7 @@ algunos de los siguientes ejemplos, puedes sustituir `truncation=True` por una `
 |                                      | padding long max de input model   | `tokenizer(batch_sentences, padding='max_length', truncation=True)` or                 |
 |                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                |
 |                                      | padding a una long especifica     | Not possible                                                                                |
-| truncation a una long especifica      | no padding                        | `tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
+| truncationa una long especifica      | no padding                        | `tokenizer(batch_sentences, truncation=True, max_length=42)` or                        |
 |                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                       |
 |                                      | padding secuencia max del batch   | `tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` or          |
 |                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`         |

docs/source/es/run_scripts.mdx

@@ -123,7 +123,7 @@ python examples/tensorflow/summarization/run_summarization.py  \
 [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) admite un entrenamiento distribuido y de precisión mixta, lo que significa que también puedes usarlo en un script. Para habilitar ambas características:
 
 - Agrega el argumento `fp16` para habilitar la precisión mixta.
-- Establece la cantidad de GPU que se usará con el argumento `nproc_per_node`.
+- Establece la cantidad de GPU que se usarás con el argumento `nproc_per_node`.
 
 ```bash
 python -m torch.distributed.launch \
@@ -200,7 +200,7 @@ En lugar del script `run_summarization.py`, debes usar el script `run_summarizat
 accelerate config
 ```
 
-Prueba tu configuración para asegurarte que está configurada correctamente:
+Prueba tu configuración para asegurarte que esta configurada correctamente:
 
 ```bash
 accelerate test
@@ -344,4 +344,4 @@ python examples/pytorch/summarization/run_summarization.py
     --per_device_eval_batch_size=4 \
     --overwrite_output_dir \
     --predict_with_generate
-```
+```

docs/source/es/training.mdx

@@ -39,7 +39,7 @@ Comienza cargando el dataset de [Yelp Reviews](https://huggingface.co/datasets/y
  'text': 'My expectations for McDonalds are t rarely high. But for one to still fail so spectacularly...that takes something special!\\nThe cashier took my friends\'s order, then promptly ignored me. I had to force myself in front of a cashier who opened his register to wait on the person BEHIND me. I waited over five minutes for a gigantic order that included precisely one kid\'s meal. After watching two people who ordered after me be handed their food, I asked where mine was. The manager started yelling at the cashiers for \\"serving off their orders\\" when they didn\'t have their food. But neither cashier was anywhere near those controls, and the manager was the one serving food to customers and clearing the boards.\\nThe manager was rude when giving me my order. She didn\'t make sure that I had everything ON MY RECEIPT, and never even had the decency to apologize that I felt I was getting poor service.\\nI\'ve eaten at various McDonalds restaurants for over 30 years. I\'ve worked at more than one location. I expect bad days, bad moods, and the occasional mistake. But I have yet to have a decent experience at this store. It will remain a place I avoid unless someone in my party needs to avoid illness from low blood sugar. Perhaps I should go back to the racially biased service of Steak n Shake instead!'}
 ```
 
-Como ya sabes, necesitas un tokenizador para procesar el texto e incluir una estrategia para el padding y el truncamiento para manejar cualquier longitud de secuencia variable. Para procesar tu dataset en un solo paso, utiliza el método de 🤗 Datasets map para aplicar una función de preprocesamiento sobre todo el dataset:
+Como ya sabes, necesitas un tokenizador para procesar el texto e incluir una estrategia para el padding y el truncamiento para manejar cualquier longitud de secuencia variable. Para procesar tu dataset en un solo paso, utiliza el método de 🤗 Datasets mappara aplicar una función de preprocesamiento sobre todo el dataset:
 
 ```py
 >>> from transformers import AutoTokenizer
@@ -80,7 +80,7 @@ Comienza cargando tu modelo y especifica el número de labels previstas. A parti
 <Tip>
 
 Verás una advertencia acerca de que algunos de los pesos pre-entrenados no están siendo utilizados y que algunos pesos están siendo inicializados al azar. No te preocupes, esto es completamente normal.
-El head/cabezal pre-entrenado del modelo BERT se descarta y se sustituye por un head de clasificación inicializado aleatoriamente. Puedes aplicar fine-tuning a este nuevo head del modelo en tu tarea de clasificación de secuencias haciendo transfer learning del modelo pre-entrenado.
+No te preocupes, esto es completamente normal. El head/cabezal pre-entrenado del modelo BERT se descarta y se sustituye por un head de clasificación inicializado aleatoriamente. Puedes aplicar fine-tuning a este nuevo head del modelo en tu tarea de clasificación de secuencias haciendo transfer learning del modelo pre-entrenado.
 
 </Tip>
 

docs/source/it/_toctree.yml

@@ -36,10 +36,3 @@
   - local: debugging
     title: Debugging
   title: Guide pratiche
-- sections:
-  - local: add_new_pipeline
-    title: Come aggiungere una pipeline a 🤗 Transformers?
-  - local: add_new_model
-    title: Come aggiungere un modello a 🤗 Transformers?
-  title: Guide How-to
-  

docs/source/it/add_new_model.mdx

@@ -1,775 +0,0 @@
-<!--Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
--->
-
-# Come aggiungere un modello a 🤗 Transformers?
-
-Aggiungere un nuovo modello é spesso difficile e richiede una profonda conoscenza della libreria 🤗 Transformers e anche 
-della repository originale del modello. A Hugging Face cerchiamo di dare alla community sempre piú poteri per aggiungere 
-modelli independentemente. Quindi, per alcuni nuovi modelli che la community vuole aggiungere a 🤗 Transformers, abbiamo 
-creato una specifica *call-for-model-addition* che spiega passo dopo passo come aggiungere il modello richiesto. Con 
-questo *call-for-model-addition* vogliamo insegnare a volenterosi e esperti collaboratori della community come implementare
-un modello in 🤗 Transformers.
-
-Se questo é qualcosa che può interessarvi, siete liberi di controllare l'attuale “calls-for-model-addition” [qui](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model/open_model_proposals/README.md)
-e contattarci. 
-
-Se il modello sarà selezionato, allora potrete lavorare insieme a un membro di Hugging Face per integrare il modello in 🤗
-Transformers. Così facendo, ci guadagnerai in una comprensione totale, sia teorica che pratica, del modello proposto. Inoltre, 
-sarai l'artefice di un importante contributo open-source a 🤗 Transformers. Durante l'implementazione avrai l'opportunità di:
-
-- ottenere più comprensione delle best practices in open-source
-- capire i principi di design di una della librerie NLP più popolari 
-- capire come efficientemente testare complessi modelli NLP
-- capire come integrare utilit Python come `black`, `isort`, `make fix-copies` in una libreria per garantire sempre di avere un codice leggibile e pulito 
-
-Siamo anche contenti se vuoi aggiungere un modello che non può essere trovato nella cartella “calls-for-model-addition”. 
-Le seguenti sezioni spiegano in dettaglio come aggiungere un nuovo modello. Può anche essere molto utile controllare modelli
-già aggiunti [qui](https://github.com/huggingface/transformers/pulls?q=is%3Apr+label%3A%22PR+for+Model+Addition%22+is%3Aclosed),
-per capire se richiamano il modello che vorreste aggiungere. 
-
-Per cominciare, vediamo una panoramica general della libreria Transformers.
-
-## Panoramica generale su 🤗 Transformers
-
-Prima di tutto, vediamo in generale 🤗 Transformers. 🤗 Transformers é una libreria molto strutturata, quindi
-puà essere che a volte ci sia un disaccordo con alcune filosofie della libreria o scelte di design. Dalla nostra esperienza, 
-tuttavia, abbiamo trovato che le scelte fondamentali di design della libreria sono cruciali per usare 🤗 Transformers efficacemente
-su larga scala, mantenendo i costi a un livello accettabile.  
-
-Un buon primo punto di partenza per capire al meglio la libreria é leggere la [documentazione sulla nostra filosofia](filosofia)
-Da qui, ci sono alcune scelte sul modo di lavorare che cerchiamo di applicare a tutti i modelli:
-
-- La composizione é generalmente favorita sulla sovra-astrazione
-- Duplicare il codice non é sempre male, soprattutto se migliora notevolmente la leggibilità e accessibilità del modello
-- Tutti i files creati per il nuovo modello devono il piu possibile "compatti". Questo vuol dire che quando qualcuno leggerá il codice 
-di uno specifico modello, potrá vedere solo il corrispettivo file `modeling_....py` senza avere multiple dipendenze.
-
-
-La cosa piú importante, é che consideriamo la libreria non solo un mezzo per dare un prodotto, *per esempio* dare la possibilità 
-di usare BERT per inferenza, ma é anche il prodotto reale che noi vogliamo migliorare sempre più. Quindi, quando aggiungi 
-un modello, non sei solo la persona che userà il modello, ma rappresenti anche tutti coloro che leggeranno, 
-cercheranno di capire e modificare il tuo modello.
-
-Tenendo questi principi in mente, immergiamoci nel design generale della libreria.
-
-### Panoramica sui modelli
-
-Per aggiungere con successo un modello, é importante capire l'interazione tra il tuo modello e la sua configurazione,
-[`PreTrainedModel`], e [`PretrainedConfig`]. Per dare un esempio, chiameremo il modello da aggiungere a 🤗 Transformers  
-`BrandNewBert`.
-
-Diamo un'occhiata:
-
-<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_overview.png"/>
-
-Come potete vedere, ci basiamo sull'ereditarietà in 🤗 Transformers, tenendo però il livello di astrazione a un minimo 
-assoluto.  Non ci sono mai più di due livelli di astrazione per ogni modello nella libreria. `BrandNewBertModel` eredita 
-da `BrandNewBertPreTrainedModel` che, a sua volta, eredita da [`PreTrainedModel`] -  semplice no? 
-Come regola generale, vogliamo essere sicuri che un nuovo modello dipenda solo da [`PreTrainedModel`]. Le funzionalità
-importanti che sono automaticamente conferite a ogni nuovo modello sono [`~PreTrainedModel.from_pretrained`]
-e [`~PreTrainedModel.save_pretrained`], che sono usate per serializzazione e deserializzazione. Tutte le altre importanti 
-funzionalità, come ad esempio `BrandNewBertModel.forward` devono essere definite completamente nel nuovo script
-`modeling_brand_new_bert.py`. Inoltre, vogliamo essere sicuri che un modello con uno specifico head layer, come 
-`BrandNewBertForMaskedLM` non erediti da `BrandNewBertModel`, ma piuttosto usi `BrandNewBertModel`
-come componente che può essere chiamata nel passaggio forward per mantenere il livello di astrazione basso. Ogni 
-nuovo modello richieste una classe di configurazione, chiamata `BrandNewBertConfig`. Questa configurazione é sempre 
-mantenuta come un attributo in [`PreTrainedModel`], e quindi può essere accessibile tramite l'attributo `config` 
-per tutte le classi che ereditano da `BrandNewBertPreTrainedModel`:
-
-```python
-model = BrandNewBertModel.from_pretrained("brandy/brand_new_bert")
-model.config  # il modello ha accesso al suo config
-```
-
-Analogamente al modello, la configurazione eredita le funzionalità base di serializzazione e deserializzazione da 
-[`PretrainedConfig`]. É da notare che la configurazione e il modello sono sempre serializzati in due formati differenti - 
-il modello é serializzato in un file *pytorch_model.bin* mentre la configurazione con *config.json*. Chiamando 
-[`~PreTrainedModel.save_pretrained`] automaticamente chiamerà [`~PretrainedConfig.save_pretrained`], cosicché sia il 
-modello che la configurazione siano salvati.
-
-
-### Stile per il codice
-
-Quando codifichi un nuovo modello, tieni presente che Transformers ha una sua struttura di fondo come libreria, perciò 
-ci sono alcuni fatti da considerare su come scrivere un codice :-)
-
-1. Il forward pass del tuo modello dev'essere scritto completamente nel file del modello, mentre dev'essere indipendente 
-   da altri modelli nella libreria. Se vuoi riutilizzare un blocco di codice da un altro modello, copia e incolla il codice con un commento `# Copied from` in cima al codice (guarda [qui](https://github.com/huggingface/transformers/blob/v4.17.0/src/transformers/models/roberta/modeling_roberta.py#L160)
-   per un ottimo esempio).
-2. Il codice dev'essere interamente comprensibile, anche da persone che non parlano in inglese. Questo significa che le 
-   variabili devono avere un nome descrittivo e bisogna evitare abbreviazioni. Per esempio, `activation` é molto meglio 
-   che `act`. Le variabili con una lettera sono da evitare fortemente, almeno che non sia per un indce in un for loop.
-3. Generamente é meglio avere un codice esplicito e piú lungo che un codice corto e magico.
-4. Evita di subclassare `nn.Sequential` in Pytorch, puoi subclassare `nn.Module` e scrivere il forward pass, cosicché 
-   chiunque può effettuare debug sul tuo codice, aggiungendo print o breaking points. 
-5. La tua function-signature dev'essere type-annoted. Per il resto, é meglio preferire variabili con un nome accettabile 
-   piuttosto che annotazioni per aumentare la comprensione e leggibilità del codice.
-
-### Panoramica sui tokenizers
-
-Questa sezione sarà creata al piu presto :-(
-
-## Aggiungere un modello a 🤗 Transformers passo dopo passo 
-
-Ci sono differenti modi per aggiungere un modello a Hugging Face. Qui trovi una lista di blog posts da parte della community su come aggiungere un modello:
-
-1. [Aggiungere GPT2](https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28) scritto da [Thomas](https://huggingface.co/thomwolf)
-2. [Aggiungere WMT19 MT](https://huggingface.co/blog/porting-fsmt) scritto da [Stas](https://huggingface.co/stas)
-
-Per esperienza, possiamo dirti che quando si aggiunge un modello é meglio tenere a mente le seguenti considerazioni:
-
--  Non sfondare una porta giá aperta! La maggior parte del codice che aggiungerai per un nuovo modello 🤗 Transformers
-  esiste già da qualche parte in 🤗 Transformers. Prendi un po' di tempo per trovare codici simili in modelli e tokenizers esistenti e fare un copia-incolla. Ricorda che [grep](https://www.gnu.org/software/grep/) e [rg](https://github.com/BurntSushi/ripgrep) sono tuoi buoni amici. Inoltre, ricorda che puó essere molto probabile che il tokenizer per il tuo modello sia basato sull'implementazione di un altro modello, e il codice del tuo modello stesso su un altro ancora. *Per esempio* il modello FSMT é basato su BART, mentre il tokenizer di FSMT é basato su XLM.
--  Ricorda che qui é piu una sfida ingegneristica che scientifica. Spendi piú tempo per create un efficiente ambiente di debugging piuttosto che cercare di capire tutti gli aspetti teorici dell'articolo del modello.
--  Chiedi aiuto se sei in panne! I modelli sono la parte principale di 🤗 Transformers, perciò qui a Hugging Face siamo più che contenti di aiutarti in ogni passo per aggiungere il tuo modello. Non esitare a chiedere se vedi che non riesci a progredire.
-
-Di seguito, diamo una ricetta generale per aiutare a portare un modello in 🤗 Transformers.
-
-La lista seguente é un sommario di tutto quello che é stato fatto per aggiungere un modello, e può essere usata come To-Do List:
-
--  1. ☐ (Opzionale) Capire gli aspetti teorici del modello
--  2. ☐ Preparare l'ambiente dev per transformers
--  3. ☐ Preparare l'ambiente debugging della repository originale 
--  4. ☐ Create uno script che gestisca con successo il forward pass usando la repository originale e checkpoint 
--  5. ☐ Aggiungere con successo lo scheletro del modello a Transformers
--  6. ☐ Convertire i checkpoint original a Transformers checkpoint
--  7. ☐ Effettuare con successo la forward pass in Transformers, di modo che dia un output identico al checkpoint originale 
--  8. ☐ Finire i tests per il modello in Transformers
--  9. ☐ Aggiungere con successo Tokenizer in Transformers
--  10. ☐ Testare e provare gli integration tests da capo a fine
--  11. ☐ Completare i docs
--  12. ☐ Caricare i moedl weights all'hub
--  13. ☐ Sottomettere una pull request
--  14. ☐ (Opzionale) Aggiungere un notebook con una demo
-
-Per cominciare di solito consigliamo `BrandNewBert`, partendo dalla teoria, di modo da avere una buona comprensione della teoria generale. TUttavia, se preferisci imparare l'aspetto teorico del modello mentre *lavori* sul modello é ok immergersi direttamente nel codice di `BrandNewBert`. Questa opzione puó essere buona se le tue skills ingegneristiche sono meglio che quelle teoriche, o se il paper `BrandNewBert` ti dá problemi, o se semplicemente ti piace programmare piú che leggere articoli scientifici.
-
-### 1. (Opzionale) Aspetti teorici di BrandNewBert 
-
-Allora con calma, prendi un po' di tempo per leggere l'articolo su *BrandNewBert* . Sicuramente, alcune sezioni dell'articolo sono molto complesse, ma non preoccuparti! L'obiettivo non é avere una compresione immensa della teoria alla base, ma estrarre le informazioni necessarie per re-implementare con successo il modello in 🤗 Transformers. Quindi, non impazzire sugli aspetti teorici, ma piuttosto focalizzati su quelli pratici, ossia:
-
-- Che tipo di modello é *brand_new_bert*? É solo un encoder in stile BERT? O tipo decoder come GPT2? O encoder e decoder stile BART? Dai un'occhiata a [model_summary](model_summary) se non sei famigliare con le differenze tra questi modelli 
-- Quali sono le applicazioni di *brand_new_bert*? Classificazione di testo? Generazione di testo? O per tasks del genere seq2seq? 
-- Quali sono le nuove aggiunte al modello che lo rendono diverso da BERT/GPT-2/BART? 
-- Quali modelli estistenti in [🤗 Transformers models](https://huggingface.co/transformers/#contents) sono molto simili a *brand_new_bert*?
-- Che tipo di tokenizer si usa in questo caso? Un sentencepiece tokenizer? O un word piece tokenizer? Il tokenizer é lo stesso di BERT o BART? 
-
-Una volta che senti che hai avuto una bella overview dell'architettura del modello, puoi scrivere senza problemi al team di Hugging Face per ogni domanda che tu hai. Questo puó includere domande sull'architettura del modello, o sull'attention layer, etc. Saremo molto felici di aiutarti :) 
-
-
-### 2. Prepare il tuo ambiente
-
-1. Forka la [repository](https://github.com/huggingface/transformers) cliccando sul tasto ‘Fork' nella pagina della repository. Questo crea una copia del codice nel tuo account GitHub 
-
-2. Clona il tuo fork `transfomers` sul tuo dico locale, e aggiungi la repository base come remota:
-
-```bash
-git clone https://github.com/[your Github handle]/transformers.git
-cd transformers
-git remote add upstream https://github.com/huggingface/transformers.git
-```
-
-
-3. Crea un ambiente di sviluppo, per esempio tramite questo comando:
-
-```bash
-python -m venv .env
-source .env/bin/activate
-pip install -e ".[dev]"
-```
-
-quindi torna alla directory principale: 
-
-```bash
-cd ..
-```
-
-
-4. Attenzione, raccomandiamo di aggiungere la versione di PyTorch di *brand_new_bert* a Transfomers. Per installare PyTorch, basta seguire queste istruzioni https://pytorch.org/get-started/locally/.
-
-**Nota bene:** Non c'é bisogno di installare o avere installato CUDA. Il nuovo modello può funzionare senza problemi su una CPU.
-
-
-5. Per trasferire *brand_new_bert* To port *brand_new_bert* avrai bisogno anche accesso alla sua repository originale:
-
-```bash
-git clone https://github.com/org_that_created_brand_new_bert_org/brand_new_bert.git 
-cd brand_new_bert
-pip install -e .
-```
-
-Ok, ora hai un ambiente di sviluppo per portare *brand_new_bert* in 🤗 Transformers.
-
-
-### 3.-4. Provare un pretrained checkpoint usando la repo originale 
-
-Per cominciare, comincerai a lavorare sulla repo originale di *brand_new_bert*. Come spesso accade, l'implementazione originale é molto sullo stile "ricerca". Questo significa che a volte la documentazione non é al top, magari manca qualche cosa e il codice puó essere difficile da capire. Tuttavia, questa é e dev'essere la motivazione per reimplementare *brand_new_bert*. In Hugging Face, uno degli obiettivi principali é di *mettere le persone sulle spalle dei giganti*, il che si traduce, in questo contesto, di prendere un modello funzionante e riscriverlo e renderlo il piú possibile **accessibile, user-friendly, e leggibile**. Questa é la top motivazione per re-implementare modelli in 🤗 Transformers - cercare di creare nuove complesse tecnologie NLP accessibili a **chiunque**. 
-
-Riuscire a far girare il modello pretrained originale dalla repository ufficiale é spesso il passo **piu arduo**. Dalla nostra esperienza, é molto importante spendere un p' di tempo per diventare familiari con il codice base originale. Come test, prova a capire i seguenti punti:
-
-- Dove si trovano i pretrained weights? 
-- Come caricare i pretrained weights nel modello corrispondente? 
-- Come girare un tokenizer independentemente dal modello? 
-- Prova a tracciare un singolo forward pass, cosicché potrai sapere che classi e funzioni sono richieste per un semplice forward pass. Di solito, dovrai reimplementare queste funzioni e basta 
-- Prova a localizzare i componenti importanti del modello: Dove si trova la classe del modello? Ci sono sotto classi nel modello *per esempio* EngoderModel, DecoderMOdel? Dove si trova il self-attention layer? Ci sono molteplici differenti layer di attention, *per esempio * *self-attention*, *cross-attention*...?
-- Come puoi fare debug sul modello nell'ambiente originale della repo? Devi aggiungere dei *print* o puoi usare *ipdb* come debugger interattivo, o vabene anche un IDE efficiente per debug come PyCharm?
-
-É molto importante che prima di cominciare a trasferire il modello nuovo tu spenda tempo a fare debug del codice originale in maniera **efficiente**! Inoltre, ricorda che tutta la library é open-soruce, quindi non temere di aprire issue o fare una pull request nella repo originale. Tutti coloro che mantengono la repository saranno piú che felici di avere qualcuno che guarda e gioca con i loro codici!
-
-A questo punto, sta a te decidere quale ambiente per debug vuoi usare. Noi consilgiamo di evitare setup con GPU, che potrebbero costare assai, lavorare su una CPU puó essere un ottimo punto di partenza per indagare la repository originale e per cominciare a scrivere il codice per 🤗 Transformers. Solo alla fine, quando il modello é stato portato con successo in  🤗 Transformers, allora si potrá verificare il suo funzionamento su GPU.
-
-In generale ci sono due possibili ambienti di debug per il testare il modello originale: 
-
-- [Jupyter notebooks](https://jupyter.org/) / [google colab](https://colab.research.google.com/notebooks/intro.ipynb)
-- Scripts locali in Python 
-
-Il vantaggio dei Jupyter notebooks é la possibilità di eseguire cella per cella, il che può essere utile per decomporre tutte le componenti logiche, cosi da a vere un ciclo di debug più rapido, siccome si possono salvare i risultati da steps intermedi. Inoltre, i notebooks spesso sono molto facili da condividere con altri contributors, il che può essere molto utile se vuoi chiedere aiuto al team di Hugging Face. Se sei famigliare con Jupyter notebooks allora racommandiamo di lavorare in questa maniera.
-
-Ovviamente se non siete abituati a lavorare con i notebook, questo può essere uno svantaggio nell'usare questa tecnologia, sprecando un sacco di tempo per setup e portare tutto al nuovo ambiente, siccome non potreste neanche usare dei tools di debug come `ipdb`. 
-
-Per ogni pratica code-base, é sempre meglio come primo step caricare un **piccolo** checkpoint pretrained e cercare di riprodurre un singolo forward pass usando un vettore fittizio di IDs fatti da numeri interi. Un esempio per uno script simile, in pseudocodice é:
-
-```python
-model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
-input_ids = [0, 4, 5, 2, 3, 7, 9]  # vector of input ids
-original_output = model.predict(input_ids)
-```
-
-Per quanto riguarda la strategia di debugging, si può scegliere tra:
-
-- Decomporre il modello originario in piccole componenenti e testare ognuna di esse 
-- Decomporre il modello originario nel *tokenizer* originale e nel *modello* originale, testare un forward pass su questi, 
-e usare dei print statement o breakpoints intermedi per verificare
-
-Ancora una volta, siete liberi di scegliere quale strategia sia ottimale per voi. Spesso una strategia é piu 
-avvantaggiosa di un'altra, ma tutto dipende dall'code-base originario.
-
-Se il code-base vi permette di decomporre il modello in piccole sub-componenenti, *per esempio* se il code-base 
-originario può essere facilmente testato in eager mode, allora vale la pena effettuare un debugging di questo genere. 
-Ricordate che ci sono dei vantaggi nel decidere di prendere la strada piu impegnativa sin da subito: 
-
-- negli stage piu finali, quando bisognerà comparare il modello originario all'implementazione in Hugging Face, potrete verificare
-automaticamente ogni componente, individualmente, di modo che ci sia una corrispondenza 1:1
-- avrete l'opportunità di decomporre un problema molto grande in piccoli passi, così da strutturare meglio il vostro lavoro
-- separare il modello in componenti logiche vi aiuterà ad avere un'ottima overview sul design del modello, quindi una migliore 
-comprensione del modello stesso 
-- verso gli stage finali i test fatti componente per componente vi aiuterà ad essere sicuri di non andare avanti e indietro
-nell'implementazione, così da continuare la modifica del codice senza interruzione
-
-Un ottimo esempio di come questo può essere fatto é dato da [Lysandre](https://gist.github.com/LysandreJik/db4c948f6b4483960de5cbac598ad4ed) 
-per il modello ELECTRA
-
-Tuttavia, se il code-base originale é molto complesso o le componenti intermedie possono essere testate solo in tramite 
-compilazione, potrebbe richiedere parecchio tempo o addirittura essere impossibile separare il modello in piccole sotto-componenti. 
-Un buon esempio é [MeshTensorFlow di T5](https://github.com/tensorflow/mesh/tree/master/mesh_tensorflow). Questa libreria 
-é molto complessa e non offre un metodo semplice di decomposizione in sotto-componenti. Per simili librerie, potrete fare 
-affidamento ai print statements.
-
-In ogni caso, indipendentemente da quale strategia scegliete, la procedura raccomandata é di cominciare a fare debug dal 
-primo layer al layer finale. 
-É consigliato recuperare gli output dai layers, tramite print o sotto-componenti, nel seguente ordine:
-
-1. Recuperare gli IDs di input dati al modello
-2. Recuperare i word embeddings
-3. Recuperare l'input del primo Transformer layer 
-4. Recuperare l'output del primo Transformer layer 
-5. Recuperare l'output dei seguenti `n - 1` Transformer layers
-6. Recuperare l'output dell'intero BrandNewBert Model
-
-Gli IDs in input dovrebbero essere un arrary di interi, *per esempio* `input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]`
-
-Gli output dei seguenti layer di solito dovrebbero essere degli array di float multi-dimensionali come questo:
-
-```
-[[
- [-0.1465, -0.6501,  0.1993,  ...,  0.1451,  0.3430,  0.6024],
- [-0.4417, -0.5920,  0.3450,  ..., -0.3062,  0.6182,  0.7132],
- [-0.5009, -0.7122,  0.4548,  ..., -0.3662,  0.6091,  0.7648],
- ...,
- [-0.5613, -0.6332,  0.4324,  ..., -0.3792,  0.7372,  0.9288],
- [-0.5416, -0.6345,  0.4180,  ..., -0.3564,  0.6992,  0.9191],
- [-0.5334, -0.6403,  0.4271,  ..., -0.3339,  0.6533,  0.8694]]],
-```
-
-Ci aspettiamo che ogni modello aggiunto a 🤗 Transformers passi con successo un paio di test d'integrazione. Questo 
-significa che il modello originale e la sua implementazione in 🤗 Transformers abbiano lo stesso output con una precisione 
-di 0.001! Siccome é normale che lo stesso esatto modello, scritto in librerie diverse, possa dare output leggermente 
-diversi, la tolleranza accettata é 1e-3 (0.001). Ricordate che i due modelli devono dare output quasi identici. Dunque, 
-é molto conveniente comparare gli output intermedi di 🤗 Transformers molteplici volte con gli output intermedi del 
-modello originale di *brand_new_bert*. Di seguito vi diamo alcuni consigli per avere un ambiente di debug il piu efficiente
-possibile:
-
-- Trovate la migliore strategia per fare debug dei risultati intermedi. Per esempio, é la repository originale scritta in PyTorch?
-Se si, molto probabilmente dovrete dedicare un po' di tempo per scrivere degli script piu lunghi, così da decomporre il 
-modello originale in piccole sotto-componenti, in modo da poter recuperare i valori intermedi. Oppure, la repo originale 
-é scritta in Tensorflow 1? Se é così dovrete fare affidamento ai print di Tensorflow [tf.print](https://www.tensorflow.org/api_docs/python/tf/print) 
-per avere i valori intermedi. Altro caso, la repo é scritta in Jax? Allora assicuratevi che il modello non sia in **jit** 
-quanto testate il foward pass, *per esempio* controllate [questo link](https://github.com/google/jax/issues/196). 
-- Usate i più piccoli pretrained checkpoint che potete trovare. Piu piccolo é il checkpoint, piu velocemente sarà il vostro 
-ciclo di debug. Non é efficiente avere un pretrained model così gigante che per il forward pass impieghi piu di 10 secondi. 
-Nel caso in cui i checkpoints siano molto grandi, e non si possa trovare di meglio, allora é buona consuetudine ricorrere
-a fare un dummy model nel nuovo ambiente, con weights inizializzati random e salvare quei weights per comprare la versione 🤗 Transformers 
-con il vostro modello
-- Accertatevi di usare la via piu semplice per chiamare il forward pass nella repo originale. Sarebbe opportuno trovare 
-la funzione originaria che chiami **solo** un singolo forward pass, *per esempio* questa funzione spesso viene chiamata 
-`predict`, `evaluate`, `forward` o `__call__`. Siate sicuri di non fare debug su una funzione che chiami `forward` molteplici 
-volte, *per esempio* per generare testo, come `autoregressive_sample`, `generate`.
-- Cercate di separare la tokenization dal forward pass del modello. Se la repo originaria mostra esempio dove potete dare 
-come input una stringa, provate a cercare dove nella forward call la stringa viene cambiata in input ids e cominciate il 
-debug da questo punto. Questo vi garantisce un ottimo punto di partenza per scrivere un piccolo script personale dove dare 
-gli input al modello, anziche delle stringhe in input. 
-- Assicuratevi che il debugging **non** sia in training mode. Spesso questo potra il modello a dare degli output random, per 
-via dei molteplici dropout layers. Assicuratevi che il forward pass nell'ambiente di debug sia **deterministico**, cosicche 
-i dropout non siano usati. Alternativamente, potete usare *transformers.utils.set_seed* se la vecchia e nuova implementazione 
-sono nello stesso framework.
-
-La seguente sezione vi da ulteriori dettagli e accorgimenti su come potete fare tutto questo per *brand_new_bert*.
-
-
-### 5.-14. Trasferire BrandNewBert in 🤗 Transformers
-
-Allora cominciamo ad aggiungere un nuovo codice in 🤗 Transformers. Andate nel vostro fork clone di 🤗 Transformers:
-
-
-```bash 
-cd transformers
-```
-
-Nel caso speciale in cui stiate aggiungendo un modello, la cui architettura sia identica a una di un modello già esistente,
-dovrete solo aggiugnere uno script di conversione, come descritto [qui](#write-a-conversion-script).
-In questo caso, potete riutilizzare l'intera architettura del modello gia esistente.
-
-Se questo non é il caso, cominciamo con il generare un nuovo modello. Avrete due opzioni:
-
-- `transformers-cli add-new-model-like` per aggiungere un nuovo modello come uno che gia esiste
-- `transformers-cli add-new-model` per aggiungere un nuovo modello da un nostro template (questo assomigliera a BERT o Bart, in base al modello che selezionerete)
-
-In entrambi i casi, l'output vi darà un questionario da riempire con informazioni basi sul modello. Il secondo comando richiede di installare
-un `cookiecutter` - maggiori informazioni [qui](https://github.com/huggingface/transformers/tree/main/templates/adding_a_new_model).
-
-**Aprire una Pull Request in main huggingface/transformers repo**
-
-Prime di cominciare ad adattare il codice automaticamente generato, aprite una nuova PR come "Work in progress (WIP)", 
-*per esempio* "[WIP] Aggiungere *brand_new_bert*", cosicché il team di Hugging Face possa lavorare al vostro fianco nell'
-integrare il modello in 🤗 Transformers.
-
-Questi sarebbero gli step generali da seguire:
-
-1. Creare un branch dal main branch con un nome descrittivo 
-
-```bash 
-git checkout -b add_brand_new_bert 
-```
-
-2. Commit del codice automaticamente generato 
-
-```bash 
-git add . 
-git commit 
-```
-
-3. Fare fetch e rebase del main esistente
-
-```bash 
-git fetch upstream 
-git rebase upstream/main 
-```
-
-4. Push dei cambiamenti al proprio account: 
-
-```bash
-git push -u origin a-descriptive-name-for-my-changes
-```
-
-5. Una volte che siete soddisfatti dei nuovi cambiamenti, andate sulla webpage del vostro fork su GitHub. Cliccate "Pull request". 
-Assiuratevi di aggiungere alcuni membri di Hugging Face come reviewers, nel riguardo alla destra della pagina della PR, cosicche il team 
-Hugging Face verrà notificato anche per i futuri cambiamenti. 
-
-6. Cambiare la PR a draft, cliccando su "Convert to draft" alla destra della pagina della PR
-
-Da quel punto in poi, ricordate di fare commit di ogni progresso e cambiamento, cosicche venga mostrato nella PR. Inoltre, 
-ricordatevi di tenere aggiornato il vostro lavoro con il main esistente:
-
-```bash
-git fetch upstream
-git merge upstream/main
-```
-
-In generale, tutte le domande che avrete riguardo al modello o l'implementazione dovranno essere fatte nella vostra PR 
-e discusse/risolte nella PR stessa. In questa maniera, il team di Hugging Face sarà sempre notificato quando farete commit 
-di un nuovo codice o se avrete qualche domanda. É molto utile indicare al team di Hugging Face il codice a cui fate riferimento 
-nella domanda, cosicche il team potra facilmente capire il problema o la domanda. 
-
-Per fare questo andate sulla tab "Files changed", dove potrete vedere tutti i vostri cambiamenti al codice, andate sulla linea 
-dove volete chiedere una domanda, e cliccate sul simbolo "+" per aggiungere un commento. Ogni volta che una domanda o problema 
-é stato risolto, cliccate sul bottone "Resolve".
-
-In questa stessa maniera, Hugging Face aprirà domande o commenti nel rivedere il vostro codice. Mi raccomando, chiedete più 
-domande possibili nella pagina della vostra PR. Se avete domande molto generali, non molto utili per il pubblico, siete liberi 
-di chiedere al team Hugging Face direttamente su slack o email.
-
-
-**5. Adattare i codici per brand_new_bert**
-
-Per prima cosa, ci focalizzeremo sul modello e non sui tokenizer. Tutto il codice relative dovrebbe trovarsi in  
-`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` e
-`src/transformers/models/brand_new_bert/configuration_brand_new_bert.py`.
-
-Ora potete finalmente cominciare il codice :). Il codice generato in 
-`src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` avrà sia la stessa architettura di BERT se é un 
-modello encoder-only o BART se é encoder-decoder. A questo punto, ricordatevi cio che avete imparato all'inizio, riguardo 
-agli aspetti teorici del modello: *In che maniera il modello che sto implmementando é diverso da BERT o BART?*. Implementare 
-questi cambi  spesso vuol dire cambiare il layer *self-attention*, l'ordine dei layer di normalizzazione e così via... 
-Ancora una volta ripetiamo, é molto utile vedere architetture simili di modelli gia esistenti in Transformers per avere 
-un'idea migliore su come implementare il modello. 
-
-**Notate** che a questo punto non dovete avere subito un codice tutto corretto o pulito. Piuttosto, é consigliato cominciare con un 
-codice poco pulito, con copia-incolla del codice originale in `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py` 
-fino a che non avrete tutto il codice necessario. In base alla nostra esperienza, é molto meglio aggiungere una prima bozza 
-del codice richiesto e poi correggere e migliorare iterativamente. L'unica cosa essenziale che deve funzionare qui é la seguente 
-instanza: 
-
-```python
-from transformers import BrandNewBertModel, BrandNewBertConfig
-
-model = BrandNewBertModel(BrandNewBertConfig())
-```
-
-Questo comando creerà un modello con i parametri di default definiti in `BrandNewBergConfig()` e weights random. Questo garantisce 
-che `init()` di tutte le componenti funzioni correttamente.
-
-
-**6. Scrivere uno script di conversione**
-
-Il prossimo step é scrivere uno script per convertire il checkpoint che avete usato per fare debug su *brand_new_berts* nella 
-repo originale in un checkpoint per la nuova implementazione di *brand_new_bert* in 🤗 Transformers. Non é consigliato scrivere 
-lo script di conversione da zero, ma piuttosto cercate e guardate script gia esistenti in 🤗 Transformers, così da trovarne
-uno simile al vostro modello. Di solito basta fare una copia di uno script gia esistente e adattarlo al vostro caso. 
-Non esistate a chiedre al team di Hugging Face a riguardo.
-
-- Se state convertendo un modello da TensorFlow a PyTorch, un ottimo inizio é vedere [questo script di conversione per BERT](https://github.com/huggingface/transformers/blob/7acfa95afb8194f8f9c1f4d2c6028224dbed35a2/src/transformers/models/bert/modeling_bert.py#L91)
-- Se state convertendo un modello da PyTorch a PyTorch, [lo script di conversione di BART può esservi utile](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bart/convert_bart_original_pytorch_checkpoint_to_pytorch.py)
-
-Qui di seguito spiegheremo come i modelli PyTorch salvano i weights per ogni layer e come i nomi dei layer sono definiti. In PyTorch, 
-il nomde del layer é definito dal nome della class attribute che date al layer. Definiamo un modello dummy in PyTorch, 
-chiamato `SimpleModel`:
-
-```python
-from torch import nn
-
-
-class SimpleModel(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.dense = nn.Linear(10, 10)
-        self.intermediate = nn.Linear(10, 10)
-        self.layer_norm = nn.LayerNorm(10)
-```
-Ora possiamo creare un'instanza di questa definizione di modo da inizializzare a random weights: `dense`, `intermediate`, `layer_norm`.
-Possiamo usare print per vedere l'architettura del modello:
-
-```python
-model = SimpleModel()
-
-print(model)
-```
-
-Da cui si ottiene:
-
-```
-SimpleModel(
-  (dense): Linear(in_features=10, out_features=10, bias=True)
-  (intermediate): Linear(in_features=10, out_features=10, bias=True)
-  (layer_norm): LayerNorm((10,), eps=1e-05, elementwise_affine=True)
-)
-```
-
-Si può vedere come i nomi dei layers siano definiti dal nome della class attribute in PyTorch. I valori dei weights di uno 
-specifico layer possono essere visualizzati:
-
-
-```python
-print(model.dense.weight.data)
-```
-
-ad esempio:
-
-```
-tensor([[-0.0818,  0.2207, -0.0749, -0.0030,  0.0045, -0.1569, -0.1598,  0.0212,
-         -0.2077,  0.2157],
-        [ 0.1044,  0.0201,  0.0990,  0.2482,  0.3116,  0.2509,  0.2866, -0.2190,
-          0.2166, -0.0212],
-        [-0.2000,  0.1107, -0.1999, -0.3119,  0.1559,  0.0993,  0.1776, -0.1950,
-         -0.1023, -0.0447],
-        [-0.0888, -0.1092,  0.2281,  0.0336,  0.1817, -0.0115,  0.2096,  0.1415,
-         -0.1876, -0.2467],
-        [ 0.2208, -0.2352, -0.1426, -0.2636, -0.2889, -0.2061, -0.2849, -0.0465,
-          0.2577,  0.0402],
-        [ 0.1502,  0.2465,  0.2566,  0.0693,  0.2352, -0.0530,  0.1859, -0.0604,
-          0.2132,  0.1680],
-        [ 0.1733, -0.2407, -0.1721,  0.1484,  0.0358, -0.0633, -0.0721, -0.0090,
-          0.2707, -0.2509],
-        [-0.1173,  0.1561,  0.2945,  0.0595, -0.1996,  0.2988, -0.0802,  0.0407,
-          0.1829, -0.1568],
-        [-0.1164, -0.2228, -0.0403,  0.0428,  0.1339,  0.0047,  0.1967,  0.2923,
-          0.0333, -0.0536],
-        [-0.1492, -0.1616,  0.1057,  0.1950, -0.2807, -0.2710, -0.1586,  0.0739,
-          0.2220,  0.2358]]).
-```
-
-Nello script di conversione, dovreste riempire quei valori di inizializzazione random con gli stessi weights del corrispondente 
-layer nel checkpoint. *Per esempio*
-
-```python
-# retrieve matching layer weights, e.g. by
-# recursive algorithm
-layer_name = "dense"
-pretrained_weight = array_of_dense_layer
-
-model_pointer = getattr(model, "dense")
-
-model_pointer.weight.data = torch.from_numpy(pretrained_weight)
-```
-
-Così facendo, dovete verificare che ogni inizializzazione random di un peso del modello PyTorch e il suo corrispondente peso nel pretrained checkpoint 
-siano esattamente gli stessi e uguali in **dimensione/shape e nome**. Per fare questo, é **necessario** aggiungere un `assert` 
-per la dimensione/shape e nome:
-
-```python
-assert (
-    model_pointer.weight.shape == pretrained_weight.shape
-), f"Pointer shape of random weight {model_pointer.shape} and array shape of checkpoint weight {pretrained_weight.shape} mismatched"
-```
-
-Inoltre, dovrete fare il print sia dei nomi che dei weights per essere sicuri che siano gli stessi:
-
-```python
-logger.info(f"Initialize PyTorch weight {layer_name} from {pretrained_weight.name}")
-```
-
-Se la dimensione o il nome non sono uguali, probabilmente avete sbagliato ad assegnare il peso nel checkpoint o nel layer costrutture di 
- 🤗 Transformers.
-
-Una dimensione sbagliata può essere dovuta ad un errore nei parameteri in `BrandNewBertConfig()`. Tuttavia, può essere anche 
-che l'implementazione del layer in PyTorch richieda di fare una transposizione della matrice dei weights. 
-
-Infine, controllate **tutti** che tutti i weights inizializzati e fate print di tutti i weights del checkpoint che non sono stati 
-usati per l'inizializzazione, di modo da essere sicuri che il modello sia correttamente convertito. É normale che ci siano 
-errori nel test di conversione, fai per un errore in `BrandNewBertConfig()`, o un errore nell'architettura in 🤗 Transformers, 
-o un bug in `init()`. 
-
-Questo step dev'essere fatto tramite iterazioni fino a che non si raggiungano gli stessi valori per i weights. Una volta che 
-il checkpoint é stato correttamente caricato in 🤗 Transformers, potete salvare il modello in una cartella di vostra scelta 
-`/path/to/converted/checkpoint/folder` che contenga sia
-`pytorch_model.bin` che `config.json`:
-
-```python
-model.save_pretrained("/path/to/converted/checkpoint/folder")
-```
-
-
-**7. Implementare il forward pass**
-
-Una volta che i weights pretrained sono stati correttamente caricati in 🤗 Transformers, dovrete assicurarvi che il forward pass 
-sia correttamente implementato. [Qui](#provare-un-pretrained-checkpoint-usando-la-repo-originale), avete give creato e provato
-uno script che testi il forward pass del modello usando la repo originaria. Ora dovrete fare lo stesso con uno script analogo 
-usando l'implementazione in 🤗 Transformers anziché l'originale. Piu o meno lo script dovrebbe essere:
-
-```python
-model = BrandNewBertModel.from_pretrained("/path/to/converted/checkpoint/folder")
-input_ids = [0, 4, 4, 3, 2, 4, 1, 7, 19]
-output = model(input_ids).last_hidden_states
-```
-
-Di solito l'output da 🤗 Transformers non é uguale uguale all'output originario, sopratto la prima volta. Non vi abbattete - 
-é normale! Prima di tutto assicuratevi che non ci siano errori o che non vengano segnalati degli errori nella forward pass. 
-Spesso capita che ci siano dimensioni sbagliate o data type sbagliati, *ad esempio* `torch.long` anziche `torch.float32`. 
-Non esistate a chiedere al team Hugging Face!
-
-Nella parte finale assicuratevi che l'implementazione 🤗 Transformers funzioni correttamente cosi da testare che gli output 
-siano equivalenti a una precisione di `1e-3`. Controllate che `outputs.shape` siano le stesse tra 🤗 Transformers e l'implementazione 
-originaria. Poi, controllate che i valori in output siano identici. Questa é sicuramente la parte più difficile, qui una serie 
-di errori comuni quando gli output non sono uguali:
-
-- Alcuni layers non sono stati aggiunti, *ad esempio* un *activation* layer non é stato aggiunto, o ci si é scordati di una connessione 
-- La matrice del word embedding non é stata ripareggiata 
-- Ci sono degli embeddings posizionali sbagliati perché l'implementazione originaria ha un offset 
-- Il dropout é in azione durante il forward pass. Per sistemare questo errore controllate che *model.training = False* e che 
-il dropout non sia stato attivato nel forward pass, * per esempio * passate *self.training* a [PyTorch's functional dropout](https://pytorch.org/docs/stable/nn.functional.html?highlight=dropout#torch.nn.functional.dropout)
-
-La miglior maniera per sistemare il problema é di vedere all'implementazione originaria del forward pass e in 🤗 Transformers 
-fianco a fianco e vedere se ci sono delle differenze. In teoria, con debug e print degli output intermedie di entrambe le 
-implementazioni nel forward pass nell'esatta posizione del network dovrebbe aiutarvi a vedere dove ci sono differenze tra 
-i due frameworks. Come prima mossa controllate che `input_ids` siano identici in entrambi gli scripts. Da lì andate fino 
-all'ultimo layer. Potrete notare una differenza tra le due implementazioni a quel punto. 
-
-Una volta che lo stesso output é stato ragguingi, verificate gli output con `torch.allclose(original_output, output, atol=1e-3)`.
-A questo punto se é tutto a posto: complimenti! Le parti seguenti saranno una passeggiata 😊.
-
-
-**8. Aggiungere i test necessari per il modello**
-
-A questo punto avete aggiunto con successo il vostro nuovo modello. Tuttavia, é molto probabile che il modello non sia 
-del tutto ok con il design richiesto. Per essere sicuri che l'implementazione sia consona e compatibile con 🤗 Transformers é
-necessario implementare dei tests. Il Cookiecutter dovrebbe fornire automaticamente dei file per test per il vostro modello, 
-di solito nella folder `tests/test_modeling_brand_new_bert.py`. Provate questo per verificare l'ok nei test piu comuni:
-
-```bash
-pytest tests/test_modeling_brand_new_bert.py
-```
-
-Una volta sistemati i test comuni, bisogna assicurarsi che il vostro lavoro sia correttamente testato cosicchè:
-
-- a) La community puo capire in maniera semplice il vostro lavoro controllando tests specifici del modello *brand_new_bert*,
-- b) Implementazioni future del vostro modello non rompano alcune feature importante del modello.
-
-Per prima cosa agguingete dei test d'integrazione. Questi sono essenziali perche fanno la stessa funzione degli scripts di 
-debug usati precedentemente. Un template per questi tests esiste gia nel Cookiecutter ed é sotto il nome di `BrandNewBertModelIntegrationTests`, 
-voi dovrete solo completarlo. Una volta che questi tests sono OK, provate:
-
-```bash
-RUN_SLOW=1 pytest -sv tests/test_modeling_brand_new_bert.py::BrandNewBertModelIntegrationTests
-```
-
-<Tip>
-
-Nel caso siate su Windows, sostituite `RUN_SLOW=1` con `SET RUN_SLOW=1`
-
-</Tip>
-
-Di seguito, tutte le features che sono utili e necessarire per *brand_new_bert* devono essere testate in test separati, 
-contenuti in `BrandNewBertModelTester`/ `BrandNewBertModelTest`. spesso la gente si scorda questi test, ma ricordate che sono utili per:
-
-
-- Aiuta gli utenti a capire il vostro codice meglio, richiamando l'attenzione su queste nuove features
-- Developers e contributors futuri potranno velocemente testare nuove implementazioni del modello testanto questi casi speciali.
-
-
-**9. Implementare il tokenizer**
-
-A questo punto avremo bisogno un tokenizer per *brand_new_bert*. Di solito il tokenizer é uguale ad altri modelli in 🤗 Transformers.
-
-É importante che troviate il file con il tokenizer originale e che lo carichiate in 🤗 Transformers.
-
-Per controllare che il tokenizer funzioni in modo corretto, create uno script nella repo originaria che riceva come input 
-una stringa e ritorni gli `input_ids`. Piu o meno questo potrebbe essere il codice:
-
-```python
-input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
-model = BrandNewBertModel.load_pretrained_checkpoint("/path/to/checkpoint/")
-input_ids = model.tokenize(input_str)
-```
-
-Potrebbe richiedere un po' di tempo, ma guardate ancora alla repo originaria per trovare la funzione corretta del tokenizer. 
-A volte capita di dover riscrivere il tokenizer nella repo originaria, di modo da avere come output gli `input_ids`. 
-A quel punto uno script analogo é necessario in 🤗 Transformers:
-
-```python
-from transformers import BrandNewBertTokenizer
-
-input_str = "This is a long example input string containing special characters .$?-, numbers 2872 234 12 and words."
-
-tokenizer = BrandNewBertTokenizer.from_pretrained("/path/to/tokenizer/folder/")
-
-input_ids = tokenizer(input_str).input_ids
-```
-
-Una volta che `input_ids` sono uguali, bisogna aggiungere un test per il tokenizer. 
-
-Il file test per tokenizer di *brand_new_brand* dovrebbe avere un paio di hard-coded test d'integrazione.
-
-
-**10. Test end-to-end**
-
-Ora che avete il tokenizer, dovrete aggiungere dei test d'integrazione per l'intero workflow in `tests/test_modeling_brand_new_bert.py` in 🤗 Transformer.
-Questi test devono mostrare che un significante campione text-to-text funzioni come ci si aspetta nell'implementazione di  🤗 Transformers.
-*Per esempio* potreste usare dei source-to-target-translation, o un sommario di un articolo, o un domanda-risposta e cosi via. 
-Se nessuno dei checkpoints é stato ultra parametrizzato per task simili, allora i tests per il modello sono piu che sufficienti. 
-Nello step finale dovete assicurarvi che il modello sia totalmente funzionale, e consigliamo anche di provare a testare su GPU. 
-Puo succedere che ci si scordi un `.to(self.device)` ad esempio. Se non avete accesso a GPU, il team Hugging Face puo provvedere
-a testare questo aspetto per voi. 
-
-**11. Aggiungere una Docstring**
-
-Siete quasi alla fine! L'ultima cosa rimasta é avere una bella docstring e una pagina doc. Il Cookiecutter dovrebbe provvedere già 
-un template chiamato `docs/source/model_doc/brand_new_bert.rst`, che dovrete compilare. La prima cosa che un utente farà 
-per usare il vostro modello sarà dare una bella lettura al doc. Quindi proponete una documentazione chiara e concisa. É molto 
-utile per la community avere anche delle *Tips* per mostrare come il modello puo' essere usato. Non esitate a chiedere a Hugging Face 
-riguardo alle docstirng. 
-
-Quindi, assicuratevi che la docstring sia stata aggiunta a `src/transformers/models/brand_new_bert/modeling_brand_new_bert.py`. 
-Assicuratevi che la docstring sia corretta e che includa tutti i necessari input e output. Abbiamo una guida dettagliata per 
-scrivere la documentazione e docstring.
-
-
-**Rifattorizzare il codice**
-
-Perfetto! Ora che abbiamo tutto per *brand_new_bert* controllate che lo stile del codice sia ok:
-
-```bash
-make style
-```
-
-E che il codice passi i quality check:
-
-```bash
-make quality
-```
-
-A volte capita che manchino delle informazioninella docstring o alcuni nomi sbagliati, questo farà fallire i tests sopra. 
-Ripetiamo: chiedete pure a Hugging Face, saremo lieti di aiutarvi. 
-
-Per ultimo, fare del refactoring del codice una volta che é stato creato.
-
-Avete finito con il codice, congratulazioni! 🎉 Siete fantasticiiiiiii! 😎
-
-**12. Caricare il modello sul model hub**
-
-In questa ultima parte dovrete convertire e caricare il modello, con tutti i checkpoints, nel model hub e aggiungere una 
-model card per ogni checkpoint caricato. Leggete la nostra guida [Model sharing and uploading Page](model_sharing) per 
-avere familiarità con l'hub. Di solito in questa parte lavorate a fianco di Hugging face per decidere un nome che sia ok 
-per ogni checkpoint, per ottenere i permessi necessari per caricare il modello nell'organizzazione dell'autore di *brand_new_bert*. 
-Il metodo `push_to_hub`, presente in tutti i modelli `transformers`, é una maniera rapida e indolore per caricare il vostro checkpoint sull'hub:
-
-```python
-brand_new_bert.push_to_hub(
-    repo_path_or_name="brand_new_bert",
-    # Uncomment the following line to push to an organization
-    # organization="<ORGANIZATION>",
-    commit_message="Add model",
-    use_temp_dir=True,
-)
-```
-
-Vale la pena spendere un po' di tempo per creare una model card ad-hoc per ogni checkpoint. Le model cards dovrebbero 
-suggerire le caratteristiche specifiche del checkpoint, *per esempio* su che dataset il checkpoint é stato pretrained o fine-tuned. 
-O che su che genere di task il modello lavoro? E anche buona pratica includere del codice su come usare il modello correttamente.
-
-
-**13. (Opzionale) Aggiungere un notebook**
-
-É molto utile aggiungere un notebook, che dimostri in dettaglio come *brand_new_bert* si utilizzi per fare inferenza e/o 
-fine-tuned su specifiche task. Non é una cosa obbligatoria da avere nella vostra PR, ma é molto utile per la community.
-
-**14. Sottomettere la PR**
-
-L'ultimissimo step! Ovvero il merge della PR nel main. Di solito il team Hugging face a questo punto vi avrà gia aiutato, 
-ma é ok prendere un po' di tempo per pulire la descirzione e commenti nel codice.
-
-
-### Condividete il vostro lavoro!!
-
-É ora tempo di prendere un po' di credito dalla communità per il vostro lavoro! Caricare e implementare un nuovo modello 
-é un grandissimo contributo per Transformers e l'intera community NLP. Il codice e la conversione dei modelli pre-trained sara 
-sicuramente utilizzato da centinaia o migliaia di sviluppatori e ricercatori. Siate fieri e orgogliosi di condividere il vostro 
-traguardo con l'intera community :) 
-
-** Avete create un altro modello che é super facile da usare per tutti quanti nella community! 🤯**

docs/source/it/add_new_pipeline.mdx

@@ -1,246 +0,0 @@
-<!--Copyright 2020 The HuggingFace Team. All rights reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
-the License. You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
-an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
--->
-
-# Come creare una pipeline personalizzata?
-
-In questa guida, scopriremo come creare una pipeline personalizzata e condividerla sull' [Hub](hf.co/models) o aggiungerla nella libreria
-Transformers.
-
-Innanzitutto, è necessario decidere gli input grezzi che la pipeline sarà in grado di accettare. Possono essere strings, raw bytes,
-dictionaries o qualsiasi cosa sia l'input desiderato più probabile. Cerca di mantenere questi input il più possibile in Python
-in quanto facilita la compatibilità (anche con altri linguaggi tramite JSON). Questi saranno gli `inputs` della
-pipeline (`preprocess`).
-
-Poi definire gli `outputs`. Stessa strategia degli `inputs`. Più è seplice e meglio è. Questi saranno gli output del metodo
-`postprocess`.
-
-Si parte ereditando la classe base `Pipeline`. con i 4 metodi che bisogna implementare `preprocess`,
-`_forward`, `postprocess` e `_sanitize_parameters`.
-
-
-```python
-from transformers import Pipeline
-
-
-class MyPipeline(Pipeline):
-    def _sanitize_parameters(self, **kwargs):
-        preprocess_kwargs = {}
-        if "maybe_arg" in kwargs:
-            preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
-        return preprocess_kwargs, {}, {}
-
-    def preprocess(self, inputs, maybe_arg=2):
-        model_input = Tensor(inputs["input_ids"])
-        return {"model_input": model_input}
-
-    def _forward(self, model_inputs):
-        # model_inputs == {"model_input": model_input}
-        outputs = self.model(**model_inputs)
-        # Maybe {"logits": Tensor(...)}
-        return outputs
-
-    def postprocess(self, model_outputs):
-        best_class = model_outputs["logits"].softmax(-1)
-        return best_class
-```
-
-La struttura di questa suddivisione consiste nel supportare in modo relativamente continuo CPU/GPU, supportando allo stesso tempo l'esecuzione di
-pre/postelaborazione sulla CPU su thread diversi.
-
-`preprocess` prenderà gli input originariamente definiti e li trasformerà in qualcosa di alimentabile dal modello. Potrebbe
-contenere più informazioni e di solito è un `Dict`.
-
-`_forward` è il dettaglio dell'implementazione e non è destinato a essere chiamato direttamente. `forward` è il metodo preferito per assicurarsi che tutto funzioni correttamente perchè contiene delle slavaguardie. Se qualcosa è
-è collegato a un modello reale, appartiene al metodo `_forward`, tutto il resto è nel preprocess/postprocess.
-
-`postprocess` prende l'otput di `_forward` e lo trasforma nell'output finale che era stato deciso in precedenza.
-
-`_sanitize_parameters` esiste per consentire agli utenti di passare i parametri ogni volta che desiderano sia a inizialization time `pipeline(...., maybe_arg=4)` che al call time `pipe = pipeline(...); output = pipe(...., maybe_arg=4)`.
-
-`_sanitize_parameters` ritorna 3 dicts di kwargs che vengono passati direttamente a `preprocess`,
-`_forward` e `postprocess`. Non riempire nulla se il chiamante non ha chiamato con alcun parametro aggiuntivo. Questo
-consente di mantenere gli argomenti predefiniti nella definizione della funzione, che è sempre più "naturale".
-
-Un esempio classico potrebbe essere l'argomento `top_k` nel post processing dei classification tasks.
-
-```python
->>> pipe = pipeline("my-new-task")
->>> pipe("This is a test")
-[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}, {"label": "3-star", "score": 0.05}
-{"label": "4-star", "score": 0.025}, {"label": "5-star", "score": 0.025}]
-
->>> pipe("This is a test", top_k=2)
-[{"label": "1-star", "score": 0.8}, {"label": "2-star", "score": 0.1}]
-```
-
-In order to achieve that, we'll update our `postprocess` method with a default parameter to `5`. and edit
-`_sanitize_parameters` to allow this new parameter.
-
-
-```python
-def postprocess(self, model_outputs, top_k=5):
-    best_class = model_outputs["logits"].softmax(-1)
-    # Add logic to handle top_k
-    return best_class
-
-
-def _sanitize_parameters(self, **kwargs):
-    preprocess_kwargs = {}
-    if "maybe_arg" in kwargs:
-        preprocess_kwargs["maybe_arg"] = kwargs["maybe_arg"]
-
-    postprocess_kwargs = {}
-    if "top_k" in kwargs:
-        postprocess_kwargs["top_k"] = kwargs["top_k"]
-    return preprocess_kwargs, {}, postprocess_kwargs
-```
-
-Cercare di mantenere gli input/output molto semplici e idealmente serializzabili in JSON, in quanto ciò rende l'uso della pipeline molto facile
-senza richiedere agli utenti di comprendere nuovi tipi di oggetti. È anche relativamente comune supportare molti tipi di argomenti
-per facilitarne l'uso (ad esempio file audio, possono essere nomi di file, URL o byte puri).
-
-## Aggiungilo alla lista dei tasks supportati
-
-Per registrar il tuo `new-task` alla lista dei tasks supportati, devi aggiungerlo al `PIPELINE_REGISTRY`:
-
-```python
-from transformers.pipelines import PIPELINE_REGISTRY
-
-PIPELINE_REGISTRY.register_pipeline(
-    "new-task",
-    pipeline_class=MyPipeline,
-    pt_model=AutoModelForSequenceClassification,
-)
-```
-
-Puoi specificare il modello di default che desideri, in questo caso dovrebbe essere accompagnato da una revisione specifica (che può essere il nome di un branch o l'hash di un commit, in questo caso abbiamo preso `"abcdef"`) e anche dal type:
-
-```python
-PIPELINE_REGISTRY.register_pipeline(
-    "new-task",
-    pipeline_class=MyPipeline,
-    pt_model=AutoModelForSequenceClassification,
-    default={"pt": ("user/awesome_model", "abcdef")},
-    type="text",  # current support type: text, audio, image, multimodal
-)
-```
-
-## Condividi la tua pipeline sull'Hub
-
-Per condividere la tua pipeline personalizzata sull'Hub, devi solo salvare il codice della tua sottoclasse `Pipeline` in un file
-python. Per esempio, supponiamo di voler utilizzare una pipeline personalizzata per la classificazione delle coppie di frasi come la seguente:
-
-```py
-import numpy as np
-
-from transformers import Pipeline
-
-
-def softmax(outputs):
-    maxes = np.max(outputs, axis=-1, keepdims=True)
-    shifted_exp = np.exp(outputs - maxes)
-    return shifted_exp / shifted_exp.sum(axis=-1, keepdims=True)
-
-
-class PairClassificationPipeline(Pipeline):
-    def _sanitize_parameters(self, **kwargs):
-        preprocess_kwargs = {}
-        if "second_text" in kwargs:
-            preprocess_kwargs["second_text"] = kwargs["second_text"]
-        return preprocess_kwargs, {}, {}
-
-    def preprocess(self, text, second_text=None):
-        return self.tokenizer(text, text_pair=second_text, return_tensors=self.framework)
-
-    def _forward(self, model_inputs):
-        return self.model(**model_inputs)
-
-    def postprocess(self, model_outputs):
-        logits = model_outputs.logits[0].numpy()
-        probabilities = softmax(logits)
-
-        best_class = np.argmax(probabilities)
-        label = self.model.config.id2label[best_class]
-        score = probabilities[best_class].item()
-        logits = logits.tolist()
-        return {"label": label, "score": score, "logits": logits}
-```
-
-L'implementazione è agnostica al framework, e lavorerà sia con modelli PyTorch che con TensorFlow. Se l'abbiamo salvato in un file chiamato `pair_classification.py`, può essere successivamente importato e registrato in questo modo:
-
-```py
-from pair_classification import PairClassificationPipeline
-from transformers.pipelines import PIPELINE_REGISTRY
-from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification
-
-PIPELINE_REGISTRY.register_pipeline(
-    "pair-classification",
-    pipeline_class=PairClassificationPipeline,
-    pt_model=AutoModelForSequenceClassification,
-    tf_model=TFAutoModelForSequenceClassification,
-)
-```
-
-Una volta fatto, possiamo usarla con un modello pretrained. L'istanza `sgugger/finetuned-bert-mrpc` è stata
-fine-tuned sul dataset MRPC, che classifica le coppie di frasi come parafrasi o no.
-
-```py
-from transformers import pipeline
-
-classifier = pipeline("pair-classification", model="sgugger/finetuned-bert-mrpc")
-```
-
-Successivamente possiamo condividerlo sull'Hub usando il metodo `save_pretrained` in un `Repository`:
-
-```py
-from huggingface_hub import Repository
-
-repo = Repository("test-dynamic-pipeline", clone_from="{your_username}/test-dynamic-pipeline")
-classifier.save_pretrained("test-dynamic-pipeline")
-repo.push_to_hub()
-```
-
-Questo codice copierà il file dove è stato definitp `PairClassificationPipeline` all'interno della cartella `"test-dynamic-pipeline"`,
-insieme al salvataggio del modello e del tokenizer della pipeline, prima di pushare il tutto nel repository
-`{your_username}/test-dynamic-pipeline`. Dopodiché chiunque potrà utilizzarlo, purché fornisca l'opzione
-`trust_remote_code=True`:
-
-```py
-from transformers import pipeline
-
-classifier = pipeline(model="{your_username}/test-dynamic-pipeline", trust_remote_code=True)
-```
-
-## Aggiungere la pipeline a Transformers
-
-Se vuoi contribuire con la tua pipeline a Transformers, dovrai aggiungere un modulo nel sottomodulo `pipelines`
-con il codice della tua pipeline, quindi aggiungilo all'elenco dei tasks definiti in `pipelines/__init__.py`.
-
-Poi hai bisogno di aggiungere i test. Crea un nuovo file `tests/test_pipelines_MY_PIPELINE.py` con esempi ed altri test.
-
-La funzione `run_pipeline_test` sarà molto generica e su piccoli modelli casuali su ogni possibile
-architettura, come definito da `model_mapping` e `tf_model_mapping`.
-
-Questo è molto importante per testare la compatibilità futura, nel senso che se qualcuno aggiunge un nuovo modello di
-`XXXForQuestionAnswering` allora il test della pipeline tenterà di essere eseguito su di esso. Poiché i modelli sono casuali, è
-è impossibile controllare i valori effettivi, per questo esiste un aiuto `ANY` che tenterà solamente di far corrispondere l'output della pipeline TYPE.
-
-Hai anche *bisogno* di implementare 2 (idealmente 4) test.
-
-- `test_small_model_pt` : Definire 1 piccolo modello per questa pipeline (non importa se i risultati non hanno senso)
-  e testare i risultati della pipeline. I risultati dovrebbero essere gli stessi di `test_small_model_tf`.
-- `test_small_model_tf` : Definire 1 piccolo modello per questa pipeline (non importa se i risultati non hanno senso)
-  e testare i risultati della pipeline. I risultati dovrebbero essere gli stessi di `test_small_model_pt`.
-- `test_large_model_pt` (`optional`): Testare la pipeline su una pipeline reale in cui i risultati dovrebbero avere
-  senso. Questi test sono lenti e dovrebbero essere contrassegnati come tali. In questo caso l'obiettivo è mostrare la pipeline e assicurarsi che non ci siano  derive nelle versioni future
-- `test_large_model_tf` (`optional`): Testare la pipeline su una pipeline reale in cui i risultati dovrebbero avere
-  senso. Questi test sono lenti e dovrebbero essere contrassegnati come tali. In questo caso l'obiettivo è mostrare la pipeline e assicurarsi
-  che non ci siano derive nelle versioni future

examples/flax/image-captioning/run_image_captioning_flax.py

@@ -335,6 +335,7 @@ def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuf
         batch_idx = np.arange(len(dataset))
 
     for idx in range(steps):
+
         start_idx = batch_size * idx
         end_idx = batch_size * (idx + 1)
 
@@ -346,6 +347,7 @@ def data_loader(rng: jax.random.PRNGKey, dataset: Dataset, batch_size: int, shuf
 
 
 def write_metric(summary_writer, metrics, train_time, step, metric_key_prefix="train"):
+
     if train_time:
         summary_writer.scalar("train_time", train_time, step)
 
@@ -780,9 +782,11 @@ def main():
         num_splits = steps // steps_per_block + int(steps % steps_per_block > 0)
 
         for idx in range(num_splits):
+
             if not block_size:
                 _ds = ds
             else:
+
                 start_idx = block_size * idx
                 end_idx = block_size * (idx + 1)
 
@@ -922,9 +926,8 @@ def main():
 
         # ignore padded tokens from loss
         loss = loss * padding_mask
-        loss = loss.sum()
-        num_labels = padding_mask.sum()
-        return loss, num_labels
+        loss = loss.sum() / padding_mask.sum()
+        return loss
 
     # Define gradient update step fn
     def train_step(state, batch, label_smoothing_factor=0.0):
@@ -933,38 +936,29 @@ def main():
         def compute_loss(params):
             labels = batch.pop("labels")
             logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-            loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
-            return loss, num_labels
+            loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
+            return loss
 
-        grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
-        (loss, num_labels), grad = grad_fn(state.params)
-        num_labels = jax.lax.psum(num_labels, "batch")
+        grad_fn = jax.value_and_grad(compute_loss)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
 
-        # true loss = total loss / total samples
-        loss = jax.lax.psum(loss, "batch")
-        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
-        # true grad = total grad / total samples
-        grad = jax.lax.psum(grad, "batch")
-        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
         new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
 
         metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
+
         return new_state, metrics
 
     # Define eval fn
     def eval_step(params, batch, label_smoothing_factor=0.0):
         labels = batch.pop("labels")
         logits = model(**batch, params=params, train=False)[0]
+        loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
 
-        loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
-        num_labels = jax.lax.psum(num_labels, "batch")
-
-        # true loss = total loss / total samples
-        loss = jax.lax.psum(loss, "batch")
-        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
+        # summarize metrics
         metrics = {"loss": loss}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
         return metrics
 
     # Define generation function
@@ -1030,6 +1024,7 @@ def main():
         ckpt_dir: str = "",
         is_prediction=False,
     ):
+
         logger.info(f"*** {'Predict' if is_prediction else 'Evaluate'} ***")
 
         metrics = []
@@ -1108,10 +1103,12 @@ def main():
             logger.info(desc)
 
         if jax.process_index() == 0:
+
             if not os.path.isdir(os.path.join(training_args.output_dir, ckpt_dir)):
                 os.makedirs(os.path.join(training_args.output_dir, ckpt_dir), exist_ok=True)
 
             if metrics:
+
                 # Save metrics (only for the evaluation/prediction being done along with training)
                 if has_tensorboard and training_args.do_train:
                     write_metric(
@@ -1146,6 +1143,7 @@ def main():
     input_rng = None
 
     if training_args.do_train:
+
         cur_step = 0
         train_time = 0
         epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0)
@@ -1168,6 +1166,7 @@ def main():
 
             # train
             for batch_idx, _ in enumerate(tqdm(range(steps_per_epoch), desc="Training...", position=1, leave=False)):
+
                 cur_step += 1
                 batch = next(train_batches)
                 batch_start = time.time()
@@ -1178,6 +1177,7 @@ def main():
 
                 # log and save info
                 if training_args.logging_steps > 0 and cur_step % training_args.logging_steps == 0:
+
                     _train_metric = unreplicate(train_metric)
                     desc = (
                         f"Epoch... ({epoch + 1}/{num_epochs} | Step: {cur_step} | Loss: {_train_metric['loss']} |"
@@ -1217,6 +1217,7 @@ def main():
 
             # log and save info
             if training_args.logging_steps <= 0:
+
                 logger.info(desc)
 
                 with open(os.path.join(training_args.output_dir, "log"), "a", encoding="UTF-8") as fp:

examples/flax/language-modeling/README.md

@@ -351,7 +351,7 @@ The example script uses the 🤗 Datasets library. You can easily customize them
 To setup all relevant files for training, let's create a directory.
 
 ```bash
-mkdir ./norwegian-bart-base
+mkdir ./norwegian-roberta-base
 ```
 
 ### Train tokenizer

examples/flax/language-modeling/run_bart_dlm_flax.py

@@ -799,25 +799,19 @@ def main():
             loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
 
             # take average
-            loss = loss.sum()
-            num_labels = label_mask.sum()
+            loss = loss.sum() / label_mask.sum()
 
-            return loss, num_labels
+            return loss
 
-        grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
-        (loss, num_labels), grad = grad_fn(state.params)
-        num_labels = jax.lax.psum(num_labels, "batch")
-
-        # true loss = total loss / total samples
-        loss = jax.lax.psum(loss, "batch")
-        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
-        # true grad = total grad / total samples
-        grad = jax.lax.psum(grad, "batch")
-        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
+        grad_fn = jax.value_and_grad(loss_fn)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
         new_state = state.apply_gradients(grads=grad)
 
-        metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
+        metrics = jax.lax.pmean(
+            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
+        )
+
         return new_state, metrics, new_dropout_rng
 
     # Create parallel version of the train step
@@ -894,7 +888,7 @@ def main():
                 num_eval_samples = len(tokenized_datasets["validation"])
                 # Avoid using jax.numpy here in case of TPU training
                 eval_samples_idx = np.arange(num_eval_samples)
-                eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+                eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
 
                 eval_metrics = []
                 for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
@@ -909,9 +903,9 @@ def main():
 
                 # normalize eval metrics
                 eval_metrics = get_metrics(eval_metrics)
-                eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics)
+                eval_metrics = jax.tree_map(jnp.sum, eval_metrics)
                 eval_normalizer = eval_metrics.pop("normalizer")
-                eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
+                eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
 
                 # Update progress bar
                 epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})"
@@ -923,7 +917,7 @@ def main():
             if cur_step % training_args.save_steps == 0 and cur_step > 0:
                 # save checkpoint after each epoch and push checkpoint to the hub
                 if jax.process_index() == 0:
-                    params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params))
+                    params = jax.device_get(jax.tree_map(lambda x: x[0], state.params))
                     model.save_pretrained(training_args.output_dir, params=params)
                     tokenizer.save_pretrained(training_args.output_dir)
                     if training_args.push_to_hub:
@@ -934,7 +928,7 @@ def main():
         num_eval_samples = len(tokenized_datasets["validation"])
         # Avoid using jax.numpy here in case of TPU training
         eval_samples_idx = np.arange(num_eval_samples)
-        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size)
+        eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False)
 
         eval_metrics = []
         for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)):
@@ -949,9 +943,9 @@ def main():
 
         # normalize eval metrics
         eval_metrics = get_metrics(eval_metrics)
-        eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
+        eval_metrics = jax.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics)
         eval_normalizer = eval_metrics.pop("normalizer")
-        eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics)
+        eval_metrics = jax.tree_map(lambda x: x / eval_normalizer, eval_metrics)
 
         try:
             perplexity = math.exp(eval_metrics["loss"])

examples/flax/language-modeling/run_mlm_flax.py

@@ -723,25 +723,18 @@ def main():
             loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask
 
             # take average
-            loss = loss.sum()
-            num_labels = label_mask.sum()
+            loss = loss.sum() / label_mask.sum()
 
-            return loss, num_labels
+            return loss
 
-        grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
-        (loss, num_labels), grad = grad_fn(state.params)
-        num_labels = jax.lax.psum(num_labels, "batch")
-
-        # true loss = total loss / total samples
-        loss = jax.lax.psum(loss, "batch")
-        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
-        # true grad = total grad / total samples
-        grad = jax.lax.psum(grad, "batch")
-        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
+        grad_fn = jax.value_and_grad(loss_fn)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
         new_state = state.apply_gradients(grads=grad)
 
-        metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
+        metrics = jax.lax.pmean(
+            {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}, axis_name="batch"
+        )
 
         return new_state, metrics, new_dropout_rng
 

examples/flax/language-modeling/run_t5_mlm_flax.py

@@ -328,6 +328,7 @@ class FlaxDataCollatorForT5MLM:
     decoder_start_token_id: int
 
     def __call__(self, examples: List[Dict[str, np.ndarray]]) -> BatchEncoding:
+
         # convert list to dict and tensorize input
         batch = BatchEncoding(
             {k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()}
@@ -348,7 +349,7 @@ class FlaxDataCollatorForT5MLM:
         if batch["input_ids"].shape[-1] != self.input_length:
             raise ValueError(
                 f"`input_ids` are incorrectly preprocessed. `input_ids` length is {batch['input_ids'].shape[-1]}, but"
-                f" should be {self.input_length}."
+                f" should be {self.target_length}."
             )
 
         if batch["labels"].shape[-1] != self.target_length:
@@ -396,6 +397,7 @@ class FlaxDataCollatorForT5MLM:
         return input_ids
 
     def random_spans_noise_mask(self, length):
+
         """This function is copy of `random_spans_helper <https://github.com/google-research/text-to-text-transfer-transformer/blob/84f8bcc14b5f2c03de51bd3587609ba8f6bbd1cd/t5/data/preprocessors.py#L2682>`__ .
 
         Noise mask consisting of random spans of noise tokens.

examples/flax/question-answering/run_qa.py

@@ -61,7 +61,7 @@ from utils_qa import postprocess_qa_predictions
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/summarization/run_summarization_flax.py

@@ -784,9 +784,8 @@ def main():
 
         # ignore padded tokens from loss
         loss = loss * padding_mask
-        loss = loss.sum()
-        num_labels = padding_mask.sum()
-        return loss, num_labels
+        loss = loss.sum() / padding_mask.sum()
+        return loss
 
     # Define gradient update step fn
     def train_step(state, batch, label_smoothing_factor=0.0):
@@ -795,38 +794,29 @@ def main():
         def compute_loss(params):
             labels = batch.pop("labels")
             logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0]
-            loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
-            return loss, num_labels
+            loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
+            return loss
 
-        grad_fn = jax.value_and_grad(compute_loss, has_aux=True)
-        (loss, num_labels), grad = grad_fn(state.params)
-        num_labels = jax.lax.psum(num_labels, "batch")
+        grad_fn = jax.value_and_grad(compute_loss)
+        loss, grad = grad_fn(state.params)
+        grad = jax.lax.pmean(grad, "batch")
 
-        # true loss = total loss / total samples
-        loss = jax.lax.psum(loss, "batch")
-        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
-        # true grad = total grad / total samples
-        grad = jax.lax.psum(grad, "batch")
-        grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad)
         new_state = state.apply_gradients(grads=grad, dropout_rng=new_dropout_rng)
 
         metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
+
         return new_state, metrics
 
     # Define eval fn
     def eval_step(params, batch, label_smoothing_factor=0.0):
         labels = batch.pop("labels")
         logits = model(**batch, params=params, train=False)[0]
+        loss = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
 
-        loss, num_labels = loss_fn(logits, labels, batch["decoder_attention_mask"], label_smoothing_factor)
-        num_labels = jax.lax.psum(num_labels, "batch")
-
-        # true loss = total loss / total samples
-        loss = jax.lax.psum(loss, "batch")
-        loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss)
-
+        # summarize metrics
         metrics = {"loss": loss}
+        metrics = jax.lax.pmean(metrics, axis_name="batch")
         return metrics
 
     # Define generation function

examples/flax/text-classification/run_flax_glue.py

@@ -54,7 +54,7 @@ from transformers.utils import check_min_version, get_full_repo_name, send_examp
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/token-classification/run_flax_ner.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/audio-classification/run_audio_classification.py

@@ -45,7 +45,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
 

examples/pytorch/contrastive-image-text/run_clip.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")
 
@@ -161,6 +161,9 @@ class DataTrainingArguments:
     overwrite_cache: bool = field(
         default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
     )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
     preprocessing_num_workers: Optional[int] = field(
         default=None,
         metadata={"help": "The number of processes to use for the preprocessing."},

examples/pytorch/image-classification/README.md

@@ -23,8 +23,8 @@ This directory contains 2 scripts that showcase how to fine-tune any model suppo
 Try out the inference widget here: https://huggingface.co/google/vit-base-patch16-224
 
 Content:
-- [PyTorch version, Trainer](#pytorch-version-trainer)
-- [PyTorch version, no Trainer](#pytorch-version-no-trainer)
+- [PyTorch version, Trainer](#pytorch-version-no-trainer)
+- [PyTorch version, no Trainer](#pytorch-version-trainer)
 
 ## PyTorch version, Trainer
 
@@ -208,4 +208,4 @@ This command is the same and will work for:
 
 Note that this library is in alpha release so your feedback is more than welcome if you encounter any problem using it.
 
-Regarding using custom data with this script, we refer to [using your own data](#using-your-own-data).
+Regarding using custom data with this script, we refer to [using your own data](#using-your-own-data).

examples/pytorch/image-classification/run_image_classification.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/image-pretraining/run_mae.py

@@ -43,7 +43,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim.py

@@ -48,7 +48,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/language-modeling/run_clm.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 logger = get_logger(__name__)
 
@@ -183,7 +183,7 @@ def parse_args():
         help="The number of processes to use for the preprocessing.",
     )
     parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+        "--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
     )
     parser.add_argument(
         "--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files."

examples/pytorch/language-modeling/run_mlm.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 
@@ -72,7 +72,7 @@ class ModelArguments:
         default=None,
         metadata={
             "help": (
-                "The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
+                "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
             )
         },
     )

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
@@ -190,7 +190,7 @@ def parse_args():
         help="The number of processes to use for the preprocessing.",
     )
     parser.add_argument(
-        "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
+        "--overwrite_cache", type=bool, default=False, help="Overwrite the cached training and evaluation sets"
     )
     parser.add_argument(
         "--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"

examples/pytorch/language-modeling/run_plm.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/multiple-choice/run_swag.py

@@ -47,7 +47,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 logger = logging.getLogger(__name__)
 

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils import PaddingStrategy, check_min_version, get_full_repo
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.24.0.dev0")
+check_min_version("4.22.0")
 
 logger = get_logger(__name__)
 # You should update this to your particular problem to have better documentation of `model_type`

examples/pytorch/question-answering/README.md

@@ -115,7 +115,7 @@ python run_seq2seq_qa.py \
   --dataset_name squad_v2 \
   --context_column context \
   --question_column question \
-  --answer_column answers \
+  --answer_column answer \
   --do_train \
   --do_eval \
   --per_device_train_batch_size 12 \