fix

* Fix saved_model_creation_extended

* Skip the BLIP model creation test for now

* Fix TF SAM test

* Fix longformer tests

* Fix Wav2Vec2

* Add a skip for XLNet

* make fixup

* make fix-copies

* Add comments

.circleci/config.yml

@@ -43,6 +43,24 @@ jobs:
                 else
                     touch test_preparation/test_list.txt
                 fi
+            - run: |
+                  if [ -f examples_test_list.txt ]; then
+                      mv examples_test_list.txt test_preparation/examples_test_list.txt
+                  else
+                      touch test_preparation/examples_test_list.txt
+                  fi
+            - run: |
+                  if [ -f filtered_test_list_cross_tests.txt ]; then
+                      mv filtered_test_list_cross_tests.txt test_preparation/filtered_test_list_cross_tests.txt
+                  else
+                      touch test_preparation/filtered_test_list_cross_tests.txt
+                  fi
+            - run: |
+                if [ -f doctest_list.txt ]; then
+                    cp doctest_list.txt test_preparation/doctest_list.txt
+                else
+                    touch test_preparation/doctest_list.txt
+                fi
             - run: |
                 if [ -f test_repo_utils.txt ]; then
                     mv test_repo_utils.txt test_preparation/test_repo_utils.txt
@@ -56,21 +74,10 @@ jobs:
                 else
                     touch test_preparation/filtered_test_list.txt
                 fi
-            - run: python utils/tests_fetcher.py --filters tests examples | tee examples_tests_fetched_summary.txt
-            - run: |
-                  if [ -f test_list.txt ]; then
-                      mv test_list.txt test_preparation/examples_test_list.txt
-                  else
-                      touch test_preparation/examples_test_list.txt
-                  fi
-            - run: |
-                  if [ -f filtered_test_list_cross_tests.txt ]; then
-                      mv filtered_test_list_cross_tests.txt test_preparation/filtered_test_list_cross_tests.txt
-                  else
-                      touch test_preparation/filtered_test_list_cross_tests.txt
-                  fi
             - store_artifacts:
                   path: test_preparation/test_list.txt
+            - store_artifacts:
+                  path: test_preparation/doctest_list.txt
             - store_artifacts:
                   path: ~/transformers/test_preparation/filtered_test_list.txt
             - store_artifacts:
@@ -103,7 +110,7 @@ jobs:
             - run: |
                   mkdir test_preparation
                   echo -n "tests" > test_preparation/test_list.txt
-                  echo -n "tests" > test_preparation/examples_test_list.txt
+                  echo -n "all" > test_preparation/examples_test_list.txt
                   echo -n "tests/repo_utils" > test_preparation/test_repo_utils.txt
             - run: |
                   echo -n "tests" > test_list.txt

.circleci/create_circleci_config.py

@@ -36,6 +36,17 @@ COMMON_PYTEST_OPTIONS = {"max-worker-restart": 0, "dist": "loadfile", "s": None}
 DEFAULT_DOCKER_IMAGE = [{"image": "cimg/python:3.8.12"}]
 
 
+class EmptyJob:
+    job_name = "empty"
+
+    def to_dict(self):
+        return {
+            "working_directory": "~/transformers",
+            "docker": copy.deepcopy(DEFAULT_DOCKER_IMAGE),
+            "steps":["checkout"],
+        }
+
+
 @dataclass
 class CircleCIJob:
     name: str
@@ -97,6 +108,8 @@ class CircleCIJob:
             },
         ]
         steps.extend([{"run": l} for l in self.install_steps])
+        # TODO (ydshieh): Remove this line after the next release (the one after 2023/06/19) of `huggingface_hub`
+        steps.append({"run": {"name": "Split tests", "command": "pip uninstall -y huggingface_hub && pip install git+https://github.com/huggingface/huggingface_hub.git@92028da882e47d61703e8c9144028e1ecadab415"}})
         steps.append(
             {
                 "save_cache": {
@@ -117,7 +130,7 @@ class CircleCIJob:
         if self.command_timeout:
             test_command = f"timeout {self.command_timeout} "
         test_command += f"python -m pytest -n {self.pytest_num_workers} " + " ".join(pytest_flags)
-        
+
         if self.parallelism == 1:
             if self.tests_to_run is None:
                 test_command += " << pipeline.parameters.tests_to_run >>"
@@ -261,6 +274,7 @@ tf_job = CircleCIJob(
         "pip install tensorflow_probability",
     ],
     parallelism=1,
+    pytest_num_workers=6,
     pytest_options={"rA": None},
 )
 
@@ -342,7 +356,6 @@ examples_torch_job = CircleCIJob(
         "pip install .[sklearn,torch,sentencepiece,testing,torch-speech]",
         "pip install -r examples/pytorch/_tests_requirements.txt",
     ],
-    tests_to_run="./examples/pytorch/",
 )
 
 
@@ -355,7 +368,6 @@ examples_tensorflow_job = CircleCIJob(
         "pip install .[sklearn,tensorflow,sentencepiece,testing]",
         "pip install -r examples/tensorflow/_tests_requirements.txt",
     ],
-    tests_to_run="./examples/tensorflow/",
 )
 
 
@@ -367,7 +379,6 @@ examples_flax_job = CircleCIJob(
         "pip install .[flax,testing,sentencepiece]",
         "pip install -r examples/flax/_tests_requirements.txt",
     ],
-    tests_to_run="./examples/flax/",
 )
 
 
@@ -448,6 +459,7 @@ doc_test_job = CircleCIJob(
         "pip install -e .[dev]",
         "pip install git+https://github.com/huggingface/accelerate",
         "pip install --upgrade pytest pytest-sugar",
+        "pip install natten",
         "find -name __pycache__ -delete",
         "find . -name \*.pyc -delete",
         # Add an empty file to keep the test step running correctly even no file is selected to be tested.
@@ -483,7 +495,6 @@ REGULAR_TESTS = [
     hub_job,
     onnx_job,
     exotic_models_job,
-    doc_test_job
 ]
 EXAMPLES_TESTS = [
     examples_torch_job,
@@ -495,6 +506,8 @@ PIPELINE_TESTS = [
     pipelines_tf_job,
 ]
 REPO_UTIL_TESTS = [repo_utils_job]
+DOC_TESTS = [doc_test_job]
+
 
 def create_circleci_config(folder=None):
     if folder is None:
@@ -550,23 +563,43 @@ def create_circleci_config(folder=None):
 
     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)
+        with open(example_file, "r", encoding="utf-8") as f:
+            example_tests = f.read().split(" ")
+        for job in EXAMPLES_TESTS:
+            framework = job.name.replace("examples_", "").replace("torch", "pytorch")
+            if example_tests == "all":
+                job.tests_to_run = [f"examples/{framework}"]
+            else:
+                job.tests_to_run = [f for f in example_tests if f.startswith(f"examples/{framework}")]
+            
+            if len(job.tests_to_run) > 0:
+                jobs.append(job)
+
+    doctest_file = os.path.join(folder, "doctest_list.txt")
+    if os.path.exists(doctest_file):
+        with open(doctest_file) as f:
+            doctest_list = f.read()
+    else:
+        doctest_list = []
+    if len(doctest_list) > 0:
+        jobs.extend(DOC_TESTS)
 
     repo_util_file = os.path.join(folder, "test_repo_utils.txt")
     if os.path.exists(repo_util_file) and os.path.getsize(repo_util_file) > 0:
         jobs.extend(REPO_UTIL_TESTS)
 
-    if len(jobs) > 0:
-        config = {"version": "2.1"}
-        config["parameters"] = {
-            # Only used to accept the parameters from the trigger
-            "nightly": {"type": "boolean", "default": False},
-            "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 len(jobs) == 0:
+        jobs = [EmptyJob()]
+    config = {"version": "2.1"}
+    config["parameters"] = {
+        # Only used to accept the parameters from the trigger
+        "nightly": {"type": "boolean", "default": False},
+        "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__":

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -41,7 +41,7 @@ body:
         
         Integrations:
         
-          - deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
+          - deepspeed: HF Trainer/Accelerate: @pacman100
           - ray/raytune: @richardliaw, @amogkam
           - Big Model Inference: @sgugger @muellerzr
         

.github/PULL_REQUEST_TEMPLATE.md

@@ -55,7 +55,7 @@ Library:
 
 Integrations:
 
-- deepspeed: HF Trainer: @stas00, Accelerate: @pacman100
+- deepspeed: HF Trainer/Accelerate: @pacman100
 - ray/raytune: @richardliaw, @amogkam
 
 Documentation: @sgugger, @stevhliu and @MKhalusova

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

@@ -71,6 +71,16 @@ jobs:
     name: "Latest PyTorch + DeepSpeed"
     runs-on: ubuntu-latest
     steps:
+      - name: Cleanup disk
+        run: |
+          sudo ls -l /usr/local/lib/
+          sudo ls -l /usr/share/
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
+          sudo rm -rf /usr/local/lib/android
+          sudo rm -rf /usr/share/dotnet
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
       -
         name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
@@ -98,6 +108,16 @@ jobs:
     name: "Latest PyTorch + DeepSpeed (Push CI - Daily Build)"
     runs-on: ubuntu-latest
     steps:
+      - name: Cleanup disk
+        run: |
+          sudo ls -l /usr/local/lib/
+          sudo ls -l /usr/share/
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
+          sudo rm -rf /usr/local/lib/android
+          sudo rm -rf /usr/share/dotnet
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
       -
         name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2
@@ -156,6 +176,16 @@ jobs:
     if: inputs.image_postfix != '-push-ci'
     runs-on: ubuntu-latest
     steps:
+      - name: Cleanup disk
+        run: |
+          sudo ls -l /usr/local/lib/
+          sudo ls -l /usr/share/
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
+          sudo rm -rf /usr/local/lib/android
+          sudo rm -rf /usr/share/dotnet
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
       -
         name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2

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

@@ -52,6 +52,16 @@ jobs:
     name: "Nightly PyTorch + DeepSpeed"
     runs-on: ubuntu-latest
     steps:
+      - name: Cleanup disk
+        run: |
+          sudo ls -l /usr/local/lib/
+          sudo ls -l /usr/share/
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
+          sudo rm -rf /usr/local/lib/android
+          sudo rm -rf /usr/share/dotnet
+          sudo du -sh /usr/local/lib/
+          sudo du -sh /usr/share/
       -
         name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v2

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

@@ -15,7 +15,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        version: ["1.13", "1.12", "1.11", "1.10", "1.9"]
+        version: ["1.13", "1.12", "1.11", "1.10"]
     runs-on: ubuntu-latest
     steps:
       -

.github/workflows/build_documentation.yml

@@ -18,3 +18,4 @@ jobs:
       languages: de en es fr it ko pt zh
     secrets:
       token: ${{ secrets.HUGGINGFACE_PUSH }}
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}

.github/workflows/delete_doc_comment.yml

@@ -1,13 +1,14 @@
-name: Delete dev documentation
+name: Delete doc comment
 
 on:
-  pull_request:
-    types: [ closed ]
+  workflow_run:
+    workflows: ["Delete doc comment trigger"]
+    types:
+      - completed
 
 
 jobs:
   delete:
     uses: huggingface/doc-builder/.github/workflows/delete_doc_comment.yml@main
-    with:
-      pr_number: ${{ github.event.number }}
-      package: transformers
+    secrets:
+      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

.github/workflows/delete_doc_comment_trigger.yml

@@ -0,0 +1,12 @@
+name: Delete doc comment trigger
+
+on:
+  pull_request:
+    types: [ closed ]
+
+
+jobs:
+  delete:
+    uses: huggingface/doc-builder/.github/workflows/delete_doc_comment_trigger.yml@main
+    with:
+      pr_number: ${{ github.event.number }}

.github/workflows/self-nightly-past-ci-caller.yml

@@ -67,21 +67,10 @@ jobs:
       sha: ${{ github.sha }}
     secrets: inherit
 
-  run_past_ci_pytorch_1-9:
-    name: PyTorch 1.9
-    if: (cancelled() != true) && ((github.event_name == 'schedule') || ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci')))
-    needs: [run_past_ci_pytorch_1-10]
-    uses: ./.github/workflows/self-past.yml
-    with:
-      framework: pytorch
-      version: "1.9"
-      sha: ${{ github.sha }}
-    secrets: inherit
-
   run_past_ci_tensorflow_2-11:
     name: TensorFlow 2.11
     if: (cancelled() != true) && ((github.event_name == 'push') && startsWith(github.ref_name, 'run_past_ci'))
-    needs: [run_past_ci_pytorch_1-9]
+    needs: [run_past_ci_pytorch_1-10]
     uses: ./.github/workflows/self-past.yml
     with:
       framework: tensorflow

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

@@ -115,6 +115,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -172,6 +176,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -217,6 +225,10 @@ jobs:
         working-directory: /workspace/transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /workspace/transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Remove cached torch extensions
         run: rm -rf /github/home/.cache/torch_extensions/
 

.github/workflows/self-past.yml

@@ -111,6 +111,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ inputs.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
         # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
@@ -183,6 +187,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ inputs.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
         # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
@@ -255,6 +263,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Install
         working-directory: /transformers
         run: |

.github/workflows/self-push.yml

@@ -195,6 +195,10 @@ jobs:
           git checkout ${{ env.CI_SHA }}
           echo "log = $(git log -n 1)"
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
         # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
@@ -284,6 +288,10 @@ jobs:
           git checkout ${{ env.CI_SHA }}
           echo "log = $(git log -n 1)"
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Echo folder ${{ matrix.folders }}
         shell: bash
         # For folders like `models/bert`, set an env. var. (`matrix_folders`) to `models_bert`, which will be used to
@@ -373,6 +381,10 @@ jobs:
           git checkout ${{ env.CI_SHA }}
           echo "log = $(git log -n 1)"
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /workspace/transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Remove cached torch extensions
         run: rm -rf /github/home/.cache/torch_extensions/
 
@@ -459,6 +471,10 @@ jobs:
           git checkout ${{ env.CI_SHA }}
           echo "log = $(git log -n 1)"
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /workspace/transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Remove cached torch extensions
         run: rm -rf /github/home/.cache/torch_extensions/
 

.github/workflows/self-scheduled.yml

@@ -119,6 +119,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -176,6 +180,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -221,6 +229,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -268,6 +280,10 @@ jobs:
         working-directory: /transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -315,6 +331,10 @@ jobs:
         run: |
           git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: NVIDIA-SMI
         run: |
           nvidia-smi
@@ -361,6 +381,10 @@ jobs:
         working-directory: /workspace/transformers
         run: git fetch && git checkout ${{ github.sha }}
 
+      - name: Reinstall transformers in edit mode (remove the one installed during docker image build)
+        working-directory: /workspace/transformers
+        run: python3 -m pip uninstall -y transformers && python3 -m pip install -e .
+
       - name: Remove cached torch extensions
         run: rm -rf /github/home/.cache/torch_extensions/
 
@@ -369,7 +393,7 @@ jobs:
         working-directory: /workspace
         run: |
           python3 -m pip uninstall -y deepspeed
-          DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install deepspeed --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
+          DS_BUILD_CPU_ADAM=1 DS_BUILD_FUSED_ADAM=1 DS_BUILD_UTILS=1 python3 -m pip install deepspeed==0.9.2 --global-option="build_ext" --global-option="-j8" --no-cache -v --disable-pip-version-check
 
       - name: NVIDIA-SMI
         run: |

.github/workflows/upload_pr_documentation.yml

@@ -0,0 +1,16 @@
+name: Upload PR Documentation
+
+on:
+  workflow_run:
+    workflows: ["Build PR Documentation"]
+    types:
+      - completed
+
+jobs:
+  build:
+    uses: huggingface/doc-builder/.github/workflows/upload_pr_documentation.yml@main
+    with:
+      package_name: transformers
+    secrets:
+      hf_token: ${{ secrets.HF_DOC_BUILD_PUSH }}
+      comment_bot_token: ${{ secrets.COMMENT_BOT_TOKEN }}

CONTRIBUTING.md

@@ -275,7 +275,7 @@ You'll need **[Python 3.7]((https://github.com/huggingface/transformers/blob/mai
 request description to make sure they are linked (and people viewing the issue know you
 are working on it).<br>
 ☐ To indicate a work in progress please prefix the title with `[WIP]`. These are
-useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.
+useful to avoid duplicated work, and to differentiate it from PRs ready to be merged.<br>
 ☐ Make sure existing tests pass.<br>
 ☐ If adding a new feature, also add tests for it.<br>
    - If you are adding a new model, make sure you use
@@ -284,7 +284,7 @@ useful to avoid duplicated work, and to differentiate it from PRs ready to be me
      `RUN_SLOW=1 python -m pytest tests/models/my_new_model/test_my_new_model.py`.
    - If you are adding a new tokenizer, write tests and make sure
      `RUN_SLOW=1 python -m pytest tests/models/{your_model_name}/test_tokenization_{your_model_name}.py` passes.
-   CircleCI does not run the slow tests, but GitHub Actions does every night!<br>
+   - CircleCI does not run the slow tests, but GitHub Actions does every night!<br>
 
 ☐ All public methods must have informative docstrings (see
 [`modeling_bert.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/bert/modeling_bert.py)

MANIFEST.in

@@ -1 +0,0 @@
-include LICENSE

Makefile

@@ -111,3 +111,10 @@ post-release:
 
 post-patch:
 	python utils/release.py --post_release --patch
+
+build-release:
+	rm -rf dist
+	rm -rf build
+	python setup.py bdist_wheel
+	python setup.py sdist
+	python utils/check_build.py

README.md

@@ -115,6 +115,19 @@ In Multimodal tasks:
 
 **[Write With Transformer](https://transformer.huggingface.co)**, built by the Hugging Face team, is the official demo of this repo’s text generation capabilities.
 
+
+## 100 projects using Transformers
+
+Transformers is more than a toolkit to use pretrained models: it's a community of projects built around it and the 
+Hugging Face Hub. We want Transformers to enable developers, researchers, students, professors, engineers, and anyone 
+else to build their dream projects.
+
+In order to celebrate the 100,000 stars of transformers, we have decided to put the spotlight on the
+community, and we have created the [awesome-transformers](./awesome-transformers.md) page which lists 100
+incredible projects built in the vicinity of transformers.
+
+If you own or use a project that you believe should be part of the list, please open a PR to add it!
+
 ## If you are looking for custom support from the Hugging Face team
 
 <a target="_blank" href="https://huggingface.co/support">
@@ -236,7 +249,7 @@ The model itself is a regular [Pytorch `nn.Module`](https://pytorch.org/docs/sta
 
 ### With pip
 
-This repository is tested on Python 3.6+, Flax 0.3.2+, PyTorch 1.3.1+ and TensorFlow 2.3+.
+This repository is tested on Python 3.7+, Flax 0.4.1+, PyTorch 1.9+ and TensorFlow 2.4+.
 
 You should install 🤗 Transformers in a [virtual environment](https://docs.python.org/3/library/venv.html). If you're unfamiliar with Python virtual environments, check out the [user guide](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/).
 
@@ -279,6 +292,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 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.
@@ -332,6 +346,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 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. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
 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. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
@@ -382,15 +397,17 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 1. **[MatCha](https://huggingface.co/docs/transformers/model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
 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. **[MEGA](https://huggingface.co/docs/transformers/model_doc/mega)** (from Facebook) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
+1. **[MEGA](https://huggingface.co/docs/transformers/model_doc/mega)** (from Meta/USC/CMU/SJTU) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
 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. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
 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. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
 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. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
 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. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) 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.
@@ -432,7 +449,7 @@ Current number of checkpoints: ![](https://img.shields.io/endpoint?url=https://h
 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. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
 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. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.

README_es.md

@@ -267,6 +267,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 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.
@@ -320,6 +321,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 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. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
 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. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
@@ -375,10 +377,12 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 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. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
 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. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
 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. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
 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. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) 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.
@@ -420,7 +424,7 @@ Número actual de puntos de control: ![](https://img.shields.io/endpoint?url=htt
 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. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
 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. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.

README_hd.md

@@ -239,6 +239,7 @@ conda install -c huggingface transformers
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (Google Research से) Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. द्वाराअनुसंधान पत्र [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) के साथ जारी किया गया
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (फेसबुक) साथ थीसिस [बार्ट: प्राकृतिक भाषा निर्माण, अनुवाद के लिए अनुक्रम-से-अनुक्रम पूर्व प्रशिक्षण , और समझ] (https://arxiv.org/pdf/1910.13461.pdf) पर निर्भर माइक लुईस, यिनहान लियू, नमन गोयल, मार्जन ग़ज़विनिनेजाद, अब्देलरहमान मोहम्मद, ओमर लेवी, वेस स्टोयानोव और ल्यूक ज़ेटलमॉयर
 1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (से École polytechnique) साथ थीसिस [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) पर निर्भर Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis रिहाई।
 1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (VinAI Research से) साथ में पेपर [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701)गुयेन लुओंग ट्रान, डुओंग मिन्ह ले और डाट क्वोक गुयेन द्वारा पोस्ट किया गया।
@@ -292,6 +293,7 @@ conda install -c huggingface transformers
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (Google रिसर्च/स्टैनफोर्ड यूनिवर्सिटी से) साथ में दिया गया पेपर [इलेक्ट्रा: जेनरेटर के बजाय भेदभाव करने वाले के रूप में टेक्स्ट एन्कोडर्स का पूर्व-प्रशिक्षण] (https://arxiv.org/abs/2003.10555) केविन क्लार्क, मिन्ह-थांग लुओंग, क्वोक वी. ले, क्रिस्टोफर डी. मैनिंग द्वारा पोस्ट किया गया।
+1. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (Meta AI से) Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi. द्वाराअनुसंधान पत्र [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) के साथ जारी किया गया
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (Google रिसर्च से) साथ में दिया गया पेपर [सीक्वेंस जेनरेशन टास्क के लिए प्री-ट्रेंड चेकपॉइंट का इस्तेमाल करना](https:/ /arxiv.org/abs/1907.12461) साशा रोठे, शशि नारायण, अलियाक्सि सेवेरिन द्वारा।
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)**(Baidu से) साथ देने वाला पेपर [ERNIE: एन्हांस्ड रिप्रेजेंटेशन थ्रू नॉलेज इंटीग्रेशन](https://arxiv.org/abs/1904.09223) यू सन, शुओहुआन वांग, युकुन ली, शिकुन फेंग, ज़ुई चेन, हान झांग, शिन तियान, डैनक्सियांग झू, हाओ तियान, हुआ वू द्वारा पोस्ट किया गया।
 1. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (Baidu से) Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang. द्वाराअनुसंधान पत्र [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) के साथ जारी किया गया
@@ -347,10 +349,12 @@ conda install -c huggingface transformers
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (NVIDIA से) साथ वाला पेपर [Megatron-LM: ट्रेनिंग मल्टी-बिलियन पैरामीटर लैंग्वेज मॉडल्स यूजिंग मॉडल पैरेललिज़्म] (https://arxiv.org/abs/1909.08053) मोहम्मद शोएबी, मोस्टोफा पटवारी, राउल पुरी, पैट्रिक लेग्रेस्ले, जेरेड कैस्पर और ब्रायन कैटानज़ारो द्वारा पोस्ट किया गया।
 1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (Alibaba Research से) Peng Wang, Cheng Da, and Cong Yao. द्वाराअनुसंधान पत्र [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) के साथ जारी किया गया
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (फ्रॉम Studio Ousia) साथ में पेपर [mLUKE: द पावर ऑफ एंटिटी रिप्रेजेंटेशन इन मल्टीलिंगुअल प्रीट्रेन्ड लैंग्वेज मॉडल्स](https://arxiv.org/abs/2110.08151) रयोकन री, इकुया यामाडा, और योशिमासा त्सुरोका द्वारा।
+1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (Facebook से) Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli. द्वाराअनुसंधान पत्र [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) के साथ जारी किया गया
 1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (सीएमयू/गूगल ब्रेन से) साथ में कागज [मोबाइलबर्ट: संसाधन-सीमित उपकरणों के लिए एक कॉम्पैक्ट टास्क-अज्ञेय बीईआरटी] (https://arxiv.org/abs/2004.02984) Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, और Denny Zhou द्वारा पोस्ट किया गया।
 1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
 1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (Apple से) साथ में कागज [MobileViT: लाइट-वेट, जनरल-पर्पस, और मोबाइल-फ्रेंडली विजन ट्रांसफॉर्मर] (https://arxiv.org/abs/2110.02178) सचिन मेहता और मोहम्मद रस्तगरी द्वारा पोस्ट किया गया।
+1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (Apple से) Sachin Mehta and Mohammad Rastegari. द्वाराअनुसंधान पत्र [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) के साथ जारी किया गया
 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)** (Google AI से) साथ वाला पेपर [mT5: एक व्यापक बहुभाषी पूर्व-प्रशिक्षित टेक्स्ट-टू-टेक्स्ट ट्रांसफॉर्मर]( https://arxiv.org/abs/2010.11934) लिंटिंग ज़ू, नोआ कॉन्सटेंट, एडम रॉबर्ट्स, मिहिर काले, रामी अल-रफू, आदित्य सिद्धांत, आदित्य बरुआ, कॉलिन रैफेल द्वारा पोस्ट किया गया।
 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.
@@ -392,7 +396,7 @@ conda install -c huggingface transformers
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (फेसबुक से) साथ में पेपर [लार्ज-स्केल सेल्फ- एंड सेमी-सुपरवाइज्ड लर्निंग फॉर स्पीच ट्रांसलेशन](https://arxiv.org/abs/2104.06678) चांगहान वांग, ऐनी वू, जुआन पिनो, एलेक्सी बेवस्की, माइकल औली, एलेक्सिस द्वारा Conneau द्वारा पोस्ट किया गया।
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (तेल अवीव यूनिवर्सिटी से) साथ में पेपर [स्पैन सिलेक्शन को प्री-ट्रेनिंग करके कुछ-शॉट क्वेश्चन आंसरिंग](https:// arxiv.org/abs/2101.00438) ओरि राम, युवल कर्स्टन, जोनाथन बेरेंट, अमीर ग्लोबर्सन, ओमर लेवी द्वारा।
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (बर्कले से) कागज के साथ [SqueezeBERT: कुशल तंत्रिका नेटवर्क के बारे में NLP को कंप्यूटर विज़न क्या सिखा सकता है?](https: //arxiv.org/abs/2006.11316) फॉरेस्ट एन. इनडोला, अल्बर्ट ई. शॉ, रवि कृष्णा, और कर्ट डब्ल्यू. केटज़र द्वारा।
-1. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (MBZUAI से) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. द्वाराअनुसंधान पत्र [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) के साथ जारी किया गया
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI से) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. द्वाराअनुसंधान पत्र [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) के साथ जारी किया गया
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (माइक्रोसॉफ्ट से) साथ में कागज [स्वाइन ट्रांसफॉर्मर: शिफ्टेड विंडोज का उपयोग कर पदानुक्रमित विजन ट्रांसफॉर्मर](https://arxiv .org/abs/2103.14030) ज़ी लियू, युटोंग लिन, यू काओ, हान हू, यिक्सुआन वेई, झेंग झांग, स्टीफन लिन, बैनिंग गुओ द्वारा।
 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft से) साथ वाला पेपर [Swin Transformer V2: स्केलिंग अप कैपेसिटी एंड रेजोल्यूशन](https:// ज़ी लियू, हान हू, युटोंग लिन, ज़ुलिआंग याओ, ज़ेंडा ज़ी, यिक्सुआन वेई, जिया निंग, यू काओ, झेंग झांग, ली डोंग, फुरु वेई, बैनिंग गुओ द्वारा arxiv.org/abs/2111.09883।
 1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.

README_ja.md

@@ -301,6 +301,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (Google Research から) Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig. から公開された研究論文 [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918)
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (BAAI から) Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell から公開された研究論文: [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679)
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (MIT から) Yuan Gong, Yu-An Chung, James Glass から公開された研究論文: [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778)
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (Facebook から) Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer から公開された研究論文: [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/abs/1910.13461)
 1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (École polytechnique から) Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis から公開された研究論文: [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321)
 1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (VinAI Research から) Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen から公開された研究論文: [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701)
@@ -354,6 +355,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (Snap Research から) Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren. から公開された研究論文 [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191)
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (Google Research/Stanford University から) Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning から公開された研究論文: [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555)
+1. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (Meta AI から) Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi. から公開された研究論文 [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438)
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (Google Research から) Sascha Rothe, Shashi Narayan, Aliaksei Severyn から公開された研究論文: [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461)
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (Baidu から) Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu から公開された研究論文: [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223)
 1. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (Baidu から) Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang. から公開された研究論文 [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674)
@@ -409,10 +411,12 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (NVIDIA から) Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro から公開された研究論文: [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053)
 1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (Alibaba Research から) Peng Wang, Cheng Da, and Cong Yao. から公開された研究論文 [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592)
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (Studio Ousia から) Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka から公開された研究論文: [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151)
+1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (Facebook から) Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli. から公開された研究論文 [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516)
 1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (CMU/Google Brain から) Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou から公開された研究論文: [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984)
 1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (Google Inc. から) Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam から公開された研究論文: [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (Google Inc. から) Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen から公開された研究論文: [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
 1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (Apple から) Sachin Mehta and Mohammad Rastegari から公開された研究論文: [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178)
+1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (Apple から) Sachin Mehta and Mohammad Rastegari. から公開された研究論文 [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680)
 1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (Microsoft Research から) Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu から公開された研究論文: [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297)
 1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (Google AI から) Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel から公開された研究論文: [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934)
 1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (RUC AI Box から) Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen から公開された研究論文: [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131)
@@ -454,7 +458,7 @@ Flax、PyTorch、TensorFlowをcondaでインストールする方法は、それ
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (Facebook から), Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau から公開された研究論文: [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678)
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (Tel Aviv University から), Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy から公開された研究論文: [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438)
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (Berkeley から) Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer から公開された研究論文: [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316)
-1. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (MBZUAI から) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. から公開された研究論文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI から) Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan. から公開された研究論文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (Microsoft から) Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo から公開された研究論文: [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft から) Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo から公開された研究論文: [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883)
 1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (University of Würzburg から) Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte から公開された研究論文: [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345)

README_ko.md

@@ -216,6 +216,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (Google Research 에서 제공)은 Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.의 [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918)논문과 함께 발표했습니다.
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 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/pdf/1910.13461.pdf) 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.
@@ -269,6 +270,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 1. **[ELECTRA](https://huggingface.co/docs/transformers/model_doc/electra)** (Google Research/Stanford University 에서) Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning 의 [ELECTRA: Pre-training text encoders as discriminators rather than generators](https://arxiv.org/abs/2003.10555) 논문과 함께 발표했습니다.
+1. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (Meta AI 에서 제공)은 Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.의 [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438)논문과 함께 발표했습니다.
 1. **[EncoderDecoder](https://huggingface.co/docs/transformers/model_doc/encoder-decoder)** (Google Research 에서) Sascha Rothe, Shashi Narayan, Aliaksei Severyn 의 [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://arxiv.org/abs/1907.12461) 논문과 함께 발표했습니다.
 1. **[ERNIE](https://huggingface.co/docs/transformers/model_doc/ernie)** (Baidu 에서) Yu Sun, Shuohuan Wang, Yukun Li, Shikun Feng, Xuyi Chen, Han Zhang, Xin Tian, Danxiang Zhu, Hao Tian, Hua Wu 의 [ERNIE: Enhanced Representation through Knowledge Integration](https://arxiv.org/abs/1904.09223) 논문과 함께 발표했습니다.
 1. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (Baidu 에서 제공)은 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.의 [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674)논문과 함께 발표했습니다.
@@ -324,10 +326,12 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (NVIDIA 에서) Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro 의 [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/abs/1909.08053) 논문과 함께 발표했습니다.
 1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (Alibaba Research 에서 제공)은 Peng Wang, Cheng Da, and Cong Yao.의 [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592)논문과 함께 발표했습니다.
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (Studio Ousia 에서) Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka 의 [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) 논문과 함께 발표했습니다.
+1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (Facebook 에서 제공)은 Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.의 [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516)논문과 함께 발표했습니다.
 1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (CMU/Google Brain 에서) Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou 의 [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) 논문과 함께 발표했습니다.
 1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (Google Inc. 에서) Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam 의 [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) 논문과 함께 발표했습니다.
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (Google Inc. 에서) Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen 의 [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) 논문과 함께 발표했습니다.
 1. **[MobileViT](https://huggingface.co/docs/transformers/model_doc/mobilevit)** (Apple 에서) Sachin Mehta and Mohammad Rastegari 의 [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://arxiv.org/abs/2110.02178) 논문과 함께 발표했습니다.
+1. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (Apple 에서 제공)은 Sachin Mehta and Mohammad Rastegari.의 [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680)논문과 함께 발표했습니다.
 1. **[MPNet](https://huggingface.co/docs/transformers/model_doc/mpnet)** (Microsoft Research 에서) Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu 의 [MPNet: Masked and Permuted Pre-training for Language Understanding](https://arxiv.org/abs/2004.09297) 논문과 함께 발표했습니다.
 1. **[MT5](https://huggingface.co/docs/transformers/model_doc/mt5)** (Google AI 에서) Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel 의 [mT5: A massively multilingual pre-trained text-to-text transformer](https://arxiv.org/abs/2010.11934) 논문과 함께 발표했습니다.
 1. **[MVP](https://huggingface.co/docs/transformers/model_doc/mvp)** (RUC AI Box 에서) Tianyi Tang, Junyi Li, Wayne Xin Zhao and Ji-Rong Wen 의 [MVP: Multi-task Supervised Pre-training for Natural Language Generation](https://arxiv.org/abs/2206.12131) 논문과 함께 발표했습니다.
@@ -369,7 +373,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (Facebook 에서) Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 의 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 논문과 함께 발표했습니다.
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (Tel Aviv University 에서) Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 의 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 논문과 함께 발표했습니다.
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (Berkeley 에서) Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 의 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 논문과 함께 발표했습니다.
-1. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (MBZUAI 에서 제공)은 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.의 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)논문과 함께 발표했습니다.
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (MBZUAI 에서 제공)은 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.의 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446)논문과 함께 발표했습니다.
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (Microsoft 에서) Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 의 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 논문과 함께 발표했습니다.
 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (Microsoft 에서) Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 의 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 논문과 함께 발표했습니다.
 1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (University of Würzburg 에서) Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte 의 [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) 논문과 함께 발표했습니다.

README_zh-hans.md

@@ -240,6 +240,7 @@ conda install -c huggingface transformers
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (来自 Google Research) 伴随论文 [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) 由 Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig 发布。
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (来自 BAAI) 伴随论文 [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) 由 Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell 发布。
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (来自 MIT) 伴随论文 [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) 由 Yuan Gong, Yu-An Chung, James Glass 发布。
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 1. **[BART](https://huggingface.co/docs/transformers/model_doc/bart)** (来自 Facebook) 伴随论文 [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://arxiv.org/pdf/1910.13461.pdf) 由 Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer 发布。
 1. **[BARThez](https://huggingface.co/docs/transformers/model_doc/barthez)** (来自 École polytechnique) 伴随论文 [BARThez: a Skilled Pretrained French Sequence-to-Sequence Model](https://arxiv.org/abs/2010.12321) 由 Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis 发布。
 1. **[BARTpho](https://huggingface.co/docs/transformers/model_doc/bartpho)** (来自 VinAI Research) 伴随论文 [BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese](https://arxiv.org/abs/2109.09701) 由 Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen 发布。
@@ -293,6 +294,7 @@ conda install -c huggingface transformers
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (来自 Snap Research) 伴随论文 [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) 由 Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren 发布。
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 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. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (来自 Meta AI) 伴随论文 [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) 由 Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi 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. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (来自 Baidu) 伴随论文 [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) 由 Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang 发布。
@@ -339,7 +341,7 @@ conda install -c huggingface transformers
 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. **[Mask2Former](https://huggingface.co/docs/transformers/model_doc/mask2former)** (来自 FAIR and UIUC) 伴随论文 [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) 由 Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar 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
+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. **[MatCha](https://huggingface.co/docs/transformers/model_doc/matcha)** (来自 Google AI) 伴随论文 [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) 由 Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos 发布。
 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。
@@ -348,10 +350,12 @@ conda install -c huggingface transformers
 1. **[Megatron-GPT2](https://huggingface.co/docs/transformers/model_doc/megatron_gpt2)** (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。
 1. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (来自 Alibaba Research) 伴随论文 [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) 由 Peng Wang, Cheng Da, and Cong Yao 发布。
 1. **[mLUKE](https://huggingface.co/docs/transformers/model_doc/mluke)** (来自 Studio Ousia) 伴随论文 [mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models](https://arxiv.org/abs/2110.08151) 由 Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka 发布。
+1. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (来自 Facebook) 伴随论文 [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) 由 Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli 发布。
 1. **[MobileBERT](https://huggingface.co/docs/transformers/model_doc/mobilebert)** (来自 CMU/Google Brain) 伴随论文 [MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://arxiv.org/abs/2004.02984) 由 Zhiqing Sun, Hongkun Yu, Xiaodan Song, Renjie Liu, Yiming Yang, and Denny Zhou 发布。
 1. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (来自 Google Inc.) 伴随论文 [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) 由 Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam 发布。
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (来自 Google Inc.) 伴随论文 [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) 由 Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen 发布。
 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. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (来自 Apple) 伴随论文 [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) 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。
@@ -393,7 +397,7 @@ conda install -c huggingface transformers
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (来自 Facebook) 伴随论文 [Large-Scale Self- and Semi-Supervised Learning for Speech Translation](https://arxiv.org/abs/2104.06678) 由 Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau 发布。
 1. **[Splinter](https://huggingface.co/docs/transformers/model_doc/splinter)** (来自 Tel Aviv University) 伴随论文 [Few-Shot Question Answering by Pretraining Span Selection](https://arxiv.org/abs/2101.00438) 由 Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy 发布。
 1. **[SqueezeBERT](https://huggingface.co/docs/transformers/model_doc/squeezebert)** (来自 Berkeley) 伴随论文 [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](https://arxiv.org/abs/2006.11316) 由 Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer 发布。
-1. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (来自 MBZUAI) 伴随论文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) 由 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan 发布。
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (来自 MBZUAI) 伴随论文 [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) 由 Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan 发布。
 1. **[Swin Transformer](https://huggingface.co/docs/transformers/model_doc/swin)** (来自 Microsoft) 伴随论文 [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030) 由 Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo 发布。
 1. **[Swin Transformer V2](https://huggingface.co/docs/transformers/model_doc/swinv2)** (来自 Microsoft) 伴随论文 [Swin Transformer V2: Scaling Up Capacity and Resolution](https://arxiv.org/abs/2111.09883) 由 Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo 发布。
 1. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (来自 University of Würzburg) 伴随论文 [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) 由 Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte 发布。
@@ -450,7 +454,7 @@ conda install -c huggingface transformers
 
 | 章节 | 描述 |
 |-|-|
-| [文档](https://huggingface.co/transformers/) | 完整的 API 文档和教程 |
+| [文档](https://huggingface.co/docs/transformers/) | 完整的 API 文档和教程 |
 | [任务总结](https://huggingface.co/docs/transformers/task_summary) | 🤗 Transformers 支持的任务 |
 | [预处理教程](https://huggingface.co/docs/transformers/preprocessing) | 使用 `Tokenizer` 来为模型准备数据 |
 | [训练和微调](https://huggingface.co/docs/transformers/training) | 在 PyTorch/TensorFlow 的训练循环或 `Trainer` API 中使用 🤗 Transformers 提供的模型 |

README_zh-hant.md

@@ -252,6 +252,7 @@ conda install -c huggingface transformers
 1. **[ALIGN](https://huggingface.co/docs/transformers/model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
 1. **[AltCLIP](https://huggingface.co/docs/transformers/model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
 1. **[Audio Spectrogram Transformer](https://huggingface.co/docs/transformers/model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](https://huggingface.co/docs/transformers/model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 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/pdf/1910.13461.pdf) 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.
@@ -305,6 +306,7 @@ conda install -c huggingface transformers
 1. **[EfficientFormer](https://huggingface.co/docs/transformers/model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
 1. **[EfficientNet](https://huggingface.co/docs/transformers/model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 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. **[EnCodec](https://huggingface.co/docs/transformers/main/model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
 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. **[ErnieM](https://huggingface.co/docs/transformers/model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
@@ -360,10 +362,12 @@ conda install -c huggingface transformers
 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. **[MGP-STR](https://huggingface.co/docs/transformers/model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
 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. **[MMS](https://huggingface.co/docs/transformers/model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
 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. **[MobileNetV1](https://huggingface.co/docs/transformers/model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
 1. **[MobileNetV2](https://huggingface.co/docs/transformers/model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
 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. **[MobileViTV2](https://huggingface.co/docs/transformers/model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) 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.
@@ -405,7 +409,7 @@ conda install -c huggingface transformers
 1. **[SpeechToTextTransformer2](https://huggingface.co/docs/transformers/model_doc/speech_to_text_2)** (from Facebook) released 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 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. **[SwiftFormer](https://huggingface.co/docs/transformers/main/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
+1. **[SwiftFormer](https://huggingface.co/docs/transformers/model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
 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. **[Swin2SR](https://huggingface.co/docs/transformers/model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.

awesome-transformers.md

@@ -0,0 +1,603 @@
+# Awesome projects built with Transformers
+
+This page lists awesome projects built on top of Transformers. Transformers is more than a toolkit to use pretrained
+models: it's a community of projects built around it and the Hugging Face Hub. We want Transformers to enable
+developers, researchers, students, professors, engineers, and anyone else to build their dream projects.
+
+In this list, we showcase incredibly impactful and novel projects that have pushed the field forward. We celebrate
+100 of these projects as we reach the milestone of 100k stars as a community; but we're very open to pull requests
+adding other projects to the list. If you believe a project should be here and it's not, then please, open a PR 
+to add it.
+
+## [gpt4all](https://github.com/nomic-ai/gpt4all)
+
+[gpt4all](https://github.com/nomic-ai/gpt4all) is an ecosystem of open-source chatbots trained on massive collections of clean assistant data including code, stories and dialogue. It offers open-source, large language models such as LLaMA and GPT-J trained in an assistant-style.
+
+Keywords: Open-source, LLaMa, GPT-J, instruction, assistant
+
+## [recommenders](https://github.com/microsoft/recommenders)
+
+This repository contains examples and best practices for building recommendation systems, provided as Jupyter notebooks. It goes over several aspects required to build efficient recommendation systems: data preparation, modeling, evaluation, model selection & optimization, as well as operationalization
+
+Keywords: Recommender systems, AzureML
+
+## [lama-cleaner](https://github.com/Sanster/lama-cleaner)
+
+Image inpainting tool powered by Stable Diffusion. Remove any unwanted object, defect, people from your pictures or erase and replace anything on your pictures.
+
+Keywords: inpainting, SD, Stable Diffusion
+
+## [flair](https://github.com/flairNLP/flair)
+
+FLAIR is a powerful PyTorch NLP framework, convering several important tasks: NER, sentiment-analysis, part-of-speech tagging, text and document embeddings, among other things.
+
+Keywords: NLP, text embedding, document embedding, biomedical, NER, PoS, sentiment-analysis
+
+## [mindsdb](https://github.com/mindsdb/mindsdb)
+
+MindsDB is a low-code ML platform, which automates and integrates several ML frameworks into the data stack as "AI Tables" to streamline the integration of AI into applications, making it accessible to developers of all skill levels.
+
+Keywords: Database, low-code, AI table
+
+## [langchain](https://github.com/hwchase17/langchain)
+
+[langchain](https://github.com/hwchase17/langchain) is aimed at assisting in the development of apps merging both LLMs and other sources of knowledge. The library allows chaining calls to applications, creating a sequence across many tools.
+
+Keywords: LLMs, Large Language Models, Agents, Chains
+
+## [LlamaIndex](https://github.com/jerryjliu/llama_index)
+
+[LlamaIndex](https://github.com/jerryjliu/llama_index) is a project that provides a central interface to connect your LLM's with external data. It provides various kinds of indices and retreival mechanisms to perform different LLM tasks and obtain knowledge-augmented results.
+
+Keywords: LLMs, Large Language Models, Data Retrieval, Indices, Knowledge Augmentation 
+
+## [ParlAI](https://github.com/facebookresearch/ParlAI)
+
+[ParlAI](https://github.com/facebookresearch/ParlAI) is a python framework for sharing, training and testing dialogue models, from open-domain chitchat, to task-oriented dialogue, to visual question answering. It provides more than 100 datasets under the same API, a large zoo of pretrained models, a set of agents, and has several integrations.
+
+Keywords: Dialogue, Chatbots, VQA, Datasets, Agents
+
+## [sentence-transformers](https://github.com/UKPLab/sentence-transformers)
+
+This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various task. Text is embedding in vector space such that similar text is close and can efficiently be found using cosine similarity.
+
+Keywords: Dense vector representations, Text embeddings, Sentence embeddings
+
+## [ludwig](https://github.com/ludwig-ai/ludwig)
+
+Ludwig is a declarative machine learning framework that makes it easy to define machine learning pipelines using a simple and flexible data-driven configuration system. Ludwig is targeted at a wide variety of AI tasks. It provides a data-driven configuration system, training, prediction, and evaluation scripts, as well as a programmatic API.
+
+Keywords: Declarative, Data-driven, ML Framework
+
+## [InvokeAI](https://github.com/invoke-ai/InvokeAI)
+
+[InvokeAI](https://github.com/invoke-ai/InvokeAI) is an engine for Stable Diffusion models, aimed at professionals, artists, and enthusiasts. It leverages the latest AI-driven technologies through CLI as well as a WebUI.
+
+Keywords: Stable-Diffusion, WebUI, CLI
+
+## [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP)
+
+[PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP) is an easy-to-use and powerful NLP library particularly targeted at the Chinese languages. It has support for multiple pre-trained model zoos, and supports a wide-range of NLP tasks from research to industrial applications.
+
+Keywords: NLP, Chinese, Research, Industry
+
+## [stanza](https://github.com/stanfordnlp/stanza)
+
+The Stanford NLP Group's official Python NLP library. It contains support for running various accurate natural language processing tools on 60+ languages and for accessing the Java Stanford CoreNLP software from Python.
+
+Keywords: NLP, Multilingual, CoreNLP
+
+## [DeepPavlov](https://github.com/deeppavlov/DeepPavlov)
+
+[DeepPavlov](https://github.com/deeppavlov/DeepPavlov) is an open-source conversational AI library. It is designed for the development of production ready chat-bots and complex conversational systems, as well as research in the area of NLP and, particularly, of dialog systems.
+
+Keywords: Conversational, Chatbot, Dialog
+
+## [alpaca-lora](https://github.com/tloen/alpaca-lora)
+
+Alpaca-lora contains code for reproducing the Stanford Alpaca results using low-rank adaptation (LoRA). The repository provides training (fine-tuning) as well as generation scripts.
+
+Keywords: LoRA, Parameter-efficient fine-tuning
+
+## [imagen-pytorch](https://github.com/lucidrains/imagen-pytorch)
+
+An open-source Implementation of Imagen, Google's closed-source Text-to-Image Neural Network that beats DALL-E2. As of release, it is the new SOTA for text-to-image synthesis.
+
+Keywords: Imagen, Text-to-image
+
+## [adapter-transformers](https://github.com/adapter-hub/adapter-transformers)
+
+[adapter-transformers](https://github.com/adapter-hub/adapter-transformers) is an extension of HuggingFace's Transformers library, integrating adapters into state-of-the-art language models by incorporating AdapterHub, a central repository for pre-trained adapter modules. It is a drop-in replacement for transformers, which is regularly updated to stay up-to-date with the developments of transformers.
+
+Keywords: Adapters, LoRA, Parameter-efficient fine-tuning, Hub
+
+## [NeMo](https://github.com/NVIDIA/NeMo)
+
+NVIDIA [NeMo](https://github.com/NVIDIA/NeMo) is a conversational AI toolkit built for researchers working on automatic speech recognition (ASR), text-to-speech synthesis (TTS), large language models (LLMs), and natural language processing (NLP). The primary objective of [NeMo](https://github.com/NVIDIA/NeMo) is to help researchers from industry and academia to reuse prior work (code and pretrained models) and make it easier to create new https://developer.nvidia.com/conversational-ai#started.
+
+Keywords: Conversational, ASR, TTS, LLMs, NLP
+
+## [Runhouse](https://github.com/run-house/runhouse)
+
+[Runhouse](https://github.com/run-house/runhouse) allows to send code and data to any of your compute or data infra, all in Python, and continue to interact with them normally from your existing code and environment. Runhouse developers mention:
+
+> Think of it as an expansion pack to your Python interpreter that lets it take detours to remote machines or manipulate remote data.
+
+Keywords: MLOps, Infrastructure, Data storage, Modeling
+
+## [MONAI](https://github.com/Project-MONAI/MONAI)
+
+[MONAI](https://github.com/Project-MONAI/MONAI) is a PyTorch-based, open-source framework for deep learning in healthcare imaging, part of PyTorch Ecosystem. Its ambitions are:
+- developing a community of academic, industrial and clinical researchers collaborating on a common foundation;
+- creating state-of-the-art, end-to-end training workflows for healthcare imaging;
+- providing researchers with the optimized and standardized way to create and evaluate deep learning models.
+
+Keywords: Healthcare imaging, Training, Evaluation
+
+## [simpletransformers](https://github.com/ThilinaRajapakse/simpletransformers)
+
+Simple Transformers lets you quickly train and evaluate Transformer models. Only 3 lines of code are needed to initialize, train, and evaluate a model. It supports a wide variety of NLP tasks.
+
+Keywords: Framework, simplicity, NLP
+
+## [JARVIS](https://github.com/microsoft/JARVIS)
+
+[JARVIS](https://github.com/microsoft/JARVIS) is a system attempting to merge LLMs such as GPT-4 with the rest of the open-source ML community: leveraging up to 60 downstream models in order to perform tasks identified by the LLM.
+
+Keywords: LLM, Agents, HF Hub
+
+## [transformers.js](https://xenova.github.io/transformers.js/)
+
+[transformers.js](https://xenova.github.io/transformers.js/) is a JavaScript library targeted at running models from transformers directly within the browser.
+
+Keywords: Transformers, JavaScript, browser
+
+## [bumblebee](https://github.com/elixir-nx/bumblebee)
+
+Bumblebee provides pre-trained Neural Network models on top of Axon, a neural networks library for the Elixir language. It includes integration with 🤗 Models, allowing anyone to download and perform Machine Learning tasks with few lines of code.
+
+Keywords: Elixir, Axon
+
+## [argilla](https://github.com/argilla-io/argilla)
+
+Argilla is an open-source platform providing advanced NLP labeling, monitoring, and workspaces. It is compatible with many open source ecosystems such as Hugging Face, Stanza, FLAIR, and others.
+
+Keywords: NLP, Labeling, Monitoring, Workspaces
+
+## [haystack](https://github.com/deepset-ai/haystack)
+
+Haystack is an open source NLP framework to interact with your data using Transformer models and LLMs. It offers production-ready tools to quickly build complex decision making, question answering, semantic search, text generation applications, and more.
+
+Keywords: NLP, Framework, LLM
+
+## [spaCy](https://github.com/explosion/spaCy)
+
+[spaCy](https://github.com/explosion/spaCy) is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It offers support for transformers models through its third party package, spacy-transformers.
+
+Keywords: NLP, Framework
+
+## [speechbrain](https://github.com/speechbrain/speechbrain)
+
+SpeechBrain is an open-source and all-in-one conversational AI toolkit based on PyTorch.
+The goal is to create a single, flexible, and user-friendly toolkit that can be used to easily develop state-of-the-art speech technologies, including systems for speech recognition, speaker recognition, speech enhancement, speech separation, language identification, multi-microphone signal processing, and many others.
+
+Keywords: Conversational, Speech
+
+## [skorch](https://github.com/skorch-dev/skorch)
+
+Skorch is a scikit-learn compatible neural network library that wraps PyTorch. It has support for models within transformers, and tokenizers from tokenizers.
+
+Keywords: Scikit-Learn, PyTorch
+
+## [bertviz](https://github.com/jessevig/bertviz)
+
+BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models.
+
+Keywords: Visualization, Transformers
+
+## [mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax)
+
+[mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) is a haiku library using the xmap/pjit operators in JAX for model parallelism of transformers. This library is designed for scalability up to approximately 40B parameters on TPUv3s. It was the library used to train the GPT-J model.
+
+Keywords: Haiku, Model parallelism, LLM, TPU
+
+## [deepchem](https://github.com/deepchem/deepchem)
+
+DeepChem aims to provide a high quality open-source toolchain that democratizes the use of deep-learning in drug discovery, materials science, quantum chemistry, and biology.
+
+Keywords: Drug discovery, Materials Science, Quantum Chemistry, Biology
+
+## [OpenNRE](https://github.com/thunlp/OpenNRE)
+
+An Open-Source Package for Neural Relation Extraction (NRE). It is targeted at a wide range of users, from newcomers to relation extraction, to developers, researchers, or students.
+
+Keywords: Neural Relation Extraction, Framework
+
+## [pycorrector](https://github.com/shibing624/pycorrector)
+
+PyCorrector is a Chinese Text Error Correction Tool. It uses a language model to detect errors, pinyin feature and shape feature to correct Chinese text errors. it can be used for Chinese Pinyin and stroke input method.
+
+Keywords: Chinese, Error correction tool, Language model, Pinyin
+
+## [nlpaug](https://github.com/makcedward/nlpaug)
+
+This python library helps you with augmenting nlp for machine learning projects. It is a lightweight library featuring synthetic data generation for improving model performance, support for audio and text, and compatibility with several ecosystems (scikit-learn, pytorch, tensorflow).
+
+Keywords: Data augmentation, Synthetic data generation, Audio, NLP
+
+## [dream-textures](https://github.com/carson-katri/dream-textures)
+
+[dream-textures](https://github.com/carson-katri/dream-textures) is a library targeted at bringing stable-diffusion support within Blender. It supports several use-cases, such as image generation, texture projection, inpainting/outpainting, ControlNet, and upscaling.
+
+Keywords: Stable-Diffusion, Blender
+
+## [seldon-core](https://github.com/SeldonIO/seldon-core)
+
+Seldon core converts your ML models (Tensorflow, Pytorch, H2o, etc.) or language wrappers (Python, Java, etc.) into production REST/GRPC microservices.
+Seldon handles scaling to thousands of production machine learning models and provides advanced machine learning capabilities out of the box including Advanced Metrics, Request Logging, Explainers, Outlier Detectors, A/B Tests, Canaries and more.
+
+Keywords: Microservices, Modeling, Language wrappers
+
+## [open_model_zoo](https://github.com/openvinotoolkit/open_model_zoo)
+
+This repository includes optimized deep learning models and a set of demos to expedite development of high-performance deep learning inference applications. Use these free pre-trained models instead of training your own models to speed-up the development and production deployment process.
+
+Keywords: Optimized models, Demos
+
+## [ml-stable-diffusion](https://github.com/apple/ml-stable-diffusion)
+
+ML-Stable-Diffusion is a repository by Apple bringing Stable Diffusion support to Core ML, on Apple Silicon devices. It supports stable diffusion checkpoints hosted on the Hugging Face Hub.
+
+Keywords: Stable Diffusion, Apple Silicon, Core ML
+
+## [stable-dreamfusion](https://github.com/ashawkey/stable-dreamfusion)
+
+Stable-Dreamfusion is a pytorch implementation of the text-to-3D model Dreamfusion, powered by the Stable Diffusion text-to-2D model.
+
+Keywords: Text-to-3D, Stable Diffusion
+
+## [txtai](https://github.com/neuml/txtai)
+ 
+[txtai](https://github.com/neuml/txtai) is an open-source platform for semantic search and workflows powered by language models. txtai builds embeddings databases, which are a union of vector indexes and relational databases enabling similarity search with SQL. Semantic workflows connect language models together into unified applications.
+
+Keywords: Semantic search, LLM
+
+## [djl](https://github.com/deepjavalibrary/djl)
+
+Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for developers. DJL provides a native Java development experience and functions like any other regular Java library. DJL offers [a Java binding](https://github.com/deepjavalibrary/djl/tree/master/extensions/tokenizers) for HuggingFace Tokenizers and easy conversion toolkit for HuggingFace model to deploy in Java.
+
+Keywords: Java, Framework
+
+## [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/)
+
+This project provides a unified framework to test generative language models on a large number of different evaluation tasks. It has support for more than 200 tasks, and supports different ecosystems: HF Transformers, GPT-NeoX, DeepSpeed, as well as the OpenAI API.
+
+Keywords: LLM, Evaluation, Few-shot
+
+## [gpt-neox](https://github.com/EleutherAI/gpt-neox)
+
+This repository records EleutherAI's library for training large-scale language models on GPUs. The framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. It is focused on training multi-billion-parameter models.
+
+Keywords: Training, LLM, Megatron, DeepSpeed
+
+## [muzic](https://github.com/microsoft/muzic)
+
+Muzic is a research project on AI music that empowers music understanding and generation with deep learning and artificial intelligence. Muzic was created by researchers from Microsoft Research Asia.
+
+Keywords: Music understanding, Music generation
+
+## [dalle-flow](https://github.com/jina-ai/dalle-flow)
+
+DALL·E Flow is an interactive workflow for generating high-definition images from a text prompt. Itt leverages DALL·E-Mega, GLID-3 XL, and Stable Diffusion to generate image candidates, and then calls CLIP-as-service to rank the candidates w.r.t. the prompt.
+The preferred candidate is fed to GLID-3 XL for diffusion, which often enriches the texture and background. Finally, the candidate is upscaled to 1024x1024 via SwinIR.
+
+Keywords: High-definition image generation, Stable Diffusion, DALL-E Mega, GLID-3 XL, CLIP, SwinIR
+
+## [lightseq](https://github.com/bytedance/lightseq)
+
+LightSeq is a high performance training and inference library for sequence processing and generation implemented in CUDA. It enables highly efficient computation of modern NLP and CV models such as BERT, GPT, Transformer, etc. It is therefore best useful for machine translation, text generation, image classification, and other sequence related tasks.
+
+Keywords: Training, Inference, Sequence Processing, Sequence Generation
+
+## [LaTeX-OCR](https://github.com/lukas-blecher/LaTeX-OCR)
+
+The goal of this project is to create a learning based system that takes an image of a math formula and returns corresponding LaTeX code.
+
+Keywords: OCR, LaTeX, Math formula
+
+## [open_clip](https://github.com/mlfoundations/open_clip)
+
+OpenCLIP is an open source implementation of OpenAI's CLIP.
+
+The goal of this repository is to enable training models with contrastive image-text supervision, and to investigate their properties such as robustness to distribution shift. 
+The starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset. 
+
+Specifically, a ResNet-50 model trained with this codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet.
+
+Keywords: CLIP, Open-source, Contrastive, Image-text
+
+## [dalle-playground](https://github.com/saharmor/dalle-playground)
+
+A playground to generate images from any text prompt using Stable Diffusion and Dall-E mini.
+
+Keywords: WebUI, Stable Diffusion, Dall-E mini
+
+## [FedML](https://github.com/FedML-AI/FedML)
+
+[FedML](https://github.com/FedML-AI/FedML) is a federated learning and analytics library enabling secure and collaborative machine learning on decentralized data anywhere at any scale.
+
+It supports large-scale cross-silo federated learning, and cross-device federated learning on smartphones/IoTs, and research simulation.
+
+Keywords: Federated Learning, Analytics, Collaborative ML, Decentralized
+
+## [gpt-code-clippy](https://github.com/CodedotAl/gpt-code-clippy)
+
+GPT-Code-Clippy (GPT-CC) is an open source version of GitHub Copilot, a language model -- based on GPT-3, called GPT-Codex -- that is fine-tuned on publicly available code from GitHub.
+
+Keywords: LLM, Code
+
+## [TextAttack](https://github.com/QData/TextAttack)
+
+[TextAttack](https://github.com/QData/TextAttack) 🐙 is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
+
+Keywords: Adversarial attacks, Data augmentation, NLP
+
+## [OpenPrompt](https://github.com/thunlp/OpenPrompt)
+
+Prompt-learning is a paradigm to adapt pre-trained language models (PLMs) to downstream NLP tasks, which modify the input text with a textual template and directly uses PLMs to conduct pre-trained tasks. This library provides a standard, flexible and extensible framework to deploy the prompt-learning pipeline. [OpenPrompt](https://github.com/thunlp/OpenPrompt) supports loading PLMs directly from https://github.com/huggingface/transformers.
+
+## [text-generation-webui](https://github.com/oobabooga/text-generation-webui/)
+
+[text-generation-webui](https://github.com/oobabooga/text-generation-webui/) is a Gradio Web UI for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA.
+
+Keywords: LLM, WebUI
+
+## [libra](https://github.com/Palashio/libra)
+
+An ergonomic machine learning [libra](https://github.com/Palashio/libra)ry for non-technical users. It focuses on ergonomics and on ensuring that training a model is as simple as it can be.
+
+Keywords: Ergonomic, Non-technical
+
+## [alibi](https://github.com/SeldonIO/alibi)
+
+Alibi is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.
+
+Keywords: Model inspection, Model interpretation, Black-box, White-box
+
+## [tortoise-tts](https://github.com/neonbjb/tortoise-tts)
+
+Tortoise is a text-to-speech program built with the following priorities: strong multi-voice capabilities, and highly realistic prosody and intonation.
+
+Keywords: Text-to-speech
+
+## [flower](https://github.com/adap/flower)
+
+Flower (flwr) is a framework for building federated learning systems. The design of Flower is based on a few guiding principles: customizability, extendability, framework agnosticity, and ease-of-use.
+
+Keywords: Federated learning systems, Customizable, Extendable, Framework-agnostic, Simplicity
+
+## [fast-bert](https://github.com/utterworks/fast-bert)
+
+Fast-Bert is a deep learning library that allows developers and data scientists to train and deploy BERT and XLNet based models for natural language processing tasks beginning with Text Classification. It is aimed at simplicity.
+
+Keywords: Deployment, BERT, XLNet
+
+## [towhee](https://github.com/towhee-io/towhee)
+
+Towhee makes it easy to build neural data processing pipelines for AI applications. We provide hundreds of models, algorithms, and transformations that can be used as standard pipeline building blocks. Users can use Towhee's Pythonic API to build a prototype of their pipeline and automatically optimize it for production-ready environments.
+
+Keywords: Data processing pipeline, Optimization
+
+## [alibi-detect](https://github.com/SeldonIO/alibi-detect)
+
+Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection.
+
+Keywords: Adversarial, Outlier, Drift detection
+
+## [FARM](https://github.com/deepset-ai/FARM)
+
+[FARM](https://github.com/deepset-ai/FARM) makes Transfer Learning with BERT & Co simple, fast and enterprise-ready. It's built upon transformers and provides additional features to simplify the life of developers: Parallelized preprocessing, highly modular design, multi-task learning, experiment tracking, easy debugging and close integration with AWS SageMaker.
+
+Keywords: Transfer Learning, Modular design, Multi-task learning, Experiment tracking
+
+## [aitextgen](https://github.com/minimaxir/aitextgen)
+
+A robust Python tool for text-based AI training and generation using OpenAI's GPT-2 and EleutherAI's GPT Neo/GPT-3 architecture.
+[aitextgen](https://github.com/minimaxir/aitextgen) is a Python package that leverages PyTorch, Hugging Face Transformers and pytorch-lightning with specific optimizations for text generation using GPT-2, plus many added features.
+
+Keywords: Training, Generation
+
+## [diffgram](https://github.com/diffgram/diffgram)
+
+Diffgram aims to integrate human supervision into platforms. We support your team programmatically changing the UI (Schema, layout, etc.) like in Streamlit. This means that you can collect and annotate timely data from users. In other words, we are the platform behind your platform, an integrated part of your application, to ship new & better AI products faster.
+
+Keywords: Human supervision, Platform
+
+## [ecco](https://github.com/jalammar/ecco)
+
+Explain, analyze, and visualize NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2, BERT, RoBERTA, T5, and T0).
+
+Keywords: Model explainability
+
+## [s3prl](https://github.com/s3prl/s3prl)
+
+[s3prl](https://github.com/s3prl/s3prl) stands for Self-Supervised Speech Pre-training and Representation Learning. Self-supervised speech pre-trained models are called upstream in this toolkit, and are utilized in various downstream tasks.
+
+Keywords: Speech, Training
+
+## [ru-dalle](https://github.com/ai-forever/ru-dalle)
+
+RuDALL-E aims to be similar to DALL-E, targeted to Russian.
+
+Keywords: DALL-E, Russian
+
+## [DeepKE](https://github.com/zjunlp/DeepKE)
+
+[DeepKE](https://github.com/zjunlp/DeepKE) is a knowledge extraction toolkit for knowledge graph construction supporting cnSchema，low-resource, document-level and multimodal scenarios for entity, relation and attribute extraction.
+
+Keywords: Knowledge Extraction, Knowledge Graphs
+
+## [Nebuly](https://github.com/nebuly-ai/nebuly)
+
+Nebuly is the next-generation platform to monitor and optimize your AI costs in one place. The platform connects to all your AI cost sources (compute, API providers, AI software licenses, etc) and centralizes them in one place to give you full visibility on a model basis. The platform also provides optimization recommendations and a co-pilot model that can guide during the optimization process. The platform builds on top of the open-source tools allowing you to optimize the different steps of your AI stack to squeeze out the best possible cost performances.
+
+Keywords: Optimization, Performance, Monitoring
+
+## [imaginAIry](https://github.com/brycedrennan/imaginAIry)
+
+Offers a CLI and a Python API to generate images with Stable Diffusion. It has support for many tools, like image structure control (controlnet), instruction-based image edits (InstructPix2Pix), prompt-based masking (clipseg), among others.
+
+Keywords: Stable Diffusion, CLI, Python API
+
+## [sparseml](https://github.com/neuralmagic/sparseml)
+
+SparseML is an open-source model optimization toolkit that enables you to create inference-optimized sparse models using pruning, quantization, and distillation algorithms. Models optimized with SparseML can then be exported to the ONNX and deployed with DeepSparse for GPU-class performance on CPU hardware.
+
+Keywords: Model optimization, Pruning, Quantization, Distillation
+
+## [opacus](https://github.com/pytorch/opacus)
+
+Opacus is a library that enables training PyTorch models with differential privacy. It supports training with minimal code changes required on the client, has little impact on training performance, and allows the client to online track the privacy budget expended at any given moment.
+
+Keywords: Differential privacy
+
+## [LAVIS](https://github.com/salesforce/LAVIS)
+
+[LAVIS](https://github.com/salesforce/LAVIS) is a Python deep learning library for LAnguage-and-VISion intelligence research and applications. This library aims to provide engineers and researchers with a one-stop solution to rapidly develop models for their specific multimodal scenarios, and benchmark them across standard and customized datasets. It features a unified interface design to access
+
+Keywords: Multimodal, NLP, Vision
+
+## [buzz](https://github.com/chidiwilliams/buzz)
+
+Buzz transcribes and translates audio offline on your personal computer. Powered by OpenAI's Whisper.
+
+Keywords: Audio transcription, Translation
+
+## [rust-bert](https://github.com/guillaume-be/rust-bert)
+
+Rust-native state-of-the-art Natural Language Processing models and pipelines. Port of Hugging Face's Transformers library, using the tch-rs crate and pre-processing from rust-tokenizers. Supports multi-threaded tokenization and GPU inference. This repository exposes the model base architecture, task-specific heads and ready-to-use pipelines.
+
+Keywords: Rust, BERT, Inference
+
+## [EasyNLP](https://github.com/alibaba/EasyNLP)
+
+[EasyNLP](https://github.com/alibaba/EasyNLP) is an easy-to-use NLP development and application toolkit in PyTorch, first released inside Alibaba in 2021. It is built with scalable distributed training strategies and supports a comprehensive suite of NLP algorithms for various NLP applications. [EasyNLP](https://github.com/alibaba/EasyNLP) integrates knowledge distillation and few-shot learning for landing large pre-trained models, together with various popular multi-modality pre-trained models. It provides a unified framework of model training, inference, and deployment for real-world applications.
+
+Keywords: NLP, Knowledge distillation, Few-shot learning, Multi-modality, Training, Inference, Deployment
+
+## [TurboTransformers](https://github.com/Tencent/TurboTransformers)
+
+A fast and user-friendly runtime for transformer inference (Bert, Albert, GPT2, Decoders, etc) on CPU and GPU.
+
+Keywords: Optimization, Performance
+
+## [hivemind](https://github.com/learning-at-home/hivemind)
+
+Hivemind is a PyTorch library for decentralized deep learning across the Internet. Its intended usage is training one large model on hundreds of computers from different universities, companies, and volunteers.
+
+Keywords: Decentralized training
+
+## [docquery](https://github.com/impira/docquery)
+
+DocQuery is a library and command-line tool that makes it easy to analyze semi-structured and unstructured documents (PDFs, scanned images, etc.) using large language models (LLMs). You simply point DocQuery at one or more documents and specify a question you want to ask. DocQuery is created by the team at Impira.
+
+Keywords: Semi-structured documents, Unstructured documents, LLM, Document Question Answering
+
+## [CodeGeeX](https://github.com/THUDM/CodeGeeX)
+
+[CodeGeeX](https://github.com/THUDM/CodeGeeX) is a large-scale multilingual code generation model with 13 billion parameters, pre-trained on a large code corpus of more than 20 programming languages. It has several unique features:
+- Multilingual code generation
+- Crosslingual code translation
+- Is a customizable programming assistant
+
+Keywords: Code Generation Model
+
+## [ktrain](https://github.com/amaiya/ktrain)
+
+[ktrain](https://github.com/amaiya/ktrain) is a lightweight wrapper for the deep learning library TensorFlow Keras (and other libraries) to help build, train, and deploy neural networks and other machine learning models. Inspired by ML framework extensions like fastai and ludwig, [ktrain](https://github.com/amaiya/ktrain) is designed to make deep learning and AI more accessible and easier to apply for both newcomers and experienced practitioners.
+
+Keywords: Keras wrapper, Model building, Training, Deployment
+
+## [FastDeploy](https://github.com/PaddlePaddle/FastDeploy)
+
+[FastDeploy](https://github.com/PaddlePaddle/FastDeploy) is an Easy-to-use and High Performance AI model deployment toolkit for Cloud, Mobile and Edge with packageout-of-the-box and unified experience, endend-to-end optimization for over fire160+ Text, Vision, Speech and Cross-modal AI models. Including image classification, object detection, OCR, face detection, matting, pp-tracking, NLP, stable diffusion, TTS and other tasks to meet developers' industrial deployment needs for multi-scenario, multi-hardware and multi-platform.
+
+Keywords: Model deployment, CLoud, Mobile, Edge
+
+## [underthesea](https://github.com/undertheseanlp/underthesea)
+
+[underthesea](https://github.com/undertheseanlp/underthesea) is a Vietnamese NLP toolkit. Underthesea is a suite of open source Python modules data sets and tutorials supporting research and development in Vietnamese Natural Language Processing. We provides extremely easy API to quickly apply pretrained NLP models to your Vietnamese text, such as word segmentation, part-of-speech tagging (PoS), named entity recognition (NER), text classification and dependency parsing.
+
+Keywords: Vietnamese, NLP
+
+## [hasktorch](https://github.com/hasktorch/hasktorch)
+
+Hasktorch is a library for tensors and neural networks in Haskell. It is an independent open source community project which leverages the core C++ libraries shared by PyTorch.
+
+Keywords: Haskell, Neural Networks
+
+## [donut](https://github.com/clovaai/donut)
+
+Donut, or Document understanding transformer, is a new method of document understanding that utilizes an OCR-free end-to-end Transformer model.
+
+Donut does not require off-the-shelf OCR engines/APIs, yet it shows state-of-the-art performances on various visual document understanding tasks, such as visual document classification or information extraction (a.k.a. document parsing).
+
+Keywords: Document Understanding
+
+## [transformers-interpret](https://github.com/cdpierse/transformers-interpret)
+
+Transformers Interpret is a model explainability tool designed to work exclusively with the transformers package.
+
+In line with the philosophy of the Transformers package Transformers Interpret allows any transformers model to be explained in just two lines. Explainers are available for both text and computer vision models. Visualizations are also available in notebooks and as savable png and html files
+
+Keywords: Model interpretation, Visualization
+
+## [mlrun](https://github.com/mlrun/mlrun)
+
+MLRun is an open MLOps platform for quickly building and managing continuous ML applications across their lifecycle. MLRun integrates into your development and CI/CD environment and automates the delivery of production data, ML pipelines, and online applications, significantly reducing engineering efforts, time to production, and computation resources. With MLRun, you can choose any IDE on your local machine or on the cloud. MLRun breaks the silos between data, ML, software, and DevOps/MLOps teams, enabling collaboration and fast continuous improvements.
+
+Keywords: MLOps
+
+## [FederatedScope](https://github.com/alibaba/FederatedScope)
+
+[FederatedScope](https://github.com/alibaba/FederatedScope) is a comprehensive federated learning platform that provides convenient usage and flexible customization for various federated learning tasks in both academia and industry. Based on an event-driven architecture, [FederatedScope](https://github.com/alibaba/FederatedScope) integrates rich collections of functionalities to satisfy the burgeoning demands from federated learning, and aims to build up an easy-to-use platform for promoting learning safely and effectively.
+
+Keywords: Federated learning, Event-driven
+
+## [pythainlp](https://github.com/PyThaiNLP/pythainlp)
+
+PyThaiNLP is a Python package for text processing and linguistic analysis, similar to NLTK with focus on Thai language.
+
+Keywords: Thai, NLP, NLTK
+
+## [FlagAI](https://github.com/FlagAI-Open/FlagAI)
+
+[FlagAI](https://github.com/FlagAI-Open/FlagAI) (Fast LArge-scale General AI models) is a fast, easy-to-use and extensible toolkit for large-scale model. Our goal is to support training, fine-tuning, and deployment of large-scale models on various downstream tasks with multi-modality.
+
+Keywords: Large models, Training, Fine-tuning, Deployment, Multi-modal
+
+## [pyserini](https://github.com/castorini/pyserini)
+
+[pyserini](https://github.com/castorini/pyserini) is a Python toolkit for reproducible information retrieval research with sparse and dense representations. Retrieval using sparse representations is provided via integration with the group's Anserini IR toolkit. Retrieval using dense representations is provided via integration with Facebook's Faiss library.
+
+Keywords: IR, Information Retrieval, Dense, Sparse
+
+## [baal](https://github.com/baal-org/baal)
+
+[baal](https://github.com/baal-org/baal) is an active learning library that supports both industrial applications and research usecases. [baal](https://github.com/baal-org/baal) currently supports Monte-Carlo Dropout, MCDropConnect, deep ensembles, and semi-supervised learning.
+
+Keywords: Active Learning, Research, Labeling
+
+## [cleanlab](https://github.com/cleanlab/cleanlab)
+
+[cleanlab](https://github.com/cleanlab/cleanlab) is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels. For text, image, tabular, audio (among others) datasets, you can use cleanlab to automatically: detect data issues (outliers, label errors, near duplicates, etc), train robust ML models, infer consensus + annotator-quality for multi-annotator data, suggest data to (re)label next (active learning).
+
+Keywords: Data-Centric AI, Data Quality, Noisy Labels, Outlier Detection, Active Learning  
+
+## [BentoML](https://github.com/bentoml/BentoML)
+
+[BentoML](https://github.com/bentoml) is the unified framework for for building, shipping, and scaling production-ready AI applications incorporating traditional ML, pre-trained AI models, Generative and Large Language Models. 
+All Hugging Face models and pipelines can be seamlessly integrated into BentoML applications, enabling the running of models on the most suitable hardware and independent scaling based on usage.
+
+Keywords: BentoML, Framework, Deployment, AI Applications
+

docker/transformers-all-latest-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -13,7 +13,7 @@ ARG PYTORCH='2.0.1'
 # (not always a valid torch version)
 ARG INTEL_TORCH_EXT='1.11.0'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu117'
+ARG CUDA='cu118'
 
 RUN apt update
 RUN apt install -y git libsndfile1-dev tesseract-ocr espeak-ng python3 python3-pip ffmpeg git-lfs
@@ -35,7 +35,7 @@ RUN [ "$PYTORCH" != "pre" ] && python3 -m pip install --no-cache-dir -U $VERSION
 RUN python3 -m pip install --no-cache-dir -U tensorflow==2.12 protobuf==3.20.3 tensorflow_text tensorflow_probability
 RUN python3 -m pip uninstall -y flax jax
 
-RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT+cpu -f https://software.intel.com/ipex-whl-stable
+RUN python3 -m pip install --no-cache-dir intel_extension_for_pytorch==$INTEL_TORCH_EXT+cpu -f https://developer.intel.com/ipex-whl-stable-cpu
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/facebookresearch/detectron2.git pytesseract
 RUN python3 -m pip install -U "itsdangerous<2.1.0"

docker/transformers-past-gpu/Dockerfile

@@ -24,7 +24,7 @@ ARG FRAMEWORK
 ARG VERSION
 
 # Control `setuptools` version to avoid some issues
-RUN [ "$VERSION" != "1.9" -a "$VERSION" != "1.10" ] && python3 -m pip install -U setuptools || python3 -m pip install -U "setuptools<=59.5"
+RUN [ "$VERSION" != "1.10" ] && python3 -m pip install -U setuptools || python3 -m pip install -U "setuptools<=59.5"
 
 # Remove all frameworks
 RUN python3 -m pip uninstall -y torch torchvision torchaudio tensorflow jax flax

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

@@ -1,12 +1,12 @@
-# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel_22-08.html#rel_22-08
-FROM nvcr.io/nvidia/pytorch:22.08-py3
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-22-12.html#rel-22-12
+FROM nvcr.io/nvidia/pytorch:22.12-py3
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
 ARG PYTORCH='2.0.1'
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu117'
+ARG CUDA='cu118'
 
 RUN apt -y update
 RUN apt install -y libaio-dev
@@ -15,6 +15,8 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip
 ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
+RUN python3 -m pip uninstall -y torch torchvision torchaudio
+
 # Install latest release PyTorch
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
@@ -24,6 +26,9 @@ RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing]
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
+# Uninstall `transformer-engine` shipped with the base image
+RUN python3 -m pip uninstall -y transformer-engine
+
 # Uninstall `torch-tensorrt` shipped with the base image
 RUN python3 -m pip uninstall -y torch-tensorrt
 
@@ -31,7 +36,7 @@ RUN python3 -m pip uninstall -y torch-tensorrt
 RUN python3 -m pip uninstall -y apex
 RUN git clone https://github.com/NVIDIA/apex
 #  `MAX_JOBS=1` disables parallel building to avoid cpu memory OOM when building image on GitHub Action (standard) runners
-RUN cd apex && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check .
+RUN cd apex && git checkout 82ee367f3da74b4cd62a1fb47aa9806f0f47b58b && MAX_JOBS=1 python3 -m pip install --global-option="--cpp_ext" --global-option="--cuda_ext" --no-cache -v --disable-pip-version-check .
 
 # Pre-build **latest** DeepSpeed, so it would be ready for testing (otherwise, the 1st deepspeed test will timeout)
 RUN python3 -m pip uninstall -y deepspeed

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

@@ -1,11 +1,11 @@
-# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel_22-08.html#rel_22-08
-FROM nvcr.io/nvidia/pytorch:22.08-py3
+# https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-22-12.html#rel-22-12
+FROM nvcr.io/nvidia/pytorch:22.12-py3
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
 
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu117'
+ARG CUDA='cu118'
 
 RUN apt -y update
 RUN apt install -y libaio-dev
@@ -14,6 +14,8 @@ RUN python3 -m pip install --no-cache-dir --upgrade pip
 ARG REF=main
 RUN git clone https://github.com/huggingface/transformers && cd transformers && git checkout $REF
 
+RUN python3 -m pip uninstall -y torch torchvision torchaudio
+
 # Install **nightly** release PyTorch (flag `--pre`)
 # (PyTorch must be installed before pre-compiling any DeepSpeed c++/cuda ops.)
 # (https://www.deepspeed.ai/tutorials/advanced-install/#pre-install-deepspeed-ops)
@@ -23,6 +25,9 @@ RUN python3 -m pip install --no-cache-dir ./transformers[deepspeed-testing]
 
 RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate@main#egg=accelerate
 
+# Uninstall `transformer-engine` shipped with the base image
+RUN python3 -m pip uninstall -y transformer-engine
+
 # Uninstall `torch-tensorrt` and `apex` shipped with the base image
 RUN python3 -m pip uninstall -y torch-tensorrt apex
 

docker/transformers-pytorch-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.7.1-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive
@@ -16,7 +16,7 @@ ARG PYTORCH='2.0.1'
 ARG TORCH_VISION=''
 ARG TORCH_AUDIO=''
 # Example: `cu102`, `cu113`, etc.
-ARG CUDA='cu117'
+ARG CUDA='cu118'
 
 RUN [ ${#PYTORCH} -gt 0 ] && VERSION='torch=='$PYTORCH'.*' ||  VERSION='torch'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA
 RUN [ ${#TORCH_VISION} -gt 0 ] && VERSION='torchvision=='TORCH_VISION'.*' ||  VERSION='torchvision'; python3 -m pip install --no-cache-dir -U $VERSION --extra-index-url https://download.pytorch.org/whl/$CUDA

docker/transformers-tensorflow-gpu/Dockerfile

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04
+FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu20.04
 LABEL maintainer="Hugging Face"
 
 ARG DEBIAN_FRONTEND=noninteractive

docs/README.md

@@ -369,20 +369,7 @@ contains the example docstring to the [documentation_tests.txt](../utils/documen
 
 ### For Python files
 
-You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files):
-
-```bash
-python utils/prepare_for_doc_test.py src docs
-```
-
-If you work on a specific python module, say `modeling_wav2vec2.py`, you can run the command as follows (to avoid the unnecessary temporary changes in irrelevant files):
-
-```bash
-python utils/prepare_for_doc_test.py src/transformers/utils/doc.py src/transformers/models/wav2vec2/modeling_wav2vec2.py
-```
-(`utils/doc.py` should always be included)
-
-Then you can run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance:
+Run all the tests in the docstrings of a given file with the following command, here is how we test the modeling file of Wav2Vec2 for instance:
 
 ```bash
 pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py -sv --doctest-continue-on-failure
@@ -394,32 +381,14 @@ If you want to isolate a specific docstring, just add `::` after the file name t
 pytest --doctest-modules src/transformers/models/wav2vec2/modeling_wav2vec2.py::transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForCTC.forward -sv --doctest-continue-on-failure
 ```
 
-Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing:
-
-```bash
-python utils/prepare_for_doc_test.py src docs --remove_new_line
-```
-
 ### For Markdown files
 
-You will first need to run the following command (from the root of the repository) to prepare the doc file (doc-testing needs to add additional lines that we don't include in the doc source files):
-
-```bash
-python utils/prepare_for_doc_test.py src docs
-```
-
-Then you can test locally a given file with this command (here testing the quicktour):
+You can test locally a given file with this command (here testing the quicktour):
 
 ```bash
 pytest --doctest-modules docs/source/quicktour.mdx -sv --doctest-continue-on-failure --doctest-glob="*.mdx"
 ```
 
-Once you're done, you can run the following command (still from the root of the repository) to undo the changes made by the first command before committing:
-
-```bash
-python utils/prepare_for_doc_test.py src docs --remove_new_line
-```
-
 ### Writing doctests
 
 Here are a few tips to help you debug the doctests and make them pass:

docs/source/en/_toctree.yml

@@ -93,6 +93,8 @@
       title: Run training on Amazon SageMaker
     - local: serialization
       title: Export to ONNX
+    - local: tflite
+      title: Export to TFLite
     - local: torchscript
       title: Export to TorchScript
     - local: benchmarks
@@ -492,6 +494,8 @@
         title: MobileNetV2
       - local: model_doc/mobilevit
         title: MobileViT
+      - local: model_doc/mobilevitv2
+        title: MobileViTV2
       - local: model_doc/nat
         title: NAT
       - local: model_doc/poolformer
@@ -537,10 +541,14 @@
         title: Audio Spectrogram Transformer
       - local: model_doc/clap
         title: CLAP
+      - local: model_doc/encodec
+        title: EnCodec
       - local: model_doc/hubert
         title: Hubert
       - local: model_doc/mctct
         title: MCTCT
+      - local: model_doc/mms
+        title: MMS
       - local: model_doc/sew
         title: SEW
       - local: model_doc/sew-d
@@ -654,6 +662,8 @@
       title: Reinforcement learning models
     - isExpanded: false
       sections:
+      - local: model_doc/autoformer
+        title: Autoformer
       - local: model_doc/informer
         title: Informer
       - local: model_doc/time_series_transformer

docs/source/en/custom_tools.mdx

@@ -414,6 +414,13 @@ of the tools, it has available to it.
 
 </Tip>
 
+In both cases, you can pass a repo ID instead of the prompt template if you would like to use a template hosted by someone in the community. The default prompts live in [this repo](https://huggingface.co/datasets/huggingface-tools/default-prompts) as an example.
+
+To upload your custom prompt on a repo on the Hub and share it with the community just make sure:
+- to use a dataset repository
+- to put the prompt template for the `run` command in a file named `run_prompt_template.txt`
+- to put the prompt template for the `chat` command in a file named `chat_prompt_template.txt`
+
 ## Using custom tools
 
 In this section, we'll be leveraging two existing custom tools that are specific to image generation:

docs/source/en/generation_strategies.mdx

@@ -338,9 +338,8 @@ For the complete list of the available parameters, refer to the [API documentati
 Assisted decoding is a modification of the decoding strategies above that uses an assistant model with the same
 tokenizer (ideally a much smaller model) to greedily generate a few candidate tokens. The main model then validates
 the candidate tokens in a single forward pass, which speeds up the decoding process. Currently, only greedy search
-and sampling are supported with assisted decoding, and doesn't support batched inputs.
-
-<!-- TODO: add link to the blog post about assisted decoding when it exists -->
+and sampling are supported with assisted decoding, and doesn't support batched inputs. To learn more about assisted
+decoding, check [this blog post](https://huggingface.co/blog/assisted-generation).
 
 To enable assisted decoding, set the `assistant_model` argument with a model.
 
@@ -364,8 +363,6 @@ To enable assisted decoding, set the `assistant_model` argument with a model.
 When using assisted decoding with sampling methods, you can use the `temperarure` argument to control the randomness
 just like in multinomial sampling. However, in assisted decoding, reducing the temperature will help improving latency.
 
-<!-- TODO: link the blog post again to explain why the tradeoff exists -->
-
 ```python
 >>> from transformers import AutoModelForCausalLM, AutoTokenizer
 

docs/source/en/index.mdx

@@ -53,6 +53,7 @@ The documentation is organized into five sections:
 1. **[ALIGN](model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
 1. **[AltCLIP](model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
 1. **[Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
 1. **[BART](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](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](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.
@@ -106,6 +107,7 @@ The documentation is organized into five sections:
 1. **[EfficientFormer](model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
 1. **[EfficientNet](model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
 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. **[EnCodec](model_doc/encodec)** (from Meta AI) released with the paper [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
 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. **[ErnieM](model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
@@ -156,15 +158,17 @@ The documentation is organized into five sections:
 1. **[MatCha](model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
 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.
-1. **[MEGA](model_doc/mega)** (from Facebook) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
+1. **[MEGA](model_doc/mega)** (from Meta/USC/CMU/SJTU) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
 1. **[Megatron-BERT](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](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. **[MGP-STR](model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
 1. **[mLUKE](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. **[MMS](model_doc/mms)** (from Facebook) released with the paper [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli.
 1. **[MobileBERT](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. **[MobileNetV1](model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
 1. **[MobileNetV2](model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
 1. **[MobileViT](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. **[MobileViTV2](model_doc/mobilevitv2)** (from Apple) released with the paper [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
 1. **[MPNet](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](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](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.
@@ -268,6 +272,7 @@ Flax), PyTorch, and/or TensorFlow.
 |             ALIGN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            AltCLIP            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 | Audio Spectrogram Transformer |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          Autoformer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             BART              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             BEiT              |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
 |             BERT              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
@@ -311,9 +316,10 @@ Flax), PyTorch, and/or TensorFlow.
 |           DonutSwin           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |              DPR              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |              DPT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|        EfficientFormer        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        EfficientFormer        |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |         EfficientNet          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            ELECTRA            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|            EnCodec            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        Encoder decoder        |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
 |             ERNIE             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            ErnieM             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -366,6 +372,7 @@ Flax), PyTorch, and/or TensorFlow.
 |          MobileNetV1          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          MobileNetV2          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           MobileViT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|          MobileViTV2          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             MPNet             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |              MT5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |              MVP              |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
@@ -399,7 +406,7 @@ Flax), PyTorch, and/or TensorFlow.
 |            RoCBert            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |           RoFormer            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |             RWKV              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
-|              SAM              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              SAM              |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           SegFormer           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |              SEW              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |             SEW-D             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
@@ -419,6 +426,7 @@ Flax), PyTorch, and/or TensorFlow.
 |             TAPAS             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |    Time Series Transformer    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          TimeSformer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         TimmBackbone          |       ❌       |       ❌       |       ❌        |         ❌         |      ❌      |
 |    Trajectory Transformer     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |        Transformer-XL         |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |             TrOCR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |

docs/source/en/internal/modeling_utils.mdx

@@ -54,9 +54,6 @@ Most of those are only useful if you are studying the code of the models in the
 
 [[autodoc]] modeling_tf_utils.TFConv1D
 
-[[autodoc]] modeling_tf_utils.TFSharedEmbeddings
-    - call
-
 [[autodoc]] modeling_tf_utils.TFSequenceSummary
 
 ## TensorFlow loss functions

docs/source/en/main_classes/agent.mdx

@@ -24,16 +24,24 @@ contains the API docs for the underlying classes.
 
 ## Agents
 
-We provide two types of agents: [`HfAgent`] uses inference endpoints for opensource models and [`OpenAiAgent`] uses OpenAI closed models.
+We provide three types of agents: [`HfAgent`] uses inference endpoints for opensource models, [`LocalAgent`] uses a model of your choice locally and [`OpenAiAgent`] uses OpenAI closed models.
 
 ### HfAgent
 
 [[autodoc]] HfAgent
 
+### LocalAgent
+
+[[autodoc]] LocalAgent
+
 ### OpenAiAgent
 
 [[autodoc]] OpenAiAgent
 
+### AzureOpenAiAgent
+
+[[autodoc]] AzureOpenAiAgent
+
 ### Agent
 
 [[autodoc]] Agent
@@ -62,3 +70,32 @@ We provide two types of agents: [`HfAgent`] uses inference endpoints for opensou
 ### launch_gradio_demo
 
 [[autodoc]] launch_gradio_demo
+
+## Agent Types
+
+Agents can handle any type of object in-between tools; tools, being completely multimodal, can accept and return
+text, image, audio, video, among other types. In order to increase compatibility between tools, as well as to 
+correctly render these returns in ipython (jupyter, colab, ipython notebooks, ...), we implement wrapper classes
+around these types.
+
+The wrapped objects should continue behaving as initially; a text object should still behave as a string, an image
+object should still behave as a `PIL.Image`.
+
+These types have three specific purposes:
+
+- Calling `to_raw` on the type should return the underlying object
+- Calling `to_string` on the type should return the object as a string: that can be the string in case of an `AgentText`
+  but will be the path of the serialized version of the object in other instances
+- Displaying it in an ipython kernel should display the object correctly
+
+### AgentText
+
+[[autodoc]] transformers.tools.agent_types.AgentText
+
+### AgentImage
+
+[[autodoc]] transformers.tools.agent_types.AgentImage
+
+### AgentAudio
+
+[[autodoc]] transformers.tools.agent_types.AgentAudio

docs/source/en/main_classes/callback.mdx

@@ -39,6 +39,7 @@ By default a [`Trainer`] will use the following callbacks:
   installed.
 - [`~integrations.ClearMLCallback`] if [clearml](https://github.com/allegroai/clearml) is installed.
 - [`~integrations.DagsHubCallback`] if [dagshub](https://dagshub.com/) is installed.
+- [`~integrations.FlyteCallback`] if [flyte](https://flyte.org/) is installed.
 
 The main class that implements callbacks is [`TrainerCallback`]. It gets the
 [`TrainingArguments`] used to instantiate the [`Trainer`], can access that
@@ -79,6 +80,8 @@ Here is the list of the available [`TrainerCallback`] in the library:
 
 [[autodoc]] integrations.DagsHubCallback
 
+[[autodoc]] integrations.FlyteCallback
+
 ## TrainerCallback
 
 [[autodoc]] TrainerCallback

docs/source/en/main_classes/deepspeed.mdx

@@ -760,7 +760,7 @@ time. "reuse distance" is a metric we are using to figure out when will a parame
 use the `stage3_max_reuse_distance` to decide whether to throw away the parameter or to keep it. If a parameter is
 going to be used again in near future (less than `stage3_max_reuse_distance`) then we keep it to reduce communication
 overhead. This is super helpful when you have activation checkpointing enabled, where we do a forward recompute and
-backward passes a a single layer granularity and want to keep the parameter in the forward recompute till the backward
+backward passes a single layer granularity and want to keep the parameter in the forward recompute till the backward
 
 The following configuration values depend on the model's hidden size:
 

docs/source/en/main_classes/quantization.mdx

@@ -19,8 +19,45 @@ This is supported by most of the GPU hardwares since the `0.37.0` release of `bi
 
 Learn more about the quantization method in the [LLM.int8()](https://arxiv.org/abs/2208.07339) paper, or the [blogpost](https://huggingface.co/blog/hf-bitsandbytes-integration) about the collaboration.
 
+Since its `0.39.0` release, you can load any model that supports `device_map` using 4-bit quantization, leveraging FP4 data type.
+
 Here are the things you can do using `bitsandbytes` integration
 
+### FP4 quantization 
+
+#### Requirements
+
+Make sure that you have installed the requirements below before running any of the code snippets below.
+
+- Latest `bitsandbytes` library
+`pip install bitsandbytes>=0.39.0`
+
+- Install latest `accelerate` from source
+`pip install git+https://github.com/huggingface/accelerate.git`
+
+- Install latest `transformers` from source 
+`pip install git+https://github.com/huggingface/transformers.git`
+
+#### Load a large model in 4bit
+
+By using `load_in_4bit=True` when calling the `.from_pretrained` method, you can divide your memory use by 4 (roughly).
+
+```python
+# pip install transformers accelerate bitsandbytes
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "bigscience/bloom-1b7"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True)
+```
+
+<Tip warning={true}>
+
+Note that once a model has been loaded in 4-bit it is currently not possible to push the quantized weights on the Hub. Note also that you cannot train 4-bit weights as this is not supported yet. However you can use 4-bit models to train extra parameters, this will be covered in the next section.
+
+</Tip>
+
 ### Load a large model in 8bit
 
 You can load a model by roughly halving the memory requirements by using `load_in_8bit=True` argument when calling `.from_pretrained` method
@@ -48,10 +85,57 @@ With this integration we were able to load large models on smaller devices and r
 
 <Tip warning={true}>
 
-Note that once a model has been loaded in 8-bit it is currently not possible to push the quantized weights on the Hub. Note also that you cannot train 8-bit weights as this is not supported yet. However you can use 8-bit models to train extra parameters, this will be covered in the next section.
+Note that once a model has been loaded in 8-bit it is currently not possible to push the quantized weights on the Hub except if you use the latest `transformers` and `bitsandbytes`. Note also that you cannot train 8-bit weights as this is not supported yet. However you can use 8-bit models to train extra parameters, this will be covered in the next section.
+Note also that `device_map` is optional but setting `device_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.
 
 </Tip>
 
+#### Advanced usecases
+
+Here we will cover some advanced usecases you can perform with FP4 quantization 
+
+##### Change the compute dtype
+
+The compute dtype is used to change the dtype that will be used during computation. For example, hidden states could be in `float32` but computation can be set to bf16 for speedups. By default, the compute dtype is set to `float32`.
+
+```python
+import torch
+from transformers import BitsAndBytesConfig
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16)
+```
+
+##### Using NF4 (Normal Float 4) data type 
+
+You can also use the NF4 data type, which is a new 4bit datatype adapted for weights that have been initialized using a normal distribution. For that run:
+
+```python
+from transformers import BitsAndBytesConfig
+
+nf4_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+)
+
+model_nf4 = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config)
+```
+
+##### Use nested quantization for more memory efficient inference
+
+We also advise users to use the nested quantization technique. This saves more memory at no additional performance - from our empirical observations, this enables fine-tuning llama-13b model on an NVIDIA-T4 16GB with a sequence length of 1024, batch size of 1 and gradient accumulation steps of 4.
+
+```python
+from transformers import BitsAndBytesConfig
+
+double_quant_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+)
+
+model_double_quant = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=double_quant_config)
+```
+
+
 ### Push quantized models on the 🤗 Hub
 
 You can push a quantized model on the Hub by naively using `push_to_hub` method. This will first push the quantization configuration file, then push the quantized model weights.
@@ -79,9 +163,10 @@ You can load a quantized model from the Hub by using `from_pretrained` method. M
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("{your_username}/bloom-560m-8bit")
+model = AutoModelForCausalLM.from_pretrained("{your_username}/bloom-560m-8bit", device_map="auto")
 ```
-Note that in this case, you don't need to specify the arguments `load_in_8bit=True` and `device_map="auto"`, but you need to make sure that `bitsandbytes` and `accelerate` are installed.
+Note that in this case, you don't need to specify the arguments `load_in_8bit=True`, but you need to make sure that `bitsandbytes` and `accelerate` are installed.
+Note also that `device_map` is optional but setting `device_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.
 
 ### Advanced usecases
 
@@ -170,6 +255,8 @@ tokenizer = AutoTokenizer.from_pretrained(model_id)
 With the official support of adapters in the Hugging Face ecosystem, you can fine-tune models that have been loaded in 8-bit. 
 This enables fine-tuning large models such as `flan-t5-large` or `facebook/opt-6.7b` in a single google Colab. Please have a look at [`peft`](https://github.com/huggingface/peft) library for more details.
 
+Note that you don't need to pass `device_map` when loading the model for training. It will automatically load your model on your GPU. You can also set the device map to a specific device if needed (e.g. `cuda:0`, `0`, `torch.device('cuda:0')`). Please note that `device_map=auto` should be used for inference only. 
+
 ### BitsAndBytesConfig
 
 [[autodoc]] BitsAndBytesConfig

docs/source/en/main_classes/trainer.mdx

@@ -61,7 +61,7 @@ class CustomTrainer(Trainer):
         outputs = model(**inputs)
         logits = outputs.get("logits")
         # compute custom loss (suppose one has 3 labels with different weights)
-        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0]))
+        loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
         loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
         return (loss, outputs) if return_outputs else loss
 ```
@@ -656,7 +656,8 @@ Therefore, improving end-to-end performance.
 please follow this nice medium article [GPU-Acceleration Comes to PyTorch on M1 Macs](https://medium.com/towards-data-science/gpu-acceleration-comes-to-pytorch-on-m1-macs-195c399efcc1).
 
 **Usage**:
-User has to just pass `--use_mps_device` argument. 
+`mps` device will be used by default if available similar to the way `cuda` device is used.
+Therefore, no action from user is required. 
 For example, you can run the official Glue text classififcation task (from the root folder) using Apple Silicon GPU with below command:
 
 ```bash
@@ -672,7 +673,6 @@ python examples/pytorch/text-classification/run_glue.py \
   --learning_rate 2e-5 \
   --num_train_epochs 3 \
   --output_dir /tmp/$TASK_NAME/ \
-  --use_mps_device \
   --overwrite_output_dir
 ```
 
@@ -688,6 +688,156 @@ Finally, please, remember that, 🤗 `Trainer` only integrates MPS backend, ther
 have any problems or questions with regards to MPS backend usage, please, 
 file an issue with [PyTorch GitHub](https://github.com/pytorch/pytorch/issues).
 
+
+## Using Accelerate Launcher with Trainer
+
+Accelerate now powers Trainer. In terms of what users should expect:
+- They can keep using the Trainer ingterations such as FSDP, DeepSpeed vis trainer arguments without any changes on their part.
+- They can now use Accelerate Launcher with Trainer (recommended).
+
+Steps to use Accelerate Launcher with Trainer:
+1. Make sure 🤗 Accelerate is installed, you can't use the `Trainer` without it anyway. If not `pip install accelerate`. You may also need to update your version of Accelerate: `pip install accelerate --upgrade`
+2. Run `accelerate config` and fill the questionnaire. Below are example accelerate configs:
+  a. DDP Multi-node Multi-GPU config:
+    ```yaml
+    compute_environment: LOCAL_MACHINE                                                                                             
+    distributed_type: MULTI_GPU                                                                                                    
+    downcast_bf16: 'no'
+    gpu_ids: all
+    machine_rank: 0 #change rank as per the node
+    main_process_ip: 192.168.20.1
+    main_process_port: 9898
+    main_training_function: main
+    mixed_precision: fp16
+    num_machines: 2
+    num_processes: 8
+    rdzv_backend: static
+    same_network: true
+    tpu_env: []
+    tpu_use_cluster: false
+    tpu_use_sudo: false
+    use_cpu: false
+    ```
+
+  b. FSDP config:
+    ```yaml
+    compute_environment: LOCAL_MACHINE
+    distributed_type: FSDP
+    downcast_bf16: 'no'
+    fsdp_config:
+      fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+      fsdp_backward_prefetch_policy: BACKWARD_PRE
+      fsdp_forward_prefetch: true
+      fsdp_offload_params: false
+      fsdp_sharding_strategy: 1
+      fsdp_state_dict_type: FULL_STATE_DICT
+      fsdp_sync_module_states: true
+      fsdp_transformer_layer_cls_to_wrap: BertLayer
+      fsdp_use_orig_params: true
+    machine_rank: 0
+    main_training_function: main
+    mixed_precision: bf16
+    num_machines: 1
+    num_processes: 2
+    rdzv_backend: static
+    same_network: true
+    tpu_env: []
+    tpu_use_cluster: false
+    tpu_use_sudo: false
+    use_cpu: false
+    ```
+  c. DeepSpeed config pointing to a file:
+    ```yaml
+    compute_environment: LOCAL_MACHINE
+    deepspeed_config:
+      deepspeed_config_file: /home/user/configs/ds_zero3_config.json
+      zero3_init_flag: true
+    distributed_type: DEEPSPEED
+    downcast_bf16: 'no'
+    machine_rank: 0
+    main_training_function: main
+    num_machines: 1
+    num_processes: 4
+    rdzv_backend: static
+    same_network: true
+    tpu_env: []
+    tpu_use_cluster: false
+    tpu_use_sudo: false
+    use_cpu: false
+    ```
+
+  d. DeepSpeed config using accelerate plugin:
+    ```yaml
+    compute_environment: LOCAL_MACHINE                                                                                             
+    deepspeed_config:                                                                                                              
+      gradient_accumulation_steps: 1
+      gradient_clipping: 0.7
+      offload_optimizer_device: cpu
+      offload_param_device: cpu
+      zero3_init_flag: true
+      zero_stage: 2
+    distributed_type: DEEPSPEED
+    downcast_bf16: 'no'
+    machine_rank: 0
+    main_training_function: main
+    mixed_precision: bf16
+    num_machines: 1
+    num_processes: 4
+    rdzv_backend: static
+    same_network: true
+    tpu_env: []
+    tpu_use_cluster: false
+    tpu_use_sudo: false
+    use_cpu: false
+    ```
+
+3. Run the Trainer script with args other than the ones handled above by accelerate config or launcher args.
+Below is an example to run `run_glue.py` using `accelerate launcher` with FSDP config from above. 
+
+```bash
+cd transformers
+
+accelerate launch \
+./examples/pytorch/text-classification/run_glue.py \
+--model_name_or_path bert-base-cased \
+--task_name $TASK_NAME \
+--do_train \
+--do_eval \
+--max_seq_length 128 \
+--per_device_train_batch_size 16 \
+--learning_rate 5e-5 \
+--num_train_epochs 3 \
+--output_dir /tmp/$TASK_NAME/ \
+--overwrite_output_dir
+```
+
+4. You can also directly use the cmd args for `accelerate launch`. Above example would map to:
+
+```bash
+cd transformers
+
+accelerate launch --num_processes=2 \
+--use_fsdp \
+--mixed_precision=bf16 \
+--fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP  \
+--fsdp_transformer_layer_cls_to_wrap="BertLayer" \
+--fsdp_sharding_strategy=1 \
+--fsdp_state_dict_type=FULL_STATE_DICT \
+./examples/pytorch/text-classification/run_glue.py
+--model_name_or_path bert-base-cased \
+--task_name $TASK_NAME \
+--do_train \
+--do_eval \
+--max_seq_length 128 \
+--per_device_train_batch_size 16 \
+--learning_rate 5e-5 \
+--num_train_epochs 3 \
+--output_dir /tmp/$TASK_NAME/ \
+--overwrite_output_dir
+```
+
+For more information, please refer the 🤗 Accelerate CLI guide: [Launching your 🤗 Accelerate scripts](https://huggingface.co/docs/accelerate/basic_tutorials/launch).
+
 Sections that were moved:
 
 [ <a href="./deepspeed#deepspeed-trainer-integration">DeepSpeed</a><a id="deepspeed"></a>

docs/source/en/model_doc/autoformer.mdx

@@ -0,0 +1,42 @@
+<!--Copyright 2023 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.
+-->
+
+# Autoformer
+
+## Overview
+
+The Autoformer model was proposed in [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
+
+This model augments the Transformer as a deep decomposition architecture, which can progressively decompose the trend and seasonal components during the forecasting process.
+
+The abstract from the paper is the following:
+
+*Extending the forecasting time is a critical demand for real applications, such as extreme weather early warning and long-term energy consumption planning. This paper studies the long-term forecasting problem of time series. Prior Transformer-based models adopt various self-attention mechanisms to discover the long-range dependencies. However, intricate temporal patterns of the long-term future prohibit the model from finding reliable dependencies. Also, Transformers have to adopt the sparse versions of point-wise self-attentions for long series efficiency, resulting in the information utilization bottleneck. Going beyond Transformers, we design Autoformer as a novel decomposition architecture with an Auto-Correlation mechanism. We break with the pre-processing convention of series decomposition and renovate it as a basic inner block of deep models. This design empowers Autoformer with progressive decomposition capacities for complex time series. Further, inspired by the stochastic process theory, we design the Auto-Correlation mechanism based on the series periodicity, which conducts the dependencies discovery and representation aggregation at the sub-series level. Auto-Correlation outperforms self-attention in both efficiency and accuracy. In long-term forecasting, Autoformer yields state-of-the-art accuracy, with a 38% relative improvement on six benchmarks, covering five practical applications: energy, traffic, economics, weather and disease.*
+
+This model was contributed by [elisim](https://huggingface.co/elisim) and [kashif](https://huggingface.co/kashif).
+The original code can be found [here](https://github.com/thuml/Autoformer).
+
+## AutoformerConfig
+
+[[autodoc]] AutoformerConfig
+
+
+## AutoformerModel
+
+[[autodoc]] AutoformerModel
+    - forward
+
+
+## AutoformerForPrediction
+
+[[autodoc]] AutoformerForPrediction
+    - forward

docs/source/en/model_doc/efficientformer.mdx

@@ -37,7 +37,7 @@ EfficientFormer-L7, obtains 83.3% accuracy with only 7.0 ms latency. Our work pr
 reach extremely low latency on mobile devices while maintaining high performance.*
 
 This model was contributed by [novice03](https://huggingface.co/novice03) and [Bearnardd](https://huggingface.co/Bearnardd).
-The original code can be found [here](https://github.com/snap-research/EfficientFormer).
+The original code can be found [here](https://github.com/snap-research/EfficientFormer). The TensorFlow version of this model was added by [D-Roberts](https://huggingface.co/D-Roberts).
 
 ## Documentation resources
 
@@ -66,3 +66,18 @@ The original code can be found [here](https://github.com/snap-research/Efficient
 
 [[autodoc]] EfficientFormerForImageClassificationWithTeacher
     - forward
+
+## TFEfficientFormerModel
+
+[[autodoc]] TFEfficientFormerModel
+    - call
+
+## TFEfficientFormerForImageClassification
+
+[[autodoc]] TFEfficientFormerForImageClassification
+    - call
+
+## TFEfficientFormerForImageClassificationWithTeacher
+
+[[autodoc]] TFEfficientFormerForImageClassificationWithTeacher
+    - call

docs/source/en/model_doc/encodec.mdx

@@ -0,0 +1,59 @@
+<!--Copyright 2023 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.
+-->
+
+# EnCodec
+
+## Overview
+
+The EnCodec neural codec model was proposed in [High Fidelity Neural Audio Compression](https://arxiv.org/abs/2210.13438) by Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi.
+
+The abstract from the paper is the following:
+
+*We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficiently reduces artifacts and produce high-quality samples. We introduce a novel loss balancer mechanism to stabilize training: the weight of a loss now defines the fraction of the overall gradient it should represent, thus decoupling the choice of this hyper-parameter from the typical scale of the loss. Finally, we study how lightweight Transformer models can be used to further compress the obtained representation by up to 40%, while staying faster than real time. We provide a detailed description of the key design choices of the proposed model including: training objective, architectural changes and a study of various perceptual loss functions. We present an extensive subjective evaluation (MUSHRA tests) together with an ablation study for a range of bandwidths and audio domains, including speech, noisy-reverberant speech, and music. Our approach is superior to the baselines methods across all evaluated settings, considering both 24 kHz monophonic and 48 kHz stereophonic audio.*
+
+This model was contributed by [Matthijs](https://huggingface.co/Matthijs), [Patrick Von Platen](https://huggingface.co/patrickvonplaten) and [Arthur Zucker](https://huggingface.co/ArthurZ). 
+The original code can be found [here](https://github.com/facebookresearch/encodec).
+Here is a quick example of how to encode and decode an audio using this model:
+
+```python 
+>>> from datasets import load_dataset, Audio
+>>> from transformers import EncodecModel, AutoProcessor
+>>> librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+
+>>> model = EncodecModel.from_pretrained("facebook/encodec_24khz")
+>>> processor = AutoProcessor.from_pretrained("facebook/encodec_24khz")
+>>> librispeech_dummy = librispeech_dummy.cast_column("audio", Audio(sampling_rate=processor.sampling_rate))
+>>> audio_sample = librispeech_dummy[-1]["audio"]["array"]
+>>> inputs = processor(raw_audio=audio_sample, sampling_rate=processor.sampling_rate, return_tensors="pt")
+
+>>> encoder_outputs = model.encode(inputs["input_values"], inputs["padding_mask"])
+>>> audio_values = model.decode(encoder_outputs.audio_codes, encoder_outputs.audio_scales, inputs["padding_mask"])[0]
+>>> # or the equivalent with a forward pass
+>>> audio_values = model(inputs["input_values"], inputs["padding_mask"]).audio_values
+```
+
+
+## EncodecConfig
+
+[[autodoc]] EncodecConfig
+
+## EncodecFeatureExtractor
+
+[[autodoc]] EncodecFeatureExtractor
+    - __call__
+
+## EncodecModel
+
+[[autodoc]] EncodecModel
+    - decode
+    - encode
+    - forward

docs/source/en/model_doc/graphormer.mdx

@@ -13,7 +13,7 @@ specific language governing permissions and limitations under the License.
 ## Overview
 
 The Graphormer model was proposed in [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234)  by 
-Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie-Yan Liu. It is a Graph Transformer model, modified to allow computations on graphs instead of text sequences by generating embeddings and features of interest during preprocessign and collation, then using a modified attention.
+Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen and Tie-Yan Liu. It is a Graph Transformer model, modified to allow computations on graphs instead of text sequences by generating embeddings and features of interest during preprocessing and collation, then using a modified attention.
 
 The abstract from the paper is the following:
 

docs/source/en/model_doc/informer.mdx

@@ -25,6 +25,8 @@ The abstract from the paper is the following:
 This model was contributed by [elisim](https://huggingface.co/elisim) and [kashif](https://huggingface.co/kashif).
 The original code can be found [here](https://github.com/zhouhaoyi/Informer2020).
 
+Tips:
+- Check out the Informer blog-post in HuggingFace blog: [Multivariate Probabilistic Time Series Forecasting with Informer](https://huggingface.co/blog/informer)
 
 ## InformerConfig
 

docs/source/en/model_doc/llama.mdx

@@ -65,6 +65,7 @@ This model was contributed by [zphang](https://huggingface.co/zphang) with contr
     - build_inputs_with_special_tokens
     - get_special_tokens_mask
     - create_token_type_ids_from_sequences
+    - update_post_processor
     - save_vocabulary
 
 ## LlamaModel

docs/source/en/model_doc/mms.mdx

@@ -0,0 +1,236 @@
+<!--Copyright 2023 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.
+-->
+
+# MMS
+
+## Overview
+
+The MMS model was proposed in [Scaling Speech Technology to 1,000+ Languages](https://arxiv.org/abs/2305.13516) 
+by Vineel Pratap, Andros Tjandra, Bowen Shi, Paden Tomasello, Arun Babu, Sayani Kundu, Ali Elkahky, Zhaoheng Ni, Apoorv Vyas, Maryam Fazel-Zarandi, Alexei Baevski, Yossi Adi, Xiaohui Zhang, Wei-Ning Hsu, Alexis Conneau, Michael Auli
+
+The abstract from the paper is the following:
+
+*Expanding the language coverage of speech technology has the potential to improve access to information for many more people. 
+However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000
+languages spoken around the world. 
+The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. 
+The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging
+self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, 
+a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models 
+for the same number of languages, as well as a language identification model for 4,017 languages. 
+Experiments show that our multilingual speech recognition model more than halves the word error rate of 
+Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data.*
+
+Here are the different models open sourced in the MMS project. The models and code are originally released [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms). We have add them to the `transformers` framework, making them easier to use.
+
+### Automatic Speech Recognition (ASR)
+
+The ASR model checkpoints  can be found here : [mms-1b-fl102](https://huggingface.co/facebook/mms-1b-fl102), [mms-1b-l1107](https://huggingface.co/facebook/mms-1b-l1107), [mms-1b-all](https://huggingface.co/facebook/mms-1b-all). For best accuracy, use the `mms-1b-all` model. 
+
+Tips:
+
+- All ASR models accept a float array corresponding to the raw waveform of the speech signal. The raw waveform should be pre-processed with [`Wav2Vec2FeatureExtractor`].
+- The models were trained using connectionist temporal classification (CTC) so the model output has to be decoded using
+  [`Wav2Vec2CTCTokenizer`].
+- You can load different language adapter weights for different languages via [`~Wav2Vec2PreTrainedModel.load_adapter`]. Language adapters only consists of roughly 2 million parameters 
+  and can therefore be efficiently loaded on the fly when needed.
+
+#### Loading
+
+By default MMS loads adapter weights for English. If you want to load adapter weights of another language 
+make sure to specify `target_lang=<your-chosen-target-lang>` as well as `"ignore_mismatched_sizes=True`.
+The `ignore_mismatched_sizes=True` keyword has to be passed to allow the language model head to be resized according
+to the vocabulary of the specified language.
+Similarly, the processor should be loaded with the same target language
+
+```py
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+
+model_id = "facebook/mms-1b-all"
+target_lang = "fra"
+
+processor = AutoProcessor.from_pretrained(model_id, target_lang=target_lang)
+model = Wav2Vec2ForCTC.from_pretrained(model_id, target_lang=target_lang, ignore_mismatched_sizes=True)
+```
+
+<Tip>
+
+You can safely ignore a warning such as:
+
+```text
+Some weights of Wav2Vec2ForCTC were not initialized from the model checkpoint at facebook/mms-1b-all and are newly initialized because the shapes did not match:
+- lm_head.bias: found shape torch.Size([154]) in the checkpoint and torch.Size([314]) in the model instantiated
+- lm_head.weight: found shape torch.Size([154, 1280]) in the checkpoint and torch.Size([314, 1280]) in the model instantiated
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+```
+
+</Tip>
+
+If you want to use the ASR pipeline, you can load your chosen target language as such:
+
+```py
+from transformers import pipeline
+
+model_id = "facebook/mms-1b-all"
+target_lang = "fra"
+
+pipe = pipeline(model=model_id, model_kwargs={"target_lang": "fra", "ignore_mismatched_sizes": True})
+```
+
+#### Inference
+
+Next, let's look at how we can run MMS in inference and change adapter layers after having called [`~PretrainedModel.from_pretrained`]
+First, we load audio data in different languages using the [Datasets](https://github.com/huggingface/datasets).
+
+```py
+from datasets import load_dataset, Audio
+
+# English
+stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True)
+stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
+en_sample = next(iter(stream_data))["audio"]["array"]
+
+# French
+stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "fr", split="test", streaming=True)
+stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
+fr_sample = next(iter(stream_data))["audio"]["array"]
+```
+
+Next, we load the model and processor
+
+```py
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+import torch
+
+model_id = "facebook/mms-1b-all"
+
+processor = AutoProcessor.from_pretrained(model_id)
+model = Wav2Vec2ForCTC.from_pretrained(model_id)
+```
+
+Now we process the audio data, pass the processed audio data to the model and transcribe the model output,
+just like we usually do for [`Wav2Vec2ForCTC`].
+
+```py
+inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs).logits
+
+ids = torch.argmax(outputs, dim=-1)[0]
+transcription = processor.decode(ids)
+# 'joe keton disapproved of films and buster also had reservations about the media'
+```
+
+We can now keep the same model in memory and simply switch out the language adapters by
+calling the convenient [`~Wav2Vec2ForCTC.load_adapter`] function for the model and [`~Wav2Vec2CTCTokenizer.set_target_lang`] for the tokenizer.
+We pass the target language as an input - `"fra"` for French.
+
+```py
+processor.tokenizer.set_target_lang("fra")
+model.load_adapter("fra")
+
+inputs = processor(fr_sample, sampling_rate=16_000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs).logits
+
+ids = torch.argmax(outputs, dim=-1)[0]
+transcription = processor.decode(ids)
+# "ce dernier est volé tout au long de l'histoire romaine"
+```
+
+In the same way the language can be switched out for all other supported languages. Please have a look at:
+
+```py
+processor.tokenizer.vocab.keys()
+```
+
+to see all supported languages.
+
+To further improve performance from ASR models, language model decoding can be used. See the documentation [here](https://huggingface.co/facebook/mms-1b-all) for further details.  
+
+### Speech Synthesis (TTS)
+
+Individual TTS models are available for each of the 1100+ languages. The models and inference documentation can be found [here](https://huggingface.co/facebook/mms-tts).
+
+### Language Identification (LID)
+
+Different LID models are available based on the number of languages they can recognize - [126](https://huggingface.co/facebook/mms-lid-126), [256](https://huggingface.co/facebook/mms-lid-256), [512](https://huggingface.co/facebook/mms-lid-512), [1024](https://huggingface.co/facebook/mms-lid-1024), [2048](https://huggingface.co/facebook/mms-lid-2048), [4017](https://huggingface.co/facebook/mms-lid-4017). 
+
+#### Inference
+First, we install transformers and some other libraries
+```
+pip install torch accelerate torchaudio datasets
+pip install --upgrade transformers
+````
+pip install torch datasets[audio]
+Next, we load a couple of audio samples via `datasets`. Make sure that the audio data is sampled to 16000 kHz.
+
+```py
+from datasets import load_dataset, Audio
+
+# English
+stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "en", split="test", streaming=True)
+stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
+en_sample = next(iter(stream_data))["audio"]["array"]
+
+# Arabic
+stream_data = load_dataset("mozilla-foundation/common_voice_13_0", "ar", split="test", streaming=True)
+stream_data = stream_data.cast_column("audio", Audio(sampling_rate=16000))
+ar_sample = next(iter(stream_data))["audio"]["array"]
+```
+
+Next, we load the model and processor
+
+```py
+from transformers import Wav2Vec2ForSequenceClassification, AutoFeatureExtractor
+import torch
+
+model_id = "facebook/mms-lid-126"
+
+processor = AutoFeatureExtractor.from_pretrained(model_id)
+model = Wav2Vec2ForSequenceClassification.from_pretrained(model_id)
+```
+
+Now we process the audio data, pass the processed audio data to the model to classify it into a language, just like we usually do for Wav2Vec2 audio classification models such as [ehcalabres/wav2vec2-lg-xlsr-en-speech-emotion-recognition](https://huggingface.co/harshit345/xlsr-wav2vec-speech-emotion-recognition)
+
+```py
+# English
+inputs = processor(en_sample, sampling_rate=16_000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs).logits
+
+lang_id = torch.argmax(outputs, dim=-1)[0].item()
+detected_lang = model.config.id2label[lang_id]
+# 'eng'
+
+# Arabic
+inputs = processor(ar_sample, sampling_rate=16_000, return_tensors="pt")
+
+with torch.no_grad():
+    outputs = model(**inputs).logits
+
+lang_id = torch.argmax(outputs, dim=-1)[0].item()
+detected_lang = model.config.id2label[lang_id]
+# 'ara'
+```
+
+To see all the supported languages of a checkpoint, you can print out the language ids as follows:
+```py
+processor.id2label.values()
+```
+
+### Audio Pretrained Models
+
+Pretrained models are available for two different sizes - [300M](https://huggingface.co/facebook/mms-300m) , [1Bil](https://huggingface.co/facebook/mms-1b). The architecture is based on the Wav2Vec2 model, so one can refer to [Wav2Vec2's documentation page](wav2vec2) for further details on how to finetune with models for various downstream tasks.

docs/source/en/model_doc/mobilevitv2.mdx

@@ -0,0 +1,53 @@
+<!--Copyright 2023 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.
+-->
+
+# MobileViTV2
+
+## Overview
+
+The MobileViTV2 model was proposed in [Separable Self-attention for Mobile Vision Transformers](https://arxiv.org/abs/2206.02680) by Sachin Mehta and Mohammad Rastegari.
+
+MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention.
+
+The abstract from the paper is the following:
+
+*Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutional neural network-based models. The main efficiency bottleneck in MobileViT is the multi-headed self-attention (MHA) in transformers, which requires O(k2) time complexity with respect to the number of tokens (or patches) k. Moreover, MHA requires costly operations (e.g., batch-wise matrix multiplication) for computing self-attention, impacting latency on resource-constrained devices. This paper introduces a separable self-attention method with linear complexity, i.e. O(k). A simple yet effective characteristic of the proposed method is that it uses element-wise operations for computing self-attention, making it a good choice for resource-constrained devices. The improved model, MobileViTV2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection. With about three million parameters, MobileViTV2 achieves a top-1 accuracy of 75.6% on the ImageNet dataset, outperforming MobileViT by about 1% while running 3.2× faster on a mobile device.*
+
+Tips:
+
+- MobileViTV2 is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map.
+- One can use [`MobileViTImageProcessor`] to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB).
+- The available image classification checkpoints are pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes).
+- The segmentation model uses a [DeepLabV3](https://arxiv.org/abs/1706.05587) head. The available semantic segmentation checkpoints are pre-trained on [PASCAL VOC](http://host.robots.ox.ac.uk/pascal/VOC/).
+
+This model was contributed by [shehan97](https://huggingface.co/shehan97).
+The original code can be found [here](https://github.com/apple/ml-cvnets).
+
+
+## MobileViTV2Config
+
+[[autodoc]] MobileViTV2Config
+
+## MobileViTV2Model
+
+[[autodoc]] MobileViTV2Model
+    - forward
+
+## MobileViTV2ForImageClassification
+
+[[autodoc]] MobileViTV2ForImageClassification
+    - forward
+
+## MobileViTV2ForSemanticSegmentation
+
+[[autodoc]] MobileViTV2ForSemanticSegmentation
+    - forward

docs/source/en/model_doc/open-llama.mdx

@@ -16,7 +16,7 @@ specific language governing permissions and limitations under the License.
 
 The Open-Llama model was proposed in [Open-Llama project](https://github.com/s-JoL/Open-Llama) by community developer s-JoL.
 
-The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PLAM.
+The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PaLM.
 And the model is pre-trained on both Chinese and English, which gives it better performance on Chinese language tasks.
 
 This model was contributed by [s-JoL](https://huggingface.co/s-JoL).

docs/source/en/model_doc/opt.mdx

@@ -23,7 +23,7 @@ The abstract from the paper is the following:
 
 Tips:
 - OPT has the same architecture as [`BartDecoder`].
-- Contrary to GPT2, OPT adds the EOS token `</s>` to the beginning of every prompt. **Note**: Make sure to pass `use_fast=False` when loading OPT's tokenizer with [`AutoTokenizer`] to get the correct tokenizer.
+- Contrary to GPT2, OPT adds the EOS token `</s>` to the beginning of every prompt.
 
 This model was contributed by [Arthur Zucker](https://huggingface.co/ArthurZ), [Younes Belkada](https://huggingface.co/ybelkada), and [Patrick Von Platen](https://huggingface.co/patrickvonplaten).
 The original code can be found [here](https://github.com/facebookresearch/metaseq).

docs/source/en/model_doc/pix2struct.mdx

@@ -25,6 +25,8 @@ Tips:
 Pix2Struct has been fine tuned on a variety of tasks and datasets, ranging from image captioning, visual question answering (VQA) over different inputs (books, charts, science diagrams), captioning UI components etc. The full list can be found in Table 1 of the paper.
 We therefore advise you to use these models for the tasks they have been fine tuned on. For instance, if you want to use Pix2Struct for UI captioning, you should use the model fine tuned on the UI dataset. If you want to use Pix2Struct for image captioning, you should use the model fine tuned on the natural images captioning dataset and so on.
 
+If you want to use the model to perform conditional text captioning, make sure to use the processor with `add_special_tokens=False`.
+
 This model was contributed by [ybelkada](https://huggingface.co/ybelkada).
 The original code can be found [here](https://github.com/google-research/pix2struct).
 

docs/source/en/model_doc/sam.mdx

@@ -99,3 +99,9 @@ Resources:
 
 [[autodoc]] SamModel
     - forward
+
+
+## TFSamModel
+
+[[autodoc]] TFSamModel
+    - call

docs/source/en/model_doc/time_series_transformer.mdx

@@ -25,6 +25,7 @@ The Time Series Transformer model is a vanilla encoder-decoder Transformer for t
 
 Tips:
 
+- Check out the Time Series Transformer blog-post in HuggingFace blog: [Probabilistic Time Series Forecasting with 🤗 Transformers](https://huggingface.co/blog/time-series-transformers)
 - 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.

docs/source/en/model_doc/wav2vec2.mdx

@@ -69,6 +69,7 @@ A list of official Hugging Face and community (indicated by 🌎) resources to h
     - save_vocabulary
     - decode
     - batch_decode
+    - set_target_lang
 
 ## Wav2Vec2FeatureExtractor
 
@@ -171,6 +172,7 @@ Otherwise, [`~Wav2Vec2ProcessorWithLM.batch_decode`] performance will be slower
 
 [[autodoc]] Wav2Vec2ForCTC
     - forward
+    - load_adapter
 
 ## Wav2Vec2ForSequenceClassification
 

docs/source/en/perf_infer_gpu_one.mdx

@@ -34,6 +34,61 @@ model.save_pretrained("saved_model")
 
 As of PyTorch 2.0, the attention fastpath is supported for both encoders and decoders. The list of supported architectures can be found [here](https://huggingface.co/docs/optimum/bettertransformer/overview#supported-models).
 
+## `bitsandbytes` integration for FP4 mixed-precision inference
+
+You can install `bitsandbytes` and benefit from easy model compression on GPUs. Using FP4 quantization you can expect to reduce up to 8x the model size compared to its native full precision version. Check out below how to get started.
+
+<Tip>
+
+Note that this feature can also be used in a multi GPU setup.
+
+</Tip>
+
+### Requirements
+
+- Latest `bitsandbytes` library
+`pip install bitsandbytes>=0.39.0`
+
+- Install latest `accelerate` from source
+`pip install git+https://github.com/huggingface/accelerate.git`
+
+- Install latest `transformers` from source 
+`pip install git+https://github.com/huggingface/transformers.git`
+
+### Running FP4 models - single GPU setup - Quickstart
+
+You can quickly run a FP4 model on a single GPU by running the following code:
+
+```py
+from transformers import AutoModelForCausalLM
+
+model_name = "bigscience/bloom-2b5"
+model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
+```
+Note that `device_map` is optional but setting `device_map = 'auto'` is prefered for inference as it will dispatch efficiently the model on the available ressources.
+
+### Running FP4 models - multi GPU setup 
+
+The way to load your mixed 8-bit model in multiple GPUs is as follows (same command as single GPU setup):
+```py
+model_name = "bigscience/bloom-2b5"
+model_8bit = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
+```
+But you can control the GPU RAM you want to allocate on each GPU using `accelerate`. Use the `max_memory` argument as follows:
+
+```py
+max_memory_mapping = {0: "600MB", 1: "1GB"}
+model_name = "bigscience/bloom-3b"
+model_8bit = AutoModelForCausalLM.from_pretrained(
+    model_name, device_map="auto", load_in_4bit=True, max_memory=max_memory_mapping
+)
+```
+In this example, the first GPU will use 600MB of memory and the second 1GB.
+
+### Advanced usage 
+
+For more advanced usage of this method, please have a look at the [quantization](main_classes/quantization) documentation page.
+
 ## `bitsandbytes` integration for Int8 mixed-precision matrix decomposition
 
 <Tip>

docs/source/en/quicktour.mdx

@@ -532,12 +532,12 @@ All models are a standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs
    ... )  # doctest: +SKIP
    ```
 
-5. When you're ready, you can call `compile` and `fit` to start training:
+5. When you're ready, you can call `compile` and `fit` to start training. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
    ```py
    >>> from tensorflow.keras.optimizers import Adam
 
-   >>> model.compile(optimizer=Adam(3e-5))
+   >>> model.compile(optimizer=Adam(3e-5))  # No loss argument!
    >>> model.fit(tf_dataset)  # doctest: +SKIP
    ```
 

docs/source/en/serialization.mdx

@@ -12,13 +12,20 @@ specific language governing permissions and limitations under the License.
 
 # Export to ONNX
 
-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).
+Deploying 🤗 Transformers models in production environments often requires, or can benefit from exporting the models into 
+a serialized format that can be loaded and executed on specialized runtimes and hardware.
 
-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
+🤗 Optimum is an extension of Transformers that enables exporting models from PyTorch or TensorFlow to serialized formats 
+such as ONNX and TFLite through its `exporters` module. 🤗 Optimum also provides a set of performance optimization tools to train 
+and run models on targeted hardware with maximum efficiency.
+
+This guide demonstrates how you can export 🤗 Transformers models to ONNX with 🤗 Optimum, for the guide on exporting models to TFLite, 
+please refer to the [Export to TFLite page](tflite).
+
+## Export to ONNX 
+
+[ONNX (Open Neural Network eXchange)](http://onnx.ai) 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.
@@ -27,172 +34,141 @@ By exposing a graph with standardized operators and data types, ONNX makes it ea
 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.
-
-<Tip>
-
-You can also export 🤗 Transformers models with the [`optimum.exporters.onnx` package](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model)
-from 🤗 Optimum.
-
-Once exported, a model can be:
-
-- Optimized for inference via techniques such as quantization and graph optimization.
-- Run with ONNX Runtime via [`ORTModelForXXX` classes](https://huggingface.co/docs/optimum/onnxruntime/package_reference/modeling_ort),
+Once exported to ONNX format, a model can be:
+- optimized for inference via techniques such as [graph optimization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/optimization) and [quantization](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization). 
+- run with ONNX Runtime via [`ORTModelForXXX` classes](https://huggingface.co/docs/optimum/onnxruntime/package_reference/modeling_ort),
 which follow the same `AutoModel` API as the one you are used to in 🤗 Transformers.
-- Run with [optimized inference pipelines](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines),
-which has the same API as the [`pipeline`] function in 🤗 Transformers.
+- run with [optimized inference pipelines](https://huggingface.co/docs/optimum/main/en/onnxruntime/usage_guides/pipelines),
+which has the same API as the [`pipeline`] function in 🤗 Transformers. 
 
-To explore all these features,  check out the [🤗 Optimum library](https://github.com/huggingface/optimum).
+🤗 Optimum provides support for the ONNX export 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.
 
-</Tip>
+For the list of ready-made configurations, please refer to [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/onnx/overview).
 
-Ready-made configurations include the following architectures:
+There are two ways to export a 🤗 Transformers model to ONNX, here we show both:
 
-<!--This table is automatically generated by `make fix-copies`, do not fill manually!-->
+- export with 🤗 Optimum via CLI.
+- export with 🤗 Optimum with `optimum.onnxruntime`.
 
-- ALBERT
-- BART
-- BEiT
-- BERT
-- BigBird
-- BigBird-Pegasus
-- Blenderbot
-- BlenderbotSmall
-- BLOOM
-- CamemBERT
-- Chinese-CLIP
-- CLIP
-- CodeGen
-- Conditional DETR
-- ConvBERT
-- ConvNeXT
-- Data2VecText
-- Data2VecVision
-- DeBERTa
-- DeBERTa-v2
-- DeiT
-- DETR
-- DistilBERT
-- EfficientNet
-- ELECTRA
-- ERNIE
-- FlauBERT
-- GPT Neo
-- GPT-J
-- GPT-Sw3
-- GroupViT
-- I-BERT
-- ImageGPT
-- LayoutLM
-- LayoutLMv3
-- LeViT
-- Longformer
-- LongT5
-- M2M100
-- Marian
-- mBART
-- MEGA
-- MobileBERT
-- MobileNetV1
-- MobileNetV2
-- MobileViT
-- MT5
-- OpenAI GPT-2
-- OWL-ViT
-- Perceiver
-- PLBart
-- PoolFormer
-- RemBERT
-- ResNet
-- RoBERTa
-- RoBERTa-PreLayerNorm
-- RoFormer
-- SegFormer
-- SqueezeBERT
-- SwiftFormer
-- Swin Transformer
-- T5
-- Table Transformer
-- Vision Encoder decoder
-- ViT
-- Whisper
-- X-MOD
-- XLM
-- XLM-RoBERTa
-- XLM-RoBERTa-XL
-- YOLOS
+### Exporting a 🤗 Transformers model to ONNX with CLI
 
-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
-
-<Tip>
-
-The recommended way of exporting a model is now to use
-[`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli),
-do not worry it is very similar to `transformers.onnx`!
-
-</Tip>
-
-To export a 🤗 Transformers model to ONNX, you'll first need to install some extra
-dependencies:
+To export a 🤗 Transformers model to ONNX, first install an extra dependency:
 
 ```bash
-pip install transformers[onnx]
+pip install optimum[exporters]
 ```
 
-The `transformers.onnx` package can then be used as a Python module:
+To check out all available arguments, refer to the [🤗 Optimum docs](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli), 
+or view help in command line:
 
 ```bash
-python -m transformers.onnx --help
-
-usage: Hugging Face Transformers ONNX exporter [-h] -m MODEL [--feature {causal-lm, ...}] [--opset OPSET] [--atol ATOL] output
-
-positional arguments:
-  output                Path indicating where to store generated ONNX model.
-
-optional arguments:
-  -h, --help            show this help message and exit
-  -m MODEL, --model MODEL
-                        Model ID on huggingface.co or path on disk to load model from.
-  --feature {causal-lm, ...}
-                        The type of features to export the model with.
-  --opset OPSET         ONNX opset version to export the model with.
-  --atol ATOL           Absolute difference tolerance when validating the model.
+optimum-cli export onnx --help
 ```
 
-Exporting a checkpoint using a ready-made configuration can be done as follows:
+To export a model's checkpoint from the 🤗 Hub, for example, `distilbert-base-uncased-distilled-squad`, run the following command: 
 
 ```bash
-python -m transformers.onnx --model=distilbert-base-uncased onnx/
+optimum-cli export onnx --model distilbert-base-uncased-distilled-squad distilbert_base_uncased_squad_onnx/
 ```
 
-You should see the following logs:
+You should see the logs indicating progress and showing where the resulting `model.onnx` is saved, like this:
 
 ```bash
-Validating ONNX model...
-        -[✓] ONNX model output names match reference model ({'last_hidden_state'})
-        - Validating ONNX Model output "last_hidden_state":
-                -[✓] (2, 8, 768) matches (2, 8, 768)
-                -[✓] all values close (atol: 1e-05)
-All good, model saved at: onnx/model.onnx
+Validating ONNX model distilbert_base_uncased_squad_onnx/model.onnx...
+	-[✓] ONNX model output names match reference model (start_logits, end_logits)
+	- Validating ONNX Model output "start_logits":
+		-[✓] (2, 16) matches (2, 16)
+		-[✓] all values close (atol: 0.0001)
+	- Validating ONNX Model output "end_logits":
+		-[✓] (2, 16) matches (2, 16)
+		-[✓] all values close (atol: 0.0001)
+The ONNX export succeeded and the exported model was saved at: distilbert_base_uncased_squad_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.
+The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
+saved both the model's weights and tokenizer files in the same directory (`local_path`). When using CLI, pass the 
+`local_path` to the `model` argument instead of the checkpoint name on 🤗 Hub and provide the `--task` argument. 
+You can review the list of supported tasks in the [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/task_manager).
+If `task` argument is not provided, it will default to the model architecture without any task specific head.
+
+```bash
+optimum-cli export onnx --model local_path --task question-answering distilbert_base_uncased_squad_onnx/
+```
 
 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
 Runtime](https://onnxruntime.ai/) as follows:
 
+```python
+>>> from transformers import AutoTokenizer
+>>> from optimum.onnxruntime import ORTModelForQuestionAnswering
+
+>>> tokenizer = AutoTokenizer.from_pretrained("distilbert_base_uncased_squad_onnx")
+>>> model = ORTModelForQuestionAnswering.from_pretrained("distilbert_base_uncased_squad_onnx")
+>>> inputs = tokenizer("What am I using?", "Using DistilBERT with ONNX Runtime!", return_tensors="pt")
+>>> outputs = model(**inputs)
+```
+
+The process is identical for TensorFlow checkpoints on the Hub. For instance, here's how you would
+export a pure TensorFlow checkpoint from the [Keras organization](https://huggingface.co/keras-io):
+
+```bash
+optimum-cli export onnx --model keras-io/transformers-qa distilbert_base_cased_squad_onnx/
+```
+
+### Exporting a 🤗 Transformers model to ONNX with `optimum.onnxruntime`
+
+Alternative to CLI, you can export a 🤗 Transformers model to ONNX programmatically like so: 
+
+```python
+>>> from optimum.onnxruntime import ORTModelForSequenceClassification
+>>> from transformers import AutoTokenizer
+
+>>> model_checkpoint = "distilbert_base_uncased_squad"
+>>> save_directory = "onnx/"
+
+>>> # Load a model from transformers and export it to ONNX
+>>> ort_model = ORTModelForSequenceClassification.from_pretrained(model_checkpoint, export=True)
+>>> tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+>>> # Save the onnx model and tokenizer
+>>> ort_model.save_pretrained(save_directory)
+>>> tokenizer.save_pretrained(save_directory)
+```
+
+### Exporting a model for an unsupported architecture
+
+If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
+supported in [`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/exporters/onnx/overview),
+and if it is not, [contribute to 🤗 Optimum](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/contribute)
+directly.
+
+### Exporting a model with `transformers.onnx`
+
+<Tip warning={true}>
+
+`tranformers.onnx` is no longer maintained, please export models with 🤗 Optimum as described above. This section will be removed in the future versions.
+
+</Tip>
+
+To export a 🤗 Transformers model to ONNX with `tranformers.onnx`, install extra dependencies:
+
+```bash
+pip install transformers[onnx]
+```
+
+Use `transformers.onnx` package as a Python module to export a checkpoint using a ready-made configuration:
+
+```bash
+python -m transformers.onnx --model=distilbert-base-uncased onnx/
+```
+
+This exports an ONNX graph of the checkpoint defined by the `--model` argument. Pass any checkpoint on the 🤗 Hub or one that's stored locally.
+The resulting `model.onnx` file can then be run on one of the many accelerators that support the ONNX standard. For example, 
+load and run the model with ONNX Runtime as follows:
+
 ```python
 >>> from transformers import AutoTokenizer
 >>> from onnxruntime import InferenceSession
@@ -204,8 +180,8 @@ 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 (like `["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
@@ -216,327 +192,15 @@ 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
-organization](https://huggingface.co/keras-io) as follows:
+The process is identical for TensorFlow checkpoints on the Hub. For example, export a pure TensorFlow checkpoint like so:
 
 ```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:
-
-<frameworkcontent> <pt>
-```python
->>> from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
->>> # Load tokenizer and PyTorch weights form the Hub
->>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
->>> pt_model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
->>> # Save to disk
->>> tokenizer.save_pretrained("local-pt-checkpoint")
->>> pt_model.save_pretrained("local-pt-checkpoint")
-```
-
-Once the checkpoint is saved, we can export it to ONNX by pointing the `--model`
-argument of the `transformers.onnx` package to the desired directory:
+To export a model that's stored locally, save the model's weights and tokenizer files in the same directory (e.g. `local-pt-checkpoint`), 
+then export it to ONNX by pointing the `--model` argument of the `transformers.onnx` package to the desired directory:
 
 ```bash
 python -m transformers.onnx --model=local-pt-checkpoint onnx/
-```
-</pt> <tf>
-```python
->>> from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
-
->>> # Load tokenizer and TensorFlow weights from the Hub
->>> tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
->>> tf_model = TFAutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased")
->>> # Save to disk
->>> tokenizer.save_pretrained("local-tf-checkpoint")
->>> tf_model.save_pretrained("local-tf-checkpoint")
-```
-
-Once the checkpoint is saved, we can export it to ONNX by pointing the `--model`
-argument of the `transformers.onnx` package to the desired directory:
-
-```bash
-python -m transformers.onnx --model=local-tf-checkpoint onnx/
-```
-</tf> </frameworkcontent>
-
-## Selecting features for different model tasks
-
-<Tip>
-
-The recommended way of exporting a model is now to use `optimum.exporters.onnx`.
-You can check the [🤗 Optimum documentation](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#selecting-a-task)
-to learn how to select a task.
-
-</Tip>
-
-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`:
-
-| Feature                              | Auto Class                           |
-| ------------------------------------ | ------------------------------------ |
-| `causal-lm`, `causal-lm-with-past`   | `AutoModelForCausalLM`               |
-| `default`, `default-with-past`       | `AutoModel`                          |
-| `masked-lm`                          | `AutoModelForMaskedLM`               |
-| `question-answering`                 | `AutoModelForQuestionAnswering`      |
-| `seq2seq-lm`, `seq2seq-lm-with-past` | `AutoModelForSeq2SeqLM`              |
-| `sequence-classification`            | `AutoModelForSequenceClassification` |
-| `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:
-
-```python
->>> from transformers.onnx.features import FeaturesManager
-
->>> distilbert_features = list(FeaturesManager.get_supported_features_for_model_type("distilbert").keys())
->>> print(distilbert_features)
-["default", "masked-lm", "causal-lm", "sequence-classification", "token-classification", "question-answering"]
-```
-
-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:
-
-```bash
-python -m transformers.onnx --model=distilbert-base-uncased-finetuned-sst-2-english \
-                            --feature=sequence-classification onnx/
-```
-
-This displays the following logs:
-
-```bash
-Validating ONNX model...
-        -[✓] ONNX model output names match reference model ({'logits'})
-        - Validating ONNX Model output "logits":
-                -[✓] (2, 2) matches (2, 2)
-                -[✓] all values close (atol: 1e-05)
-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.
-
-<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.
-
-</Tip>
-
-
-## Exporting a model for an unsupported architecture
-
-<Tip>
-
-If you wish to contribute by adding support for a model that cannot be currently exported, you should first check if it is
-supported in [`optimum.exporters.onnx`](https://huggingface.co/docs/optimum/main/en/exporters/onnx/package_reference/configuration#supported-architectures),
-and if it is not, [contribute to 🤗 Optimum](https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/contribute)
-directly.
-
-</Tip>
-
-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.
-
-### 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:
-
-* Encoder-based models inherit from [`~onnx.config.OnnxConfig`]
-* Decoder-based models inherit from [`~onnx.config.OnnxConfigWithPast`]
-* Encoder-decoder models inherit from [`~onnx.config.OnnxSeq2SeqConfigWithPast`]
-
-<Tip>
-
-A good way to implement a custom ONNX configuration is to look at the existing
-implementation in the `configuration_<model_name>.py` file of a similar architecture.
-
-</Tip>
-
-Since DistilBERT is an encoder-based model, its configuration inherits from
-`OnnxConfig`:
-
-```python
->>> from typing import Mapping, OrderedDict
->>> from transformers.onnx import OnnxConfig
-
-
->>> class DistilBertOnnxConfig(OnnxConfig):
-...     @property
-...     def inputs(self) -> Mapping[str, Mapping[int, str]]:
-...         return OrderedDict(
-...             [
-...                 ("input_ids", {0: "batch", 1: "sequence"}),
-...                 ("attention_mask", {0: "batch", 1: "sequence"}),
-...             ]
-...         )
-```
-
-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.
-
-</Tip>
-
-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
-
->>> config = AutoConfig.from_pretrained("distilbert-base-uncased")
->>> 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:
-
-```python
->>> print(onnx_config.default_onnx_opset)
-11
-```
-
-You can also view the outputs associated with the model as follows:
-
-```python
->>> print(onnx_config.outputs)
-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:
-
-```python
->>> from transformers import AutoConfig
-
->>> config = AutoConfig.from_pretrained("distilbert-base-uncased")
->>> onnx_config_for_seq_clf = DistilBertOnnxConfig(config, task="sequence-classification")
->>> print(onnx_config_for_seq_clf.outputs)
-OrderedDict([('logits', {0: 'batch'})])
-```
-
-<Tip>
-
-All of the base properties and methods associated with [`~onnx.config.OnnxConfig`] and
-the other configuration classes can be overridden if needed. Check out [`BartOnnxConfig`]
-for an advanced example.
-
-</Tip>
-
-### 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:
-
-```python
->>> from pathlib import Path
->>> from transformers.onnx import export
->>> from transformers import AutoTokenizer, AutoModel
-
->>> onnx_path = Path("model.onnx")
->>> model_ckpt = "distilbert-base-uncased"
->>> base_model = AutoModel.from_pretrained(model_ckpt)
->>> tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
-
->>> 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:
-
-```python
->>> import onnx
-
->>> onnx_model = onnx.load("model.onnx")
->>> onnx.checker.check_model(onnx_model)
-```
-
-<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.
-
-</Tip>
-
-### 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:
-
-```python
->>> from transformers.onnx import validate_model_outputs
-
->>> validate_model_outputs(
-...     onnx_config, tokenizer, base_model, onnx_path, onnx_outputs, onnx_config.atol_for_validation
-... )
-```
-
-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.
-
-## 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:
-
-* Implement the ONNX configuration in the corresponding `configuration_<model_name>.py`
-file
-* 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.
+```

docs/source/en/tasks/document_question_answering.mdx

@@ -40,9 +40,6 @@ LayoutLMv2 solves the document question-answering task by adding a question-answ
 states of the tokens, to predict the positions of the start and end tokens of the
 answer. In other words, the problem is treated as extractive question answering: given the context, extract which piece
 of information answers the question. The context comes from the output of an OCR engine, here it is Google's Tesseract.
-states of the tokens, in order to predict which token is at the start of the answer and which token is at the end of the
-answer. In other words, the problem is treated as extractive question answering: given the context, extract which piece
-of information answers the question. The context comes from the output of an OCR engine, here it is Google's Tesseract.
 
 Before you begin, make sure you have all the necessary libraries installed. LayoutLMv2 depends on detectron2, torchvision and tesseract.
 

docs/source/en/tasks/image_classification.mdx

@@ -30,7 +30,7 @@ The task illustrated in this tutorial is supported by the following model archit
 
 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
 
-[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
+[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
 <!--End of the generated tip-->
 
 </Tip>
@@ -289,7 +289,7 @@ You're ready to start training your model now! Load ViT with [`AutoModelForImage
 
 At this point, only three steps remain:
 
-1. Define your training hyperparameters in [`TrainingArguments`]. It is important you don't remove unused columns because this'll drop the `image` column. Without the `image` column, you can't create `pixel_values`. Set `remove_unused_columns=False` to prevent this behavior! The only other required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the accuracy and save the training checkpoint.
+1. Define your training hyperparameters in [`TrainingArguments`]. It is important you don't remove unused columns because that'll drop the `image` column. Without the `image` column, you can't create `pixel_values`. Set `remove_unused_columns=False` to prevent this behavior! The only other required parameter is `output_dir` which specifies where to save your model. You'll push this model to the Hub by setting `push_to_hub=True` (you need to be signed in to Hugging Face to upload your model). At the end of each epoch, the [`Trainer`] will evaluate the accuracy and save the training checkpoint.
 2. Pass the training arguments to [`Trainer`] along with the model, dataset, tokenizer, data collator, and `compute_metrics` function.
 3. Call [`~Trainer.train`] to finetune your model.
 
@@ -343,7 +343,7 @@ If you are unfamiliar with fine-tuning a model with Keras, check out the [basic
 
 To fine-tune a model in TensorFlow, follow these steps:
 1. Define the training hyperparameters, and set up an optimizer and a learning rate schedule.
-2. Instantiate a pre-treined model.
+2. Instantiate a pre-trained model.
 3. Convert a 🤗 Dataset to a `tf.data.Dataset`.
 4. Compile your model.
 5. Add callbacks and use the `fit()` method to run the training.

docs/source/en/tasks/language_modeling.mdx

@@ -306,12 +306,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:

docs/source/en/tasks/masked_language_modeling.mdx

@@ -301,12 +301,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 This can be done by specifying where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:
@@ -376,7 +376,7 @@ Tokenize the text and return the `input_ids` as PyTorch tensors. You'll also nee
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_mlm_model")
+>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
 >>> inputs = tokenizer(text, return_tensors="pt")
 >>> mask_token_index = torch.where(inputs["input_ids"] == tokenizer.mask_token_id)[1]
 ```
@@ -409,7 +409,7 @@ Tokenize the text and return the `input_ids` as TensorFlow tensors. You'll also
 ```py
 >>> from transformers import AutoTokenizer
 
->>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_mlm_model")
+>>> tokenizer = AutoTokenizer.from_pretrained("stevhliu/my_awesome_eli5_mlm_model")
 >>> inputs = tokenizer(text, return_tensors="tf")
 >>> mask_token_index = tf.where(inputs["input_ids"] == tokenizer.mask_token_id)[0, 1]
 ```

docs/source/en/tasks/multiple_choice.mdx

@@ -335,10 +335,10 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/question_answering.mdx

@@ -369,6 +369,7 @@ Tokenize the text and return PyTorch tensors:
 Pass your inputs to the model and return the `logits`:
 
 ```py
+>>> import torch
 >>> from transformers import AutoModelForQuestionAnswering
 
 >>> model = AutoModelForQuestionAnswering.from_pretrained("my_awesome_qa_model")

docs/source/en/tasks/semantic_segmentation.mdx

@@ -28,7 +28,7 @@ The task illustrated in this tutorial is supported by the following model archit
 
 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
 
-[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
+[BEiT](../model_doc/beit), [Data2VecVision](../model_doc/data2vec-vision), [DPT](../model_doc/dpt), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [SegFormer](../model_doc/segformer), [UPerNet](../model_doc/upernet)
 
 <!--End of the generated tip-->
 
@@ -377,7 +377,7 @@ Start by defining the hyperparameters, optimizer and learning rate schedule:
 ```
 
 Then, load SegFormer with [`TFAutoModelForSemanticSegmentation`] along with the label mappings, and compile it with the
-optimizer:
+optimizer. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> from transformers import TFAutoModelForSemanticSegmentation
@@ -387,7 +387,7 @@ optimizer:
 ...     id2label=id2label,
 ...     label2id=label2id,
 ... )
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 Convert your datasets to the `tf.data.Dataset` format using the [`~datasets.Dataset.to_tf_dataset`] and the [`DefaultDataCollator`]:

docs/source/en/tasks/sequence_classification.mdx

@@ -259,12 +259,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the accuracy from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/summarization.mdx

@@ -267,12 +267,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the ROUGE score from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/token_classification.mdx

@@ -361,12 +361,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the seqeval scores from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tasks/translation.mdx

@@ -276,12 +276,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`~transformers.TFPre
 ... )
 ```
 
-Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
+Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method). Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 >>> import tensorflow as tf
 
->>> model.compile(optimizer=optimizer)
+>>> model.compile(optimizer=optimizer)  # No loss argument!
 ```
 
 The last two things to setup before you start training is to compute the SacreBLEU metric from the predictions, and provide a way to push your model to the Hub. Both are done by using [Keras callbacks](../main_classes/keras_callbacks).

docs/source/en/tflite.mdx

@@ -0,0 +1,58 @@
+<!--Copyright 2023 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 TFLite
+
+[TensorFlow Lite](https://www.tensorflow.org/lite/guide) is a lightweight framework for deploying machine learning models 
+on resource-constrained devices, such as mobile phones, embedded systems, and Internet of Things (IoT) devices. 
+TFLite is designed to optimize and run models efficiently on these devices with limited computational power, memory, and 
+power consumption.
+A TensorFlow Lite model is represented in a special efficient portable format identified by the `.tflite` file extension. 
+
+🤗 Optimum offers functionality to export 🤗 Transformers models to TFLite through the `exporters.tflite` module. 
+For the list of supported model architectures, please refer to [🤗 Optimum documentation](https://huggingface.co/docs/optimum/exporters/tflite/overview).
+
+To export a model to TFLite, install the required dependencies:
+ 
+```bash
+pip install optimum[exporters-tf]
+```
+
+To check out all available arguments, refer to the [🤗 Optimum docs](https://huggingface.co/docs/optimum/main/en/exporters/tflite/usage_guides/export_a_model), 
+or view help in command line:
+
+```bash
+optimum-cli export tflite --help
+```
+
+To export a model's checkpoint from the 🤗 Hub, for example, `bert-base-uncased`, run the following command:
+
+```bash
+optimum-cli export tflite --model bert-base-uncased --sequence_length 128 bert_tflite/
+```
+
+You should see the logs indicating progress and showing where the resulting `model.tflite` is saved, like this:
+
+```bash
+Validating TFLite model...
+	-[✓] TFLite model output names match reference model (logits)
+	- Validating TFLite Model output "logits":
+		-[✓] (1, 128, 30522) matches (1, 128, 30522)
+		-[x] values not close enough, max diff: 5.817413330078125e-05 (atol: 1e-05)
+The TensorFlow Lite export succeeded with the warning: The maximum absolute difference between the output of the reference model and the TFLite exported model is not within the set tolerance 1e-05:
+- logits: max diff = 5.817413330078125e-05.
+ The exported model was saved at: bert_tflite
+ ```
+
+The example above illustrates exporting a checkpoint from 🤗 Hub. When exporting a local model, first make sure that you 
+saved both the model's weights and tokenizer files in the same directory (`local_path`). When using CLI, pass the 
+`local_path` to the `model` argument instead of the checkpoint name on 🤗 Hub. 

docs/source/en/tokenizer_summary.mdx

@@ -141,7 +141,7 @@ words. Pretokenization can be as simple as space tokenization, e.g. [GPT-2](mode
 [FlauBERT](model_doc/flaubert) which uses Moses for most languages, or [GPT](model_doc/gpt) which uses
 Spacy and ftfy, to count the frequency of each word in the training corpus.
 
-After pre-tokenization, a set of unique words has been created and the frequency of each word it occurred in the
+After pre-tokenization, a set of unique words has been created and the frequency with which each word occurred in the
 training data has been determined. Next, BPE creates a base vocabulary consisting of all symbols that occur in the set
 of unique words and learns merge rules to form a new symbol from two symbols of the base vocabulary. It does so until
 the vocabulary has attained the desired vocabulary size. Note that the desired vocabulary size is a hyperparameter to

docs/source/en/tools_inference_endpoints.mdx

@@ -1,132 +0,0 @@
-<!--Copyright 2023 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.
--->
-
-# Running tools on inference endpoints
-
-<Tip>
-
-This document is about running tools on inference endpoints so that agents may use these tools remotely. 
-If you do not know what tools and agents are in the context of transformers, we recommend you read the
-[Transformers Agents](transformers_agents) page first.
-
-</Tip>
-
-Agents are designed to use tools in order to respond to a natural language query. They are setup so as to load tools
-locally and use them directly in the runtime they're at.
-
-However, some of these tools can be heavy; tools that handle images, long text, or audio signals may need a 
-significant amount of memory in order to perform inference. Tools that generate images through a diffusion
-process, may require significant compute in order to perform the multiple steps they need; but end users
-may not benefit from the powerful setups required to use them.
-
-This is why we have support for **remote** tools: these have an API that can be called from the runtime, offloading
-the processing to the remote API. In this guide we'll explore how to set up an inference endpoint for a given tool
-to leverage it with the agents.
-
-Inference endpoints are one solution to handle remote tools; but they're not the only one. We integrate with
-[`gradio_tools`](custom_tools#leveraging-gradiotools) that also offers remote tools, and we'll continue adding 
-guides to other alternatives for remote tools.
-
-## Inference Endpoints
-
-
-[Inference Endpoints](https://huggingface.co/inference-endpoints) is a paid Hugging Face solution to easily deploy
-Transformers and Diffusers models on a fully-managed infrastructure. It has default deployment options for
-transformers and diffusers, but given that we're using a specific type of object here, tools, we'll set up a custom
-handler to get it to work.
-
-<Tip warning={true}>
-
-Inference Endpoints are a paid hosting service by Hugging Face, which needs to have an organization setup with 
-billing enabled.
-
-</Tip>
-
-Tools are Spaces by default in Transformers. When calling `push_to_hub` on a tool, you're effectively pushing
-the code to a Space on the Hugging Face Hub under a namespace that you own. There are many tools living on the
-[`huggingface-tools` namespace](https://huggingface.co/huggingface-tools); having them be Spaces by default means
-that users can play around with the tool directly in the browser.
-
-However, Inference Endpoints only work with **model** repositories. We'll therefore have to create a model
-repository to act as a proxy for the Space. That model repository will contain the `handler.py` file to serve
-our tool through an inference endpoint.
-
-For demonstration purposes, we'll consider that you already have a tool handy that you'd like to use remotely. If
-you'd like to setup your custom tool, we recommend reading the [Custom Tool](custom_tools#leveraging-gradiotools) 
-guide.
-
-We'll try and deploy the `huggingface-tools/text-to-video` tool to an inference endpoint. We have it available as 
-a gradio Space [here](https://huggingface.co/huggingface-tools/text-to-video).
-
-### Setting up the repository
-
-We'll start by creating a model repository that will serve as a serving point for this tool.
-It can be public or private; for the sake of this tutorial we'll keep this one public, but having it set to
-private doesn't interfere with the inference endpoint setup.
-
-The repository is created and is available [here](https://huggingface.co/huggingface-tools/text-to-video).
-In it, you'll see there is a custom handler file, called 
-[`handler.py`](https://huggingface.co/huggingface-tools/text-to-video/blob/main/handler.py), as well as a traditional
-requirements file called 
-[`requirements.txt`](https://huggingface.co/huggingface-tools/text-to-video/blob/main/requirements.txt).
-
-#### Handler file
-
-The handler file exposes an `EndpointHandler`, which serves as the link between the requests you'll be doing to the
-remote tool and the tool itself. It should:
-
-- Instantiate the tool in its initialization method
-- Have a `__call__` method which will take the serialized input and return the computed result.
-
-For text-to-text tools, the handler file is very simple; it looks like the following:
-
-```python
-from transformers.tools import load_tool
-
-
-class EndpointHandler:
-    def __init__(self, path=""):
-        self.tool = load_tool("huggingface-tools/text-to-video")
-        self.tool.setup()
-
-    def __call__(self, data):
-        inputs = data.pop("inputs", data)
-        return self.tool(**inputs)
-```
-
-However, it is different if handling different data types as it will need to serialize this data type. 
-This guide will be completed to include different serialization for text, image, audio and video.
-
-#### Requirement file
-
-The requirement file needs to specify all requirements necessary to run the tool. The basic dependencies are the 
-following:
-
-```text
-transformers>=4.29.0
-accelerate
-```
-
-but you may need to include any and all other dependencies needed by your tool
-
-### Spinning up an endpoint
-
-Once we're done creating the repository, we can go ahead and create our first endpoint. Head over to
-[the Inference Endpoints UI](https://ui.endpoints.huggingface.co/endpoints) and create your first endpoint.
-
-If the repository is setup correctly, it should spin up directly without issue.
-
-In case you encounter a "Failed" deployment, we recommend checking out 
-[this guide](https://huggingface.co/docs/inference-endpoints/guides/logs) on checking out the logs of an inference
-endpoint.
-
-TODO add images

docs/source/en/training.mdx

@@ -191,7 +191,7 @@ tokenized_data = dict(tokenized_data)
 labels = np.array(dataset["label"])  # Label is already an array of 0 and 1
 ```
 
-Finally, load, [`compile`](https://keras.io/api/models/model_training_apis/#compile-method), and [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) the model:
+Finally, load, [`compile`](https://keras.io/api/models/model_training_apis/#compile-method), and [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) the model. Note that Transformers models all have a default task-relevant loss function, so you don't need to specify one unless you want to:
 
 ```py
 from transformers import TFAutoModelForSequenceClassification
@@ -200,7 +200,7 @@ 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.compile(optimizer=Adam(3e-5))  # No loss argument!
 
 model.fit(tokenized_data, labels)
 ```
@@ -261,7 +261,7 @@ list of samples into a batch and apply any preprocessing you want. See our
 Once you've created a `tf.data.Dataset`, you can compile and fit the model as before:
 
 ```py
-model.compile(optimizer=Adam(3e-5))
+model.compile(optimizer=Adam(3e-5))  # No loss argument!
 
 model.fit(tf_dataset)
 ```

docs/source/ko/_toctree.yml

@@ -30,8 +30,8 @@
         title: 토큰 분류
       - local: tasks/question_answering
         title: 질의 응답(Question Answering)
-      - local: in_translation
-        title: (번역중) Causal language modeling
+      - local: tasks/language_modeling
+        title: 인과적 언어 모델링(Causal language modeling)
       - local: tasks/masked_language_modeling
         title: 마스킹된 언어 모델링(Masked language modeling)
       - local: tasks/translation
@@ -45,8 +45,8 @@
   - sections:
       - local: in_translation
         title: (번역중) Audio classification
-      - local: in_translation
-        title: (번역중) Automatic speech recognition
+      - local: tasks/asr
+        title: 자동 음성 인식
     title: (번역중) 오디오
     isExpanded: false
   - sections:
@@ -54,16 +54,16 @@
         title: 이미지 분류
       - local: in_translation
         title: (번역중) Semantic segmentation
-      - local: in_translation
-        title: (번역중) Video classification
-      - local: in_translation
-        title: (번역중) Object detection
-      - local: in_translation
-        title: (번역중) Zero-shot object detection
+      - local: tasks/video_classification
+        title: 영상 분류
+      - local: tasks/object_detection
+        title: 객체 탐지
+      - local: tasks/zero_shot_object_detection
+        title: 제로샷(zero-shot) 객체 탐지
       - local: tasks/zero_shot_image_classification
         title: 제로샷(zero-shot) 이미지 분류
-      - local: in_translation
-        title: (번역중) Depth estimation
+      - local: tasks/monocular_depth_estimation
+        title: 단일 영상 기반 깊이 추정
     title: (번역중) 컴퓨터 비전
     isExpanded: false
   - sections:
@@ -75,8 +75,8 @@
     isExpanded: false
   title: 태스크 가이드
 - sections:
-    - local: in_translation
-      title: (번역중) Use fast tokenizers from 🤗 Tokenizers
+    - local: fast_tokenizers
+      title: 🤗 Tokenizers 라이브러리에서 토크나이저 사용하기
     - local: multilingual
       title: 다국어 모델 추론하기
     - local: in_translation
@@ -97,8 +97,8 @@
       title: (번역중) Notebooks with examples
     - local: in_translation
       title: (번역중) Community resources
-    - local: in_translation
-      title: (번역중) Troubleshoot
+    - local: troubleshooting
+      title: 문제 해결
   title: (번역중) 개발자 가이드
 - sections:
     - local: in_translation
@@ -156,20 +156,20 @@
     title: (번역중) Philosophy
   - local: in_translation
     title: (번역중) Glossary
-  - local: in_translation
-    title: (번역중) What 🤗 Transformers can do
-  - local: in_translation
-    title: (번역중) How 🤗 Transformers solve tasks
+  - local: task_summary
+    title: 🤗 Transformers로 할 수 있는 작업
+  - local: tasks_explained
+    title: 🤗 Transformers로 작업을 해결하는 방법
   - local: in_translation
     title: (번역중) The Transformer model family
   - local: in_translation
     title: (번역중) Summary of the tokenizers
-  - local: in_translation
-    title: (번역중) Attention mechanisms
-  - local: in_translation
-    title: (번역중) Padding and truncation
-  - local: in_translation
-    title: (번역중) BERTology
+  - local: attention
+    title: 어텐션 매커니즘
+  - local: pad_truncation
+    title: 패딩과 잘라내기
+  - local: bertology
+    title: BERTology
   - local: in_translation
     title: (번역중) Perplexity of fixed-length models
   - local: in_translation
@@ -673,4 +673,4 @@
     - local: in_translation
       title: (번역중) Utilities for Time Series
     title: (번역중) Internal Helpers
-  title: (번역중) API
+  title: (번역중) API

docs/source/ko/attention.mdx

@@ -0,0 +1,50 @@
+<!--Copyright 2023 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.
+-->
+
+# 어텐션 메커니즘[[attention_mechanisms]]
+
+대부분의 트랜스포머 모델은 정방행렬인 전체 어텐션을 사용합니다. 
+하지만 이는 긴 텍스트를 다룰 때는 큰 계산 병목 현상을 유발할 수 있습니다. 
+`Longformer`와 `Reformer`는 훈련 속도를 높이기 위해 어텐션 행렬의 희소 버전을 사용하여 효율을 높이려는 모델입니다.
+
+## LSH 어텐션[[lsh_attention]]
+
+
+[Reformer](#reformer)는 LSH(Locality Sensitive Hashing) 어텐션을 사용합니다. softmax(QK^t)에서는 행렬 QK^t의 (softmax 차원에서) 가장 큰 요소들만 유용한 기여를 할 것입니다. 
+따라서 각각의 쿼리 q에 대해, q와 가까운 키 k만 고려할 수 있습니다. 해시 함수는 q와 k가 가까운지 여부를 결정하는 데 사용됩니다. 
+어텐션 마스크는 현재 토큰을 마스킹하여 변경됩니다. 이 때 첫 번째 위치의 토큰은 제외합니다. 왜냐하면 쿼리와 키가 동일한 값을 갖게 되기 때문입니다(서로 매우 유사함). 
+해시는 약간의 무작위성을 가질 수 있으므로, 실제로는 여러 개의 해시 함수가 사용되고 (`n_rounds` 매개변수에 의해 결정됨) 그 후에 평균값을 취하게 됩니다.
+
+## 지역 어텐션[[local_attention]]
+
+[Longformer](#longformer)는 지역 어텐션을 사용합니다. 종종 특정 토큰에 대해 지역 컨텍스트(예: 왼쪽과 오른쪽에 있는 두 개의 토큰은 무엇인가요?)만으로도 작업을 수행하는데 충분합니다. 
+또한 작은 창(window)을 가진 어텐션 레이어를 쌓음으로써 마지막 레이어는 창 내의 토큰뿐만 아니라 더 많은 수의 토큰에 대한 수용 영역(receptive field)을 갖게 되어 전체 문장의 표현을 구축할 수 있습니다.
+
+사전에 선택된 일부 입력 토큰들은 전역 어텐션을 받습니다. 이 몇 개의 토큰에 대해서는 어텐션 행렬이 모든 토큰에 접근할 수 있으며, 이 과정은 대칭적으로 이루어집니다. 
+다른 모든 토큰들은 로컬 창 내의 토큰들에 더해 해당 특정 토큰들에도 접근할 수 있습니다. 이는 논문의 Figure 2d에서 나타나며, 아래에 샘플 어텐션 마스크가 제시되어 있습니다:
+
+
+<div class="flex justify-center">
+    <img scale="50 %" align="center" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/local_attention_mask.png"/>
+</div>
+
+
+적은 파라미터의 어텐션 행렬을 사용하면 모델이 더 큰 시퀀스 입력 길이를 가질 수 있습니다.
+
+## 다른 방법들[[other_tricks]]
+
+### 축별 위치 인코딩[[axial_positional_encodings]]
+
+[Reformer](#reformer)는 축별 위치 인코딩(axial positional encodings)을 사용합니다. 기존의 트랜스포머 모델에서는 위치 인코딩 행렬 E는 크기가 \\(l \times d\\)인 행렬이며, 
+여기서 \\(l\\)은 시퀀스 길이(sequence length)이고 \\(d\\)는 숨겨진 상태(hidden state)의 차원입니다. 매우 긴 텍스트의 경우, 이 행렬은 매우 크며 GPU 상에서 공간을 많이 차지할 수 있습니다. 
+이를 완화하기 위해, 축별 위치 인코딩은 큰 행렬 E를 두 개의 작은 행렬 E1과 E2로 분해합니다. 이때 E1의 크기는 \\(l_{1} \times d_{1}\\)이고, E2의 크기는 \\(l_{2} \times d_{2}\\)입니다. 
+이때 \\(l_{1} \times l_{2} = l\\)이고 \\(d_{1} + d_{2} = d\\)(길이에 대한 곱셈 연산을 사용하면 훨씬 작아집니다). E의 시간 단계 j에 대한 임베딩은 E1에서 시간 단계 \\(j \% l1\\)의 임베딩과 E2에서 시간 단계  \\(j // l1\\)의 임베딩을 연결하여 얻습니다.

docs/source/ko/bertology.mdx

@@ -0,0 +1,37 @@
+<!--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.
+-->
+
+# BERTology
+
+BERT와 같은 대규모 트랜스포머의 내부 동작을 조사하는 연구 분야가 점점 더 중요해지고 있습니다.
+혹자는 "BERTology"라 칭하기도 합니다. 이 분야의 좋은 예시는 다음과 같습니다:
+
+
+- BERT는 고전적인 NLP 파이프라인의 재발견 - Ian Tenney, Dipanjan Das, Ellie Pavlick:
+  https://arxiv.org/abs/1905.05950
+- 16개의 헤드가 정말로 1개보다 나은가? - Paul Michel, Omer Levy, Graham Neubig:
+  https://arxiv.org/abs/1905.10650
+- BERT는 무엇을 보는가? BERT의 어텐션 분석 - Kevin Clark, Urvashi Khandelwal, Omer Levy, Christopher D. Manning:
+  https://arxiv.org/abs/1906.04341
+- CAT-probing: 프로그래밍 언어에 대해 사전훈련된 모델이 어떻게 코드 구조를 보는지 알아보기 위한 메트릭 기반 접근 방법:
+  https://arxiv.org/abs/2210.04633
+
+우리는 이 새로운 연구 분야의 발전을 돕기 위해, BERT/GPT/GPT-2 모델에 내부 표현을 살펴볼 수 있는 몇 가지 기능을 추가했습니다.
+이 기능들은 주로 Paul Michel의 훌륭한 작업을 참고하여 개발되었습니다
+(https://arxiv.org/abs/1905.10650):
+
+
+- BERT/GPT/GPT-2의 모든 은닉 상태에 접근하기,
+- BERT/GPT/GPT-2의 각 헤드의 모든 어텐션 가중치에 접근하기,
+- 헤드의 출력 값과 그래디언트를 검색하여 헤드 중요도 점수를 계산하고 https://arxiv.org/abs/1905.10650에서 설명된 대로 헤드를 제거하는 기능을 제공합니다.
+
+이러한 기능들을 이해하고 직접 사용해볼 수 있도록 [bertology.py](https://github.com/huggingface/transformers/tree/main/examples/research_projects/bertology/run_bertology.py) 예제 스크립트를 추가했습니다. 이 예제 스크립트에서는 GLUE에 대해 사전훈련된 모델에서 정보를 추출하고 모델을 가지치기(prune)해봅니다.

docs/source/ko/fast_tokenizers.mdx

@@ -0,0 +1,67 @@
+<!--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.
+-->
+
+# 🤗 Tokenizers 라이브러리의 토크나이저 사용하기[[use-tokenizers-from-tokenizers]]
+
+[`PreTrainedTokenizerFast`]는 [🤗 Tokenizers](https://huggingface.co/docs/tokenizers) 라이브러리에 기반합니다. 🤗 Tokenizers 라이브러리의 토크나이저는
+🤗 Transformers로 매우 간단하게 불러올 수 있습니다.
+
+구체적인 내용에 들어가기 전에, 몇 줄의 코드로 더미 토크나이저를 만들어 보겠습니다:
+
+```python
+>>> from tokenizers import Tokenizer
+>>> from tokenizers.models import BPE
+>>> from tokenizers.trainers import BpeTrainer
+>>> from tokenizers.pre_tokenizers import Whitespace
+
+>>> tokenizer = Tokenizer(BPE(unk_token="[UNK]"))
+>>> trainer = BpeTrainer(special_tokens=["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]"])
+
+>>> tokenizer.pre_tokenizer = Whitespace()
+>>> files = [...]
+>>> tokenizer.train(files, trainer)
+```
+
+우리가 정의한 파일을 통해 이제 학습된 토크나이저를 갖게 되었습니다. 이 런타임에서 계속 사용하거나 JSON 파일로 저장하여 나중에 사용할 수 있습니다.
+
+## 토크나이저 객체로부터 직접 불러오기[[loading-directly-from-the-tokenizer-object]]
+
+🤗 Transformers 라이브러리에서 이 토크나이저 객체를 활용하는 방법을 살펴보겠습니다.
+[`PreTrainedTokenizerFast`] 클래스는 인스턴스화된 *토크나이저* 객체를 인수로 받아 쉽게 인스턴스화할 수 있습니다:
+
+```python
+>>> from transformers import PreTrainedTokenizerFast
+
+>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
+```
+
+이제 `fast_tokenizer` 객체는 🤗 Transformers 토크나이저에서 공유하는 모든 메소드와 함께 사용할 수 있습니다! 자세한 내용은 [토크나이저 페이지](main_classes/tokenizer)를 참조하세요.
+
+## JSON 파일에서 불러오기[[loading-from-a-JSON-file]]
+
+<!--In order to load a tokenizer from a JSON file, let's first start by saving our tokenizer:-->
+
+JSON 파일에서 토크나이저를 불러오기 위해, 먼저 토크나이저를 저장해 보겠습니다:
+
+```python
+>>> tokenizer.save("tokenizer.json")
+```
+
+JSON 파일을 저장한 경로는 `tokenizer_file` 매개변수를 사용하여 [`PreTrainedTokenizerFast`] 초기화 메소드에 전달할 수 있습니다:
+
+```python
+>>> from transformers import PreTrainedTokenizerFast
+
+>>> fast_tokenizer = PreTrainedTokenizerFast(tokenizer_file="tokenizer.json")
+```
+
+이제 `fast_tokenizer` 객체는 🤗 Transformers 토크나이저에서 공유하는 모든 메소드와 함께 사용할 수 있습니다! 자세한 내용은 [토크나이저 페이지](main_classes/tokenizer)를 참조하세요.

docs/source/ko/pad_truncation.mdx

@@ -0,0 +1,64 @@
+<!--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.
+-->
+
+# 패딩과 잘라내기[[padding-and-truncation]]
+
+배치 입력은 길이가 다른 경우가 많아서 고정 크기 텐서로 변환할 수 없습니다. 패딩과 잘라내기는 다양한 길이의 배치에서 직사각형 텐서를 생성할 수 있도록 이 문제를 해결하는 전략입니다. 패딩은 특수한 **패딩 토큰**을 추가하여 짧은 시퀀스가 배치에서 가장 긴 시퀀스 또는 모델에서 허용하는 최대 길이와 동일한 길이를 갖도록 합니다. 잘라내기는 긴 시퀀스를 잘라내어 패딩과 다른 방식으로 시퀀스의 길이를 동일하게 합니다.
+
+대부분의 경우 배치에 가장 긴 시퀀스의 길이로 패딩하고 모델이 허용할 수 있는 최대 길이로 잘라내는 것이 잘 작동합니다. 그러나 필요하다면 API가 지원하는 더 많은 전략을 사용할 수 있습니다. 필요한 인수는 `padding`, `truncation`, `max_length` 세 가지입니다.
+
+`padding` 인수는 패딩을 제어합니다. 불리언 또는 문자열일 수 있습니다:
+
+  - `True` 또는 `'longest'`: 배치에서 가장 긴 시퀀스로 패딩합니다(단일 시퀀스만 제공하는 경우 패딩이 적용되지 않습니다).
+  - `'max_length'`: `max_length` 인수가 지정한 길이로 패딩하거나, `max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 패딩합니다. 단일 시퀀스만 제공하는 경우에도 패딩이 적용됩니다.
+  - `False` 또는 `'do_not_pad'`: 패딩이 적용되지 않습니다. 이것이 기본 동작입니다.
+
+`truncation` 인수는 잘라낼 방법을 정합니다. 불리언 또는 문자열일 수 있습니다:
+
+  - `True` 또는 `longest_first`: `max_length` 인수가 지정한 최대 길이로 잘라내거나, 
+    `max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 잘라냅니다. 
+    시퀀스 쌍에서 가장 긴 시퀀스의 토큰을 적절한 길이에 도달할 때까지 하나씩 제거합니다.
+  - `'only_second'`: `max_length` 인수가 지정한 최대 길이로 잘라내거나, 
+    `max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 잘라냅니다.
+    시퀀스 쌍(또는 시퀀스 쌍의 배치)가 제공된 경우 쌍의 두 번째 문장만 잘라냅니다.
+  - `'only_first'`: `max_length` 인수가 지정한 최대 길이로 잘라내거나, 
+    `max_length`가 제공되지 않은 경우(`max_length=None`) 모델에서 허용되는 최대 길이로 잘라냅니다. 
+    시퀀스 쌍(또는 시퀀스 쌍의 배치)가 제공된 경우 쌍의 첫 번째 문장만 잘라냅니다.
+  - `False` 또는 `'do_not_truncate'`: 잘라내기를 적용하지 않습니다. 이것이 기본 동작입니다.
+
+`max_length` 인수는 패딩 및 잘라내기를 적용할 길이를 제어합니다. 이 인수는 정수 또는 `None`일 수 있으며, `None`일 경우 모델이 허용할 수 있는 최대 길이로 기본값이 설정됩니다. 모델에 특정한 최대 입력 길이가 없는 경우 `max_length`에 대한 잘라내기 또는 패딩이 비활성화됩니다.
+
+다음 표에는 패딩 및 잘라내기를 설정하는 권장 방법이 요약되어 있습니다. 
+입력으로 시퀀스 쌍을 사용하는 경우, 다음 예제에서 `truncation=True`를 `['only_first', 'only_second', 'longest_first']`에서 선택한 `STRATEGY`, 즉 `truncation='only_second'` 또는 `truncation='longest_first'`로 바꾸면 앞서 설명한 대로 쌍의 두 시퀀스가 잘리는 방식을 제어할 수 있습니다.
+
+| 잘라내기                             | 패딩                              | 사용 방법                                                                                 |
+|--------------------------------------|-----------------------------------|------------------------------------------------------------------------------------------|
+| 잘라내기 없음                        | 패딩 없음                          | `tokenizer(batch_sentences)`                                                             |
+|                                      | 배치 내 최대 길이로 패딩           | `tokenizer(batch_sentences, padding=True)` 또는                                          |
+|                                      |                                   | `tokenizer(batch_sentences, padding='longest')`                                          |
+|                                      | 모델의 최대 입력 길이로 패딩      | `tokenizer(batch_sentences, padding='max_length')`                                        |
+|                                      | 특정 길이로 패딩                  | `tokenizer(batch_sentences, padding='max_length', max_length=42)`                         |
+|                                      | 다양한 길이로 패딩                | `tokenizer(batch_sentences, padding=True, pad_to_multiple_of=8)                           |
+| 모델의 최대 입력 길이로 잘라내기      | 패딩 없음                         | `tokenizer(batch_sentences, truncation=True)` 또는                                        |
+|                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY)`                                         |
+|                                      | 배치 내 최대 길이로 패딩          | `tokenizer(batch_sentences, padding=True, truncation=True)` 또는                          |
+|                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY)`                           |
+|                                      | 모델의 최대 입력 길이로 패딩      | `tokenizer(batch_sentences, padding='max_length', truncation=True)` 또는                  |
+|                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY)`                   |
+|                                      | 특정 길이로 패딩                  | 사용 불가                                                                              |
+| 특정 길이로 잘라내기                 | 패딩 없음                         | `tokenizer(batch_sentences, truncation=True, max_length=42)` 또는                         |
+|                                      |                                   | `tokenizer(batch_sentences, truncation=STRATEGY, max_length=42)`                         |
+|                                      | 배치 내 최대 길이로 패딩          | `tokenizer(batch_sentences, padding=True, truncation=True, max_length=42)` 또는           |
+|                                      |                                   | `tokenizer(batch_sentences, padding=True, truncation=STRATEGY, max_length=42)`           |
+|                                      | 모델의 최대 입력 길이로 패딩       | 사용 불가                                                                             |
+|                                      | 특정 길이로 패딩                   | `tokenizer(batch_sentences, padding='max_length', truncation=True, max_length=42)` 또는  |
+|                                      |                                   | `tokenizer(batch_sentences, padding='max_length', truncation=STRATEGY, max_length=42)`   |

docs/source/ko/preprocessing.mdx

@@ -14,16 +14,16 @@ specific language governing permissions and limitations under the License.
 
 [[open-in-colab]]
 
-모델을 학습하려면 데이터셋을 모델에 맞는 입력 형식으로 전처리 해야 합니다. 데이터가 텍스트, 이미지 또는 오디오인지 여부에 관계없이 데이터를 텐서 배치로 변환하고 조립할 필요가 있습니다. 🤗 Transformers는 모델에 대한 데이터를 준비하는 데 도움이 되는 일련의 전처리 클래스를 제공합니다. 이 튜토리얼에서는 다음 내용을 배울 수 있습니다.
+모델을 훈련하려면 데이터 세트를 모델에 맞는 입력 형식으로 전처리해야 합니다. 텍스트, 이미지 또는 오디오인지 관계없이 데이터를 텐서 배치로 변환하고 조립할 필요가 있습니다. 🤗 Transformers는 모델에 대한 데이터를 준비하는 데 도움이 되는 일련의 전처리 클래스를 제공합니다. 이 튜토리얼에서는 다음 내용을 배울 수 있습니다:
 
-* 텍스트는 [Tokenizer](./main_classes/tokenizer)를 사용하여 텍스트를 토큰 시퀀스로 변환하고 토큰의 숫자 표현을 만든 후 텐서로 조립합니다.
+* 텍스트는 [Tokenizer](./main_classes/tokenizer)를 사용하여 토큰 시퀀스로 변환하고 토큰의 숫자 표현을 만든 후 텐서로 조립합니다.
 * 음성 및 오디오는 [Feature extractor](./main_classes/feature_extractor)를 사용하여 오디오 파형에서 시퀀스 특성을 파악하여 텐서로 변환합니다.
 * 이미지 입력은 [ImageProcessor](./main_classes/image)을 사용하여 이미지를 텐서로 변환합니다.
 * 멀티모달 입력은 [Processor](./main_classes/processors)을 사용하여 토크나이저와 특성 추출기 또는 이미지 프로세서를 결합합니다.
 
 <Tip>
 
-`AutoProcessor`는 **항상** 작동하며 토크나이저, 이미지 프로세서, 특성 추출기 또는 프로세서 등 사용 중인 모델에 맞는 클래스를 자동으로 선택합니다.
+`AutoProcessor`는 **언제나** 작동하여 토크나이저, 이미지 프로세서, 특성 추출기 또는 프로세서 등 사용 중인 모델에 맞는 클래스를 자동으로 선택합니다.
 
 </Tip>
 
@@ -41,11 +41,11 @@ pip install datasets
 
 <Tip>
 
-사전 훈련된 모델을 사용할 계획이라면 모델과 함께 사전 훈련된 토크나이저를 사용하는 것이 중요합니다. 이렇게 하면 텍스트가 사전 훈련 말뭉치와 동일한 방식으로 분할되고 사전 훈련 중에 동일한 해당 토큰-인덱스 쌍(일반적으로 *vocab*이라고 함)을 사용합니다.
+사전훈련된 모델을 사용할 계획이라면 모델과 함께 사전훈련된 토크나이저를 사용하는 것이 중요합니다. 이렇게 하면 텍스트가 사전훈련 말뭉치와 동일한 방식으로 분할되고 사전훈련 중에 동일한 해당 토큰-인덱스 쌍(일반적으로 *vocab*이라고 함)을 사용합니다.
 
 </Tip>
 
-시작하려면 [`AutoTokenizer.from_pretrained`] 메소드를 사용하여 사전 훈련된 토크나이저를 불러오세요. 모델과 함께 사전 훈련된 *vocab*을 다운로드합니다:
+시작하려면 [`AutoTokenizer.from_pretrained`] 메소드를 사용하여 사전훈련된 토크나이저를 불러오세요. 모델과 함께 사전훈련된 *vocab*을 다운로드합니다:
 
 ```py
 >>> from transformers import AutoTokenizer
@@ -63,7 +63,7 @@ pip install datasets
  'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}
 ```
 
-토크나이저는 세 가지 중요한 항목을 포함한 사전을 반환합니다:
+토크나이저는 세 가지 중요한 항목을 포함한 딕셔너리를 반환합니다:
 
 * [input_ids](glossary#input-ids)는 문장의 각 토큰에 해당하는 인덱스입니다.
 * [attention_mask](glossary#attention-mask)는 토큰을 처리해야 하는지 여부를 나타냅니다.
@@ -76,10 +76,10 @@ pip install datasets
 '[CLS] Do not meddle in the affairs of wizards, for they are subtle and quick to anger. [SEP]'
 ```
 
-토크나이저가 두 개의 특수한 토큰(분류 토큰 CLS와 분할 토큰 SEP)을 문장에 추가했습니다.
-모든 모델에 특수한 토큰이 필요한 것은 아니지만, 필요한 경우 토크나이저가 자동으로 추가합니다.
+토크나이저가 두 개의 특수한 토큰(분류 토큰 `CLS`와 분할 토큰 `SEP`)을 문장에 추가했습니다.
+모든 모델에 특수한 토큰이 필요한 것은 아니지만, 필요하다면 토크나이저가 자동으로 추가합니다.
 
-전처리할 문장이 여러 개 있는 경우 이를 리스트로 토크나이저에 전달합니다:
+전처리할 문장이 여러 개 있는 경우에는 리스트로 토크나이저에 전달합니다:
 
 ```py
 >>> batch_sentences = [
@@ -102,9 +102,9 @@ pip install datasets
 
 ### 패딩[[pad]]
 
-모델 입력인 텐서는 균일한 모양을 가져야 하는데, 문장의 길이가 항상 같지 않아서 문제가 될 수 있습니다. 패딩은 짧은 문장에 특수한 *패딩 토큰*을 추가하여 텐서가 직사각형 모양이 되도록 하는 전략입니다.
+모델 입력인 텐서는 모양이 균일해야 하지만, 문장의 길이가 항상 같지는 않기 때문에 문제가 될 수 있습니다. 패딩은 짧은 문장에 특수한 *패딩 토큰*을 추가하여 텐서를 직사각형 모양이 되도록 하는 전략입니다.
 
-`padding` 매개변수를 `True`로 설정하여 배치의 짧은 시퀀스를 가장 긴 시퀀스와 일치하도록 패딩합니다.
+`padding` 매개변수를 `True`로 설정하여 배치 내의 짧은 시퀀스를 가장 긴 시퀀스에 맞춰 패딩합니다.
 
 ```py
 >>> batch_sentences = [
@@ -125,11 +125,11 @@ pip install datasets
                     [1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0]]}
 ```
 
-길이가 짧은 첫 문장과 세 번째 문장은 이제 `0`으로 채워집니다.
+길이가 짧은 첫 문장과 세 번째 문장이 이제 `0`으로 채워졌습니다.
 
-### 생략[[truncation]]
+### 잘라내기[[truncation]]
 
-한편, 때로는 시퀀스가 모델에서 처리하기에 너무 길 수도 있습니다. 이 경우, 시퀀스를 더 짧은 길이로 줄일 필요가 있습니다.
+한편, 때로는 시퀀스가 모델에서 처리하기에 너무 길 수도 있습니다. 이 경우, 시퀀스를 더 짧게 줄일 필요가 있습니다.
 
 모델에서 허용하는 최대 길이로 시퀀스를 자르려면 `truncation` 매개변수를 `True`로 설정하세요:
 
@@ -154,7 +154,7 @@ pip install datasets
 
 <Tip>
 
-다양한 패딩 및 생략 인수에 대해 더 알아보려면 [Padding and truncation](./pad_truncation) 개념 가이드를 확인해보세요.
+다양한 패딩과 잘라내기 인수에 대해 더 알아보려면 [패딩과 잘라내기](./pad_truncation) 개념 가이드를 확인해보세요.
 
 </Tip>
 
@@ -214,9 +214,9 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 ## 오디오[[audio]]
 
-오디오 작업에는 데이터셋을 모델에 준비하기 위해 [특성 추출기](main_classes/feature_extractor)가 필요합니다. 특성 추출기는 원시 오디오 데이터에서 특성를 추출하고 이를 텐서로 변환하는 것이 목적입니다.
+오디오 작업은 모델에 맞는 데이터 세트를 준비하기 위해 [특성 추출기](main_classes/feature_extractor)가 필요합니다. 특성 추출기는 원시 오디오 데이터에서 특성를 추출하고 이를 텐서로 변환하는 것이 목적입니다.
 
-오디오 데이터셋에 특성 추출기를 사용하는 방법을 보려면 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 데이터셋을 가져오세요. (데이터셋을 가져오는 방법은 🤗 [데이터셋 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)에서 자세히 설명하고 있습니다.)
+오디오 데이터 세트에 특성 추출기를 사용하는 방법을 보기 위해 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 데이터 세트를 가져오세요. (데이터 세트를 가져오는 방법은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)에서 자세히 설명하고 있습니다.)
 
 ```py
 >>> from datasets import load_dataset, Audio
@@ -240,8 +240,8 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 * `path`는 오디오 파일의 위치를 가리킵니다.
 * `sampling_rate`는 음성 신호에서 초당 측정되는 데이터 포인트 수를 나타냅니다.
 
-이 튜토리얼에서는 [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) 모델을 사용합니다. 모델 카드를 보면 Wav2Vec2가 16kHz 샘플링된 음성 오디오를 기반으로 사전 학습된 것을 알 수 있습니다. 
-모델을 사전 학습하는 데 사용된 데이터셋의 샘플링 레이트와 오디오 데이터의 샘플링 레이트가 일치해야 합니다. 데이터의 샘플링 레이트가 다르면 데이터를 리샘플링해야 합니다.
+이 튜토리얼에서는 [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base) 모델을 사용합니다. 모델 카드를 보면 Wav2Vec2가 16kHz 샘플링된 음성 오디오를 기반으로 사전훈련된 것을 알 수 있습니다. 
+모델을 사전훈련하는 데 사용된 데이터 세트의 샘플링 레이트와 오디오 데이터의 샘플링 레이트가 일치해야 합니다. 데이터의 샘플링 레이트가 다르면 데이터를 리샘플링해야 합니다.
 
 1. 🤗 Datasets의 [`~datasets.Dataset.cast_column`] 메소드를 사용하여 샘플링 레이트를 16kHz로 업샘플링하세요:
 
@@ -259,8 +259,8 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
  'sampling_rate': 16000}
 ```
 
-다음으로, 입력을 정규화하고 패딩하는 특성 추출기를 가져오세요. 텍스트 데이터의 경우, 더 짧은 시퀀스에 대해 `0`이 추가됩니다. 오디오 데이터에도 같은 개념이 적용됩니다. 
-특성 추출기는 배열에 대해 `0`(묵음으로 해석)을 추가합니다.
+다음으로, 입력을 정규화하고 패딩할 특성 추출기를 가져오세요. 텍스트 데이터의 경우, 더 짧은 시퀀스에 대해 `0`이 추가됩니다. 오디오 데이터에도 같은 개념이 적용됩니다. 
+특성 추출기는 배열에 `0`(묵음으로 해석)을 추가합니다.
 
 [`AutoFeatureExtractor.from_pretrained`]를 사용하여 특성 추출기를 가져오세요:
 
@@ -270,7 +270,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
 ```
 
-오디오 `array`를 특성 추출기에 전달하세요. 또한, 특성 추출기에 `sampling_rate` 인수를 추가하여 발생할 수 있는 조용한 오류(silent errors)를 더 잘 디버깅하는 것을 권장합니다.
+오디오 `array`를 특성 추출기에 전달하세요. 또한, 발생할 수 있는 조용한 오류(silent errors)를 더 잘 디버깅할 수 있도록 특성 추출기에 `sampling_rate` 인수를 추가하는 것을 권장합니다.
 
 ```py
 >>> audio_input = [dataset[0]["audio"]["array"]]
@@ -279,7 +279,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
         5.6335266e-04,  4.6588284e-06, -1.7142107e-04], dtype=float32)]}
 ```
 
-토크나이저와 마찬가지로 배치 내에서 가변적인 시퀀스를 처리하기 위해 패딩 또는 생략을 적용할 수 있습니다. 이 두 개의 오디오 샘플의 시퀀스 길이를 확인해보세요:
+토크나이저와 마찬가지로 배치 내에서 가변적인 시퀀스를 처리하기 위해 패딩 또는 잘라내기를 적용할 수 있습니다. 이 두 개의 오디오 샘플의 시퀀스 길이를 확인해보세요:
 
 ```py
 >>> dataset[0]["audio"]["array"].shape
@@ -289,7 +289,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 (106496,)
 ```
 
-오디오 샘플의 길이가 동일하도록 데이터셋을 전처리하는 함수를 만들어 보세요. 최대 샘플 길이를 지정하면, 특성 추출기가 해당 길이에 맞춰 시퀀스를 패딩하거나 생략합니다:
+오디오 샘플의 길이가 동일하도록 데이터 세트를 전처리하는 함수를 만드세요. 최대 샘플 길이를 지정하면 특성 추출기가 해당 길이에 맞춰 시퀀스를 패딩하거나 잘라냅니다:
 
 ```py
 >>> def preprocess_function(examples):
@@ -304,13 +304,13 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 ...     return inputs
 ```
 
-`preprocess_function`을 데이터셋의 처음 몇 가지 예제에 적용해보세요:
+`preprocess_function`을 데이터 세트의 처음 예시 몇 개에 적용해보세요:
 
 ```py
 >>> processed_dataset = preprocess_function(dataset[:5])
 ```
 
-이제 샘플 길이가 모두 같고 지정된 최대 길이에 맞게 되었습니다. 드디어 전처리된 데이터셋을 모델에 전달할 수 있습니다!
+이제 샘플 길이가 모두 같고 지정된 최대 길이에 맞게 되었습니다. 드디어 전처리된 데이터 세트를 모델에 전달할 수 있습니다!
 
 ```py
 >>> processed_dataset["input_values"][0].shape
@@ -322,7 +322,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 ## 컴퓨터 비전[[computer-vision]]
 
-컴퓨터 비전 작업의 경우, 모델에 대한 데이터셋을 준비하기 위해 [이미지 프로세서](main_classes/image_processor)가 필요합니다.
+컴퓨터 비전 작업의 경우, 모델에 대한 데이터 세트를 준비하기 위해 [이미지 프로세서](main_classes/image_processor)가 필요합니다.
 이미지 전처리는 이미지를 모델이 예상하는 입력으로 변환하는 여러 단계로 이루어집니다. 
 이러한 단계에는 크기 조정, 정규화, 색상 채널 보정, 이미지의 텐서 변환 등이 포함됩니다.
 
@@ -331,22 +331,22 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 이미지 전처리는 이미지 증강 기법을 몇 가지 적용한 뒤에 할 수도 있습니다.
 이미지 전처리 및 이미지 증강은 모두 이미지 데이터를 변형하지만, 서로 다른 목적을 가지고 있습니다:
 
-* 이미지 증강은 과적합(over-fitting)을 방지하고 모델의 견고성(resiliency)을 높이는 데 도움이 되는 방식으로 이미지를 수정합니다. 
+* 이미지 증강은 과적합(over-fitting)을 방지하고 모델의 견고함(resiliency)을 높이는 데 도움이 되는 방식으로 이미지를 수정합니다. 
 밝기와 색상 조정, 자르기, 회전, 크기 조정, 확대/축소 등 다양한 방법으로 데이터를 증강할 수 있습니다. 
 그러나 증강으로 이미지의 의미가 바뀌지 않도록 주의해야 합니다.
 * 이미지 전처리는 이미지가 모델이 예상하는 입력 형식과 일치하도록 보장합니다. 
 컴퓨터 비전 모델을 미세 조정할 때 이미지는 모델이 초기에 훈련될 때와 정확히 같은 방식으로 전처리되어야 합니다.
 
-이미지 증강에는 원하는 라이브러리를 사용할 수 있습니다. 이미지 전처리에는 모델과 연결된 `ImageProcessor`를 사용합니다.
+이미지 증강에는 원하는 라이브러리를 무엇이든 사용할 수 있습니다. 이미지 전처리에는 모델과 연결된 `ImageProcessor`를 사용합니다.
 
 </Tip>
 
-[food101](https://huggingface.co/datasets/food101) 데이터셋을 가져와서 컴퓨터 비전 데이터셋에서 이미지 프로세서를 어떻게 사용하는지 알아보세요. 
-데이터셋 불러오는 방법은 🤗 [데이터셋 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)를 참고하세요.
+[food101](https://huggingface.co/datasets/food101) 데이터 세트를 가져와서 컴퓨터 비전 데이터 세트에서 이미지 프로세서를 어떻게 사용하는지 알아보세요. 
+데이터 세트를 불러오는 방법은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)을 참고하세요.
 
 <Tip>
 
-데이터셋이 상당히 크기 때문에 🤗 Datasets의 `split` 매개변수를 사용하여 학습 분할에서 작은 샘플만 가져오세요!
+데이터 세트가 상당히 크기 때문에 🤗 Datasets의 `split` 매개변수를 사용하여 훈련 세트에서 작은 샘플만 가져오세요!
 
 </Tip>
 
@@ -356,7 +356,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 >>> dataset = load_dataset("food101", split="train[:100]")
 ```
 
-다음으로, 🤗 Datasets의 [`image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image) 기능으로 이미지를 확인해보세요:
+다음으로, 🤗 Datasets의 [`image`](https://huggingface.co/docs/datasets/package_reference/main_classes.html?highlight=image#datasets.Image)로 이미지를 확인해보세요:
 
 ```py
 >>> dataset[0]["image"]
@@ -375,9 +375,9 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 ```
 
 먼저 이미지 증강 단계를 추가해 봅시다. 아무 라이브러리나 사용해도 괜찮지만, 이번 튜토리얼에서는 torchvision의 [`transforms`](https://pytorch.org/vision/stable/transforms.html) 모듈을 사용하겠습니다.
-다른 데이터 증강 라이브러리를 사용하는 방법이 알고 싶다면, [Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) 또는 [Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)에서 배울 수 있습니다.
+다른 데이터 증강 라이브러리를 사용해보고 싶다면, [Albumentations](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_albumentations.ipynb) 또는 [Kornia notebooks](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification_kornia.ipynb)에서 어떻게 사용하는지 배울 수 있습니다.
 
-1. [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html)로  [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html)와 [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) 등의 변환을 몇 가지 연결하세요.
+1. [`Compose`](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html)로  [`RandomResizedCrop`](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html)와 [`ColorJitter`](https://pytorch.org/vision/main/generated/torchvision.transforms.ColorJitter.html) 등 변환을 몇 가지 연결하세요.
 참고로 크기 조정에 필요한 이미지의 크기 요구사항은 `image_processor`에서 가져올 수 있습니다. 
 일부 모델은 정확한 높이와 너비를 요구하지만, 제일 짧은 변의 길이(`shortest_edge`)만 정의된 모델도 있습니다.
 
@@ -407,8 +407,8 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 <Tip>
 
 위의 예에서는 이미지 증강 중에 이미지 크기를 조정했기 때문에 `do_resize=False`로 설정하고, 해당 `image_processor`에서 `size` 속성을 활용했습니다. 
-이미지 증강 중에 이미지 크기를 조정하지 않은 경우 이 매개 변수를 생략하세요. 
-기본적으로 `ImageProcessor`가 크기 조정을 처리합니다. 
+이미지 증강 중에 이미지 크기를 조정하지 않은 경우 이 매개변수를 생략하세요. 
+기본적으로는 `ImageProcessor`가 크기 조정을 처리합니다. 
 
 증강 변환 과정에서 이미지를 정규화하려면 `image_processor.image_mean` 및 `image_processor.image_std` 값을 사용하세요.
 
@@ -420,8 +420,8 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 >>> dataset.set_transform(transforms)
 ```
 
-4. 이제 이미지에 액세스하면 이미지 프로세서가 `pixel_values`를 추가한 것을 알 수 있습니다. 
-드디어 처리된 데이터셋을 모델에 전달할 수 있습니다!
+4. 이제 이미지에 접근하면 이미지 프로세서가 `pixel_values`를 추가한 것을 알 수 있습니다. 
+드디어 처리된 데이터 세트를 모델에 전달할 수 있습니다!
 
 ```py
 >>> dataset[0].keys()
@@ -448,11 +448,11 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 </Tip>
 
-### 패드[[pad]]
+### 패딩[[pad]]
 
-예를 들어, [DETR](./model_doc/detr)와 같은 경우에는 모델이 학습할 때 크기 조정 증강을 적용합니다. 
-이로 인해 배치 내 이미지 크기가 다를 수 있습니다. 
-[`DetrImageProcessor`]의 [`DetrImageProcessor.pad_and_create_pixel_mask`]를 사용하고 사용자 지정 `collate_fn`을 정의해서 배치 이미지를 처리할 수 있습니다.
+예를 들어, [DETR](./model_doc/detr)와 같은 경우에는 모델이 훈련할 때 크기 조정 증강을 적용합니다. 
+이로 인해 배치 내 이미지 크기가 달라질 수 있습니다. 
+[`DetrImageProcessor`]의 [`DetrImageProcessor.pad_and_create_pixel_mask`]를 사용하고 사용자 정의 `collate_fn`을 정의해서 배치 이미지를 처리할 수 있습니다.
 
 ```py
 >>> def collate_fn(batch):
@@ -468,11 +468,11 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 
 ## 멀티모달[[multimodal]]
 
-멀티모달 입력이 필요한 작업의 경우, 모델에 데이터셋을 준비하기 위한 [프로세서](main_classes/processors)가 필요합니다. 
+멀티모달 입력이 필요한 작업의 경우, 모델에 데이터 세트를 준비하기 위한 [프로세서](main_classes/processors)가 필요합니다. 
 프로세서는 토크나이저와 특성 추출기와 같은 두 가지 처리 객체를 결합합니다.
 
-[LJ Speech](https://huggingface.co/datasets/lj_speech) 데이터셋을 로드하여 자동 음성 인식(ASR)을 위한 프로세서를 사용하는 방법을 확인하세요. 
-(데이터셋을 로드하는 방법에 대한 자세한 내용은 🤗 [데이터셋 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)에서 볼 수 있습니다.)
+[LJ Speech](https://huggingface.co/datasets/lj_speech) 데이터 세트를 가져와서 자동 음성 인식(ASR)을 위한 프로세서를 사용하는 방법을 확인하세요. 
+(데이터 세트를 가져오는 방법에 대한 자세한 내용은 🤗 [데이터 세트 튜토리얼](https://huggingface.co/docs/datasets/load_hub.html)에서 볼 수 있습니다.)
 
 ```py
 >>> from datasets import load_dataset
@@ -480,7 +480,7 @@ array([[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0],
 >>> lj_speech = load_dataset("lj_speech", split="train")
 ```
 
-ASR에서는 `audio`와 `text`에만 집중하면 되므로, 다른 열들을 제거할 수 있습니다:
+자동 음성 인식(ASR)에서는 `audio`와 `text`에만 집중하면 되므로, 다른 열들은 제거할 수 있습니다:
 
 ```py
 >>> lj_speech = lj_speech.map(remove_columns=["file", "id", "normalized_text"])
@@ -499,7 +499,7 @@ ASR에서는 `audio`와 `text`에만 집중하면 되므로, 다른 열들을
 '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'
 ```
 
-기존에 사전 학습된 모델에서 사용된 데이터셋과 새로운 오디오 데이터셋의 샘플링 레이트를 일치시키기 위해 오디오 데이터셋의 샘플링 레이트를 [리샘플링](preprocessing#audio)해야 합니다!
+기존에 사전훈련된 모델에서 사용된 데이터 세트와 새로운 오디오 데이터 세트의 샘플링 레이트를 일치시키기 위해 오디오 데이터 세트의 샘플링 레이트를 [리샘플링](preprocessing#audio)해야 합니다!
 
 ```py
 >>> lj_speech = lj_speech.cast_column("audio", Audio(sampling_rate=16_000))
@@ -531,5 +531,5 @@ ASR에서는 `audio`와 `text`에만 집중하면 되므로, 다른 열들을
 >>> prepare_dataset(lj_speech[0])
 ```
 
-이제 프로세서가 `input_values`와 `labels`를 추가하고, 샘플링 레이트도 올바르게 16kHz로 다운 샘플링했습니다. 
-드디어 처리된 데이터셋을 모델에 전달할 수 있습니다!
+이제 프로세서가 `input_values`와 `labels`를 추가하고, 샘플링 레이트도 올바르게 16kHz로 다운샘플링했습니다. 
+드디어 처리된 데이터 세트를 모델에 전달할 수 있습니다!

docs/source/ko/task_summary.mdx

@@ -0,0 +1,337 @@
+<!--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.
+-->
+
+# 🤗 Transformers로 할 수 있는 것[[what__transformers_can_do]]
+
+🤗 Transformers는 자연어처리(NLP), 컴퓨터 비전, 오디오 및 음성 처리 작업에 대한 사전훈련된 최첨단 모델 라이브러리입니다. 
+이 라이브러리는 트랜스포머 모델뿐만 아니라 컴퓨터 비전 작업을 위한 현대적인 합성곱 신경망과 같은 트랜스포머가 아닌 모델도 포함하고 있습니다. 
+
+스마트폰, 앱, 텔레비전과 같은 오늘날 가장 인기 있는 소비자 제품을 살펴보면, 딥러닝 기술이 그 뒤에 사용되고 있을 확률이 높습니다. 
+스마트폰으로 촬영한 사진에서 배경 객체를 제거하고 싶다면 어떻게 할까요? 이는 파놉틱 세그멘테이션 작업의 예입니다(아직 이게 무엇인지 모른다면, 다음 섹션에서 설명하겠습니다!).
+
+이 페이지는 다양한 음성 및 오디오, 컴퓨터 비전, NLP 작업을 🤗 Transformers 라이브러리를 활용하여 다루는 간단한 예제를 3줄의 코드로 제공합니다. 
+
+## 오디오[[audio]]
+
+
+음성 및 오디오 처리 작업은 다른 모달리티와 약간 다릅니다. 이는 주로 오디오가 연속적인 신호로 입력되기 때문입니다. 
+텍스트와 달리 원본 오디오 파형(waveform)은 문장이 단어로 나눠지는 것처럼 깔끔하게 이산적인 묶음으로 나눌 수 없습니다. 
+이를 극복하기 위해 원본 오디오 신호는 일정한 간격으로 샘플링됩니다. 해당 간격 내에서 더 많은 샘플을 취할 경우 샘플링률이 높아지며, 오디오는 원본 오디오 소스에 더 가까워집니다.
+
+과거의 접근 방식은 오디오에서 유용한 특징을 추출하기 위해 오디오를 전처리하는 것이었습니다. 
+하지만 현재는 원본 오디오 파형을 특성 인코더에 직접 넣어서 오디오 표현(representation)을 추출하는 것이 더 일반적입니다. 
+이렇게 하면 전처리 단계가 단순해지고 모델이 가장 중요한 특징을 학습할 수 있습니다.
+
+### 오디오 분류[[audio_classification]]
+
+
+오디오 분류는 오디오 데이터에 미리 정의된 클래스 집합의 레이블을 지정하는 작업입니다. 이는 많은 구체적인 응용 프로그램을 포함한 넓은 범주입니다.
+
+일부 예시는 다음과 같습니다:
+
+* 음향 장면 분류: 오디오에 장면 레이블("사무실", "해변", "경기장")을 지정합니다.
+* 음향 이벤트 감지: 오디오에 소리 이벤트 레이블("차 경적", "고래 울음소리", "유리 파손")을 지정합니다.
+* 태깅: 여러 가지 소리(새 지저귐, 회의에서의 화자 식별)가 포함된 오디오에 레이블을 지정합니다.
+* 음악 분류: 음악에 장르 레이블("메탈", "힙합", "컨트리")을 지정합니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> classifier = pipeline(task="audio-classification", model="superb/hubert-base-superb-er")
+>>> preds = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+>>> preds
+[{'score': 0.4532, 'label': 'hap'},
+ {'score': 0.3622, 'label': 'sad'},
+ {'score': 0.0943, 'label': 'neu'},
+ {'score': 0.0903, 'label': 'ang'}]
+```
+
+### 자동 음성 인식[[automatic_speech_recognition]]
+
+
+자동 음성 인식(ASR)은 음성을 텍스트로 변환하는 작업입니다. 
+음성은 인간의 자연스러운 의사소통 형태이기 때문에 ASR은 가장 일반적인 오디오 작업 중 하나입니다. 
+오늘날 ASR 시스템은 스피커, 전화 및 자동차와 같은 "스마트" 기술 제품에 내장되어 있습니다. 
+우리는 가상 비서에게 음악 재생, 알림 설정 및 날씨 정보를 요청할 수 있습니다.
+
+하지만 트랜스포머 아키텍처가 해결하는 데 도움을 준 핵심 도전 과제 중 하나는 양이 데이터 양이 적은 언어(low-resource language)에 대한 것입니다. 대량의 음성 데이터로 사전 훈련한 후 데이터 양이 적은 언어에서 레이블이 지정된 음성 데이터 1시간만으로 모델을 미세 조정하면 이전의 100배 많은 레이블이 지정된 데이터로 훈련된 ASR 시스템보다 훨씬 더 높은 품질의 결과를 얻을 수 있습니다. 
+```py
+>>> from transformers import pipeline
+
+>>> transcriber = pipeline(task="automatic-speech-recognition", model="openai/whisper-small")
+>>> transcriber("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/mlk.flac")
+{'text': ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}
+```
+
+## 컴퓨터 비전[[computer_vision]]
+
+컴퓨터 비전 작업 중 가장 초기의 성공적인 작업 중 하나는 [합성곱 신경망(CNN)](glossary#convolution)을 사용하여 우편번호 숫자 이미지를 인식하는 것이었습니다. 이미지는 픽셀로 구성되어 있으며 각 픽셀은 숫자 값으로 표현됩니다. 이로써 이미지를 픽셀 값의 행렬로 나타내는 것이 쉬워집니다. 특정한 픽셀 값의 조합은 이미지의 색상을 의미합니다.
+
+컴퓨터 비전 작업은 일반적으로 다음 두 가지 방법으로 접근 가능합니다:
+
+1. 합성곱을 사용하여 이미지의 낮은 수준 특징에서 높은 수준의 추상적인 요소까지 계층적으로 학습합니다.
+
+2. 이미지를 패치로 나누고 트랜스포머를 사용하여 점진적으로 각 이미지 패치가 서로 어떠한 방식으로 연관되어 이미지를 형성하는지 학습합니다. `CNN`에서 선호하는 상향식 접근법과는 달리, 이 방식은 흐릿한 이미지로 초안을 그리고 점진적으로 선명한 이미지로 만들어가는 것과 유사합니다.
+
+### 이미지 분류[[image_classification]]
+
+
+이미지 분류는 한 개의 전체 이미지에 미리 정의된 클래스 집합의 레이블을 지정하는 작업입니다. 
+
+대부분의 분류 작업과 마찬가지로, 이미지 분류에는 다양한 실용적인 용도가 있으며, 일부 예시는 다음과 같습니다:
+
+
+* 의료: 질병을 감지하거나 환자 건강을 모니터링하기 위해 의료 이미지에 레이블을 지정합니다.
+* 환경: 위성 이미지를 분류하여 산림 벌채를 감시하고 야생 지역 관리를 위한 정보를 제공하거나 산불을 감지합니다. 
+* 농업: 작물 이미지를 분류하여 식물 건강을 확인하거나 위성 이미지를 분류하여 토지 이용 관찰에 사용합니다.
+* 생태학: 동물이나 식물 종 이미지를 분류하여 야생 동물 개체군을 조사하거나 멸종 위기에 처한 종을 추적합니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> classifier = pipeline(task="image-classification")
+>>> preds = classifier(
+...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+... )
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+>>> print(*preds, sep="\n")
+{'score': 0.4335, 'label': 'lynx, catamount'}
+{'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}
+{'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}
+{'score': 0.0239, 'label': 'Egyptian cat'}
+{'score': 0.0229, 'label': 'tiger cat'}
+```
+
+### 객체 탐지[[object_detection]]
+
+
+이미지 분류와 달리 객체 탐지는 이미지 내에서 여러 객체를 식별하고 바운딩 박스로 정의된 객체의 위치를 파악합니다. 
+
+객체 탐지의 몇 가지 응용 예시는 다음과 같습니다:
+
+* 자율 주행 차량: 다른 차량, 보행자 및 신호등과 같은 일상적인 교통 객체를 감지합니다.
+* 원격 감지: 재난 모니터링, 도시 계획 및 기상 예측 등을 수행합니다.
+* 결함 탐지: 건물의 균열이나 구조적 손상, 제조 결함 등을 탐지합니다.
+
+
+```py
+>>> from transformers import pipeline
+
+>>> detector = pipeline(task="object-detection")
+>>> preds = detector(
+...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+... )
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"], "box": pred["box"]} for pred in preds]
+>>> preds
+[{'score': 0.9865,
+  'label': 'cat',
+  'box': {'xmin': 178, 'ymin': 154, 'xmax': 882, 'ymax': 598}}]
+```
+
+### 이미지 분할[[image_segmentation]]
+
+
+이미지 분할은 픽셀 차원의 작업으로, 이미지 내의 모든 픽셀을 클래스에 할당합니다. 이는 객체 탐지와 다릅니다. 객체 탐지는 바운딩 박스를 사용하여 이미지 내의 객체를 레이블링하고 예측하는 반면, 분할은 더 세분화된 작업입니다. 분할은 픽셀 수준에서 객체를 감지할 수 있습니다. 
+
+이미지 분할에는 여러 유형이 있습니다:
+
+* 인스턴스 분할: 개체의 클래스를 레이블링하는 것 외에도, 개체의 각 구분된 인스턴스에도 레이블을 지정합니다 ("개-1", "개-2" 등).
+* 파놉틱 분할: 의미적 분할과 인스턴스 분할의 조합입니다. 각 픽셀을 의미적 클래스로 레이블링하는 **동시에** 개체의 각각 구분된 인스턴스로도 레이블을 지정합니다.
+
+분할 작업은 자율 주행 차량에서 유용하며, 주변 환경의 픽셀 수준 지도를 생성하여 보행자와 다른 차량 주변에서 안전하게 탐색할 수 있습니다. 또한 의료 영상에서도 유용합니다. 분할 작업이 픽셀 수준에서 객체를 감지할 수 있기 때문에 비정상적인 세포나 장기의 특징을 식별하는 데 도움이 될 수 있습니다. 이미지 분할은 의류 가상 시착이나 카메라를 통해 실제 세계에 가상 개체를 덧씌워 증강 현실 경험을 만드는 등 전자 상거래 분야에서도 사용될 수 있습니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> segmenter = pipeline(task="image-segmentation")
+>>> preds = segmenter(
+...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+... )
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+>>> print(*preds, sep="\n")
+{'score': 0.9879, 'label': 'LABEL_184'}
+{'score': 0.9973, 'label': 'snow'}
+{'score': 0.9972, 'label': 'cat'}
+```
+
+### 깊이 추정[[depth_estimation]]
+
+깊이 추정은 카메라로부터 이미지 내부의 각 픽셀의 거리를 예측합니다. 이 컴퓨터 비전 작업은 특히 장면 이해와 재구성에 중요합니다. 예를 들어, 자율 주행 차량은 보행자, 교통 표지판 및 다른 차량과 같은 객체와의 거리를 이해하여 장애물과 충돌을 피해야 합니다. 깊이 정보는 또한 2D 이미지에서 3D 표현을 구성하는 데 도움이 되며 생물학적 구조나 건물의 고품질 3D 표현을 생성하는 데 사용될 수 있습니다.
+
+깊이 추정에는 두 가지 접근 방식이 있습니다:
+
+* 스테레오: 약간 다른 각도에서 촬영된 동일한 이미지 두 장을 비교하여 깊이를 추정합니다.
+* 단안: 단일 이미지에서 깊이를 추정합니다.
+
+
+```py
+>>> from transformers import pipeline
+
+>>> depth_estimator = pipeline(task="depth-estimation")
+>>> preds = depth_estimator(
+...     "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+... )
+```
+
+## 자연어처리[[natural_language_processing]]
+
+텍스트는 인간이 의사 소통하는 자연스러운 방식 중 하나이기 때문에 자연어처리 역시 가장 일반적인 작업 유형 중 하나입니다. 모델이 인식하는 형식으로 텍스트를 변환하려면 토큰화해야 합니다. 이는 텍스트 시퀀스를 개별 단어 또는 하위 단어(토큰)로 분할한 다음 이러한 토큰을 숫자로 변환하는 것을 의미합니다. 결과적으로 텍스트 시퀀스를 숫자 시퀀스로 표현할 수 있으며, 숫자 시퀀스를 다양한 자연어처리 작업을 해결하기 위한 모델에 입력할 수 있습니다!
+
+### 텍스트 분류[[text_classification]]
+
+다른 모달리티에서의 분류 작업과 마찬가지로 텍스트 분류는 미리 정의된 클래스 집합에서 텍스트 시퀀스(문장 수준, 단락 또는 문서 등)에 레이블을 지정합니다. 텍스트 분류에는 다양한 실용적인 응용 사례가 있으며, 일부 예시는 다음과 같습니다:
+
+* 감성 분석: 텍스트를 `긍정` 또는 `부정`과 같은 어떤 극성에 따라 레이블링하여 정치, 금융, 마케팅과 같은 분야에서 의사 결정에 정보를 제공하고 지원할 수 있습니다.
+* 콘텐츠 분류: 텍스트를 주제에 따라 레이블링(날씨, 스포츠, 금융 등)하여 뉴스 및 소셜 미디어 피드에서 정보를 구성하고 필터링하는 데 도움이 될 수 있습니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> classifier = pipeline(task="sentiment-analysis")
+>>> preds = classifier("Hugging Face is the best thing since sliced bread!")
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+>>> preds
+[{'score': 0.9991, 'label': 'POSITIVE'}]
+```
+
+### 토큰 분류[[token_classification]]
+
+모든 자연어처리 작업에서는 텍스트가 개별 단어나 하위 단어로 분리되어 전처리됩니다. 분리된 단어를 [토큰](/glossary#token)이라고 합니다. 토큰 분류는 각 토큰에 미리 정의된 클래스 집합의 레이블을 할당합니다.
+
+토큰 분류의 두 가지 일반적인 유형은 다음과 같습니다:
+
+* 개체명 인식 (NER): 토큰을 조직, 인물, 위치 또는 날짜와 같은 개체 범주에 따라 레이블링합니다. NER은 특히 유전체학적인 환경에서 유전자, 단백질 및 약물 이름에 레이블을 지정하는 데 널리 사용됩니다.
+* 품사 태깅 (POS): 명사, 동사, 형용사와 같은 품사에 따라 토큰에 레이블을 할당합니다. POS는 번역 시스템이 동일한 단어가 문법적으로 어떻게 다른지 이해하는 데 도움이 됩니다 (명사로 사용되는 "bank(은행)"과 동사로 사용되는 "bank(예금을 예치하다)"과 같은 경우).
+
+
+```py
+>>> from transformers import pipeline
+
+>>> classifier = pipeline(task="ner")
+>>> preds = classifier("Hugging Face is a French company based in New York City.")
+>>> preds = [
+...     {
+...         "entity": pred["entity"],
+...         "score": round(pred["score"], 4),
+...         "index": pred["index"],
+...         "word": pred["word"],
+...         "start": pred["start"],
+...         "end": pred["end"],
+...     }
+...     for pred in preds
+... ]
+>>> print(*preds, sep="\n")
+{'entity': 'I-ORG', 'score': 0.9968, 'index': 1, 'word': 'Hu', 'start': 0, 'end': 2}
+{'entity': 'I-ORG', 'score': 0.9293, 'index': 2, 'word': '##gging', 'start': 2, 'end': 7}
+{'entity': 'I-ORG', 'score': 0.9763, 'index': 3, 'word': 'Face', 'start': 8, 'end': 12}
+{'entity': 'I-MISC', 'score': 0.9983, 'index': 6, 'word': 'French', 'start': 18, 'end': 24}
+{'entity': 'I-LOC', 'score': 0.999, 'index': 10, 'word': 'New', 'start': 42, 'end': 45}
+{'entity': 'I-LOC', 'score': 0.9987, 'index': 11, 'word': 'York', 'start': 46, 'end': 50}
+{'entity': 'I-LOC', 'score': 0.9992, 'index': 12, 'word': 'City', 'start': 51, 'end': 55}
+```
+
+### 질의응답[[question_answering]]
+
+질의응답은 또 하나의 토큰 차원의 작업으로, 문맥이 있을 때(개방형 도메인)와 문맥이 없을 때(폐쇄형 도메인) 질문에 대한 답변을 반환합니다. 이 작업은 가상 비서에게 식당이 영업 중인지와 같은 질문을 할 때마다 발생할 수 있습니다. 고객 지원 또는 기술 지원을 제공하거나 검색 엔진이 요청한 정보를 검색하는 데 도움을 줄 수 있습니다.
+
+질문 답변에는 일반적으로 두 가지 유형이 있습니다:
+
+* 추출형: 질문과 문맥이 주어졌을 때, 모델이 주어진 문맥의 일부에서 가져온 텍스트의 범위를 답변으로 합니다.
+* 생성형: 질문과 문맥이 주어졌을 때, 주어진 문맥을 통해 답변을 생성합니다. 이 접근 방식은 [`QuestionAnsweringPipeline`] 대신 [`Text2TextGenerationPipeline`]을 통해 처리됩니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> question_answerer = pipeline(task="question-answering")
+>>> preds = question_answerer(
+...     question="What is the name of the repository?",
+...     context="The name of the repository is huggingface/transformers",
+... )
+>>> print(
+...     f"score: {round(preds['score'], 4)}, start: {preds['start']}, end: {preds['end']}, answer: {preds['answer']}"
+... )
+score: 0.9327, start: 30, end: 54, answer: huggingface/transformers
+```
+
+### 요약[[summarization]]
+
+요약은 원본 문서의 의미를 최대한 보존하면서 긴 문서를 짧은 문서로 만드는 작업입니다. 요약은 `sequence-to-sequence` 작업입니다. 입력보다 짧은 텍스트 시퀀스를 출력합니다. 요약 작업은 독자가 장문 문서들의 주요 포인트를 빠르게 이해하는 데 도움을 줄 수 있습니다. 입법안, 법률 및 금융 문서, 특허 및 과학 논문은 요약 작업이 독자의 시간을 절약하고 독서 보조 도구로 사용될 수 있는 몇 가지 예시입니다.
+
+질문 답변과 마찬가지로 요약에는 두 가지 유형이 있습니다:
+
+* 추출형: 원본 텍스트에서 가장 중요한 문장을 식별하고 추출합니다.
+* 생성형: 원본 텍스트에서 목표 요약을 생성합니다. 입력 문서에 없는 새로운 단어를 포함할 수도 있습니다. [`SummarizationPipeline`]은 생성형 접근 방식을 사용합니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> summarizer = pipeline(task="summarization")
+>>> summarizer(
+...     "In this work, we presented the Transformer, the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks, we achieve a new state of the art. In the former task our best model outperforms even all previously reported ensembles."
+... )
+[{'summary_text': ' The Transformer is the first sequence transduction model based entirely on attention . It replaces the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention . For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers .'}]
+```
+
+### 번역[[translation]]
+
+번역은 한 언어로 된 텍스트 시퀀스를 다른 언어로 변환하는 작업입니다. 이는 서로 다른 배경을 가진 사람들이 서로 소통하는 데 도움을 주는 중요한 역할을 합니다. 더 넓은 대중에게 콘텐츠를 번역하여 전달하거나, 새로운 언어를 배우는 데 도움이 되는 학습 도구가 될 수도 있습니다. 요약과 마찬가지로, 번역은 `sequence-to-sequence` 작업입니다. 즉, 모델은 입력 시퀀스를 받아서 출력이 되는 목표 시퀀스를 반환합니다.
+
+초기의 번역 모델은 대부분 단일 언어로 이루어져 있었지만, 최근에는 많은 언어 쌍 간에 번역을 수행할 수 있는 다중 언어 모델에 대한 관심이 높아지고 있습니다.
+
+```py
+>>> from transformers import pipeline
+
+>>> text = "translate English to French: Hugging Face is a community-based open-source platform for machine learning."
+>>> translator = pipeline(task="translation", model="t5-small")
+>>> translator(text)
+[{'translation_text': "Hugging Face est une tribune communautaire de l'apprentissage des machines."}]
+```
+
+### 언어 모델링[[language_modeling]]
+
+언어 모델링은 텍스트 시퀀스에서 단어를 예측하는 작업입니다. 사전 훈련된 언어 모델은 많은 다른 하위 작업에 따라 미세 조정될 수 있기 때문에 매우 인기 있는 자연어처리 작업이 되었습니다. 최근에는 제로 샷(zero-shot) 또는 퓨 샷(few-shot) 학습이 가능한 대규모 언어 모델(Large Language Models, LLM)에 대한 많은 관심이 발생하고 있습니다. 이는 모델이 명시적으로 훈련되지 않은 작업도 해결할 수 있다는 것을 의미합니다! 언어 모델은 유창하고 설득력 있는 텍스트를 생성하는 데 사용될 수 있지만, 텍스트가 항상 정확하지는 않을 수 있으므로 주의가 필요합니다.
+
+언어 모델링에는 두 가지 유형이 있습니다:
+
+* 인과적 언어 모델링: 이 모델의 목적은 시퀀스에서 다음 토큰을 예측하는 것이며, 미래 토큰이 마스킹 됩니다.
+    ```py
+    >>> from transformers import pipeline
+
+    >>> prompt = "Hugging Face is a community-based open-source platform for machine learning."
+    >>> generator = pipeline(task="text-generation")
+    >>> generator(prompt)  # doctest: +SKIP
+    ```
+
+* 마스킹된 언어 모델링: 이 모델의 목적은 시퀀스 내의 마스킹된 토큰을 예측하는 것이며, 시퀀스 내의 모든 토큰에 대한 접근이 제공됩니다.
+    
+    ```py
+    >>> text = "Hugging Face is a community-based open-source <mask> for machine learning."
+    >>> fill_mask = pipeline(task="fill-mask")
+    >>> preds = fill_mask(text, top_k=1)
+    >>> preds = [
+    ...     {
+    ...         "score": round(pred["score"], 4),
+    ...         "token": pred["token"],
+    ...         "token_str": pred["token_str"],
+    ...         "sequence": pred["sequence"],
+    ...     }
+    ...     for pred in preds
+    ... ]
+    >>> preds
+    [{'score': 0.2236,
+      'token': 1761,
+      'token_str': ' platform',
+      'sequence': 'Hugging Face is a community-based open-source platform for machine learning.'}]
+    ```
+
+이 페이지를 통해 각 모달리티의 다양한 작업 유형과 각 작업의 실용적 중요성에 대해 추가적인 배경 정보를 얻으셨기를 바랍니다. 다음 [섹션](tasks_explained)에서는 🤗 Transformer가 이러한 작업을 해결하는 **방법**에 대해 알아보실 수 있습니다.

docs/source/ko/tasks/asr.mdx

@@ -0,0 +1,376 @@
+<!--Copyright 2023 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.
+-->
+
+# 자동 음성 인식[[automatic-speech-recognition]]
+
+[[open-in-colab]]
+
+<Youtube id="TksaY_FDgnk"/>
+
+자동 음성 인식(Automatic Speech Recognition, ASR)은 음성 신호를 텍스트로 변환하여 음성 입력 시퀀스를 텍스트 출력에 매핑합니다. 
+Siri와 Alexa와 같은 가상 어시스턴트는 ASR 모델을 사용하여 일상적으로 사용자를 돕고 있으며, 회의 중 라이브 캡션 및 메모 작성과 같은 유용한 사용자 친화적 응용 프로그램도 많이 있습니다.
+
+이 가이드에서 소개할 내용은 아래와 같습니다:
+
+1. [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 데이터 세트에서 [Wav2Vec2](https://huggingface.co/facebook/wav2vec2-base)를 미세 조정하여 오디오를 텍스트로 변환합니다.
+2. 미세 조정한 모델을 추론에 사용합니다.
+
+<Tip>
+이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에 의해 지원됩니다:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[Data2VecAudio](../model_doc/data2vec-audio), [Hubert](../model_doc/hubert), [M-CTC-T](../model_doc/mctct), [SEW](../model_doc/sew), [SEW-D](../model_doc/sew-d), [UniSpeech](../model_doc/unispeech), [UniSpeechSat](../model_doc/unispeech-sat), [Wav2Vec2](../model_doc/wav2vec2), [Wav2Vec2-Conformer](../model_doc/wav2vec2-conformer), [WavLM](../model_doc/wavlm)
+
+<!--End of the generated tip-->
+
+</Tip>
+
+시작하기 전에 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
+
+```bash
+pip install transformers datasets evaluate jiwer
+```
+
+Hugging Face 계정에 로그인하면 모델을 업로드하고 커뮤니티에 공유할 수 있습니다. 토큰을 입력하여 로그인하세요.
+
+```py
+>>> from huggingface_hub import notebook_login
+
+>>> notebook_login()
+```
+
+## MInDS-14 데이터 세트 가져오기[[load-minds-14-dataset]]
+
+먼저, 🤗 Datasets 라이브러리에서 [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) 데이터 세트의 일부분을 가져오세요. 
+이렇게 하면 전체 데이터 세트에 대한 훈련에 시간을 들이기 전에 모든 것이 작동하는지 실험하고 검증할 수 있습니다.
+
+```py
+>>> from datasets import load_dataset, Audio
+
+>>> minds = load_dataset("PolyAI/minds14", name="en-US", split="train[:100]")
+```
+
+[`~Dataset.train_test_split`] 메소드를 사용하여 데이터 세트의 `train`을 훈련 세트와 테스트 세트로 나누세요:
+
+```py
+>>> minds = minds.train_test_split(test_size=0.2)
+```
+
+그리고 데이터 세트를 확인하세요:
+
+```py
+>>> minds
+DatasetDict({
+    train: Dataset({
+        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
+        num_rows: 16
+    })
+    test: Dataset({
+        features: ['path', 'audio', 'transcription', 'english_transcription', 'intent_class', 'lang_id'],
+        num_rows: 4
+    })
+})
+```
+
+데이터 세트에는 `lang_id`와 `english_transcription`과 같은 유용한 정보가 많이 포함되어 있지만, 이 가이드에서는 `audio`와 `transcription`에 초점을 맞출 것입니다. 다른 열은 [`~datasets.Dataset.remove_columns`] 메소드를 사용하여 제거하세요:
+
+```py
+>>> minds = minds.remove_columns(["english_transcription", "intent_class", "lang_id"])
+```
+
+예시를 다시 한번 확인해보세요:
+
+```py
+>>> minds["train"][0]
+{'audio': {'array': array([-0.00024414,  0.        ,  0.        , ...,  0.00024414,
+          0.00024414,  0.00024414], dtype=float32),
+  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
+  'sampling_rate': 8000},
+ 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
+ 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
+```
+
+두 개의 필드가 있습니다:
+
+- `audio`: 오디오 파일을 가져오고 리샘플링하기 위해 호출해야 하는 음성 신호의 1차원 `array(배열)`
+- `transcription`: 목표 텍스트
+
+## 전처리[[preprocess]]
+
+다음으로 오디오 신호를 처리하기 위한 Wav2Vec2 프로세서를 가져옵니다:
+
+```py
+>>> from transformers import AutoProcessor
+
+>>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
+```
+
+MInDS-14 데이터 세트의 샘플링 레이트는 8000kHz이므로([데이터 세트 카드](https://huggingface.co/datasets/PolyAI/minds14)에서 확인), 사전 훈련된 Wav2Vec2 모델을 사용하려면 데이터 세트를 16000kHz로 리샘플링해야 합니다:
+
+```py
+>>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))
+>>> minds["train"][0]
+{'audio': {'array': array([-2.38064706e-04, -1.58618059e-04, -5.43987835e-06, ...,
+          2.78103951e-04,  2.38446111e-04,  1.18740834e-04], dtype=float32),
+  'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
+  'sampling_rate': 16000},
+ 'path': '/root/.cache/huggingface/datasets/downloads/extracted/f14948e0e84be638dd7943ac36518a4cf3324e8b7aa331c5ab11541518e9368c/en-US~APP_ERROR/602ba9e2963e11ccd901cd4f.wav',
+ 'transcription': "hi I'm trying to use the banking app on my phone and currently my checking and savings account balance is not refreshing"}
+```
+
+위의 'transcription'에서 볼 수 있듯이 텍스트는 대문자와 소문자가 섞여 있습니다. Wav2Vec2 토크나이저는 대문자 문자에 대해서만 훈련되어 있으므로 텍스트가 토크나이저의 어휘와 일치하는지 확인해야 합니다:
+
+```py
+>>> def uppercase(example):
+...     return {"transcription": example["transcription"].upper()}
+
+
+>>> minds = minds.map(uppercase)
+```
+
+이제 다음 작업을 수행할 전처리 함수를 만들어보겠습니다:
+
+1. `audio` 열을 호출하여 오디오 파일을 가져오고 리샘플링합니다.
+2. 오디오 파일에서 `input_values`를 추출하고 프로세서로 `transcription` 열을 토큰화합니다.
+
+```py
+>>> def prepare_dataset(batch):
+...     audio = batch["audio"]
+...     batch = processor(audio["array"], sampling_rate=audio["sampling_rate"], text=batch["transcription"])
+...     batch["input_length"] = len(batch["input_values"][0])
+...     return batch
+```
+
+전체 데이터 세트에 전처리 함수를 적용하려면 🤗 Datasets [`~datasets.Dataset.map`] 함수를 사용하세요. `num_proc` 매개변수를 사용하여 프로세스 수를 늘리면 `map`의 속도를 높일 수 있습니다. [`~datasets.Dataset.remove_columns`] 메소드를 사용하여 필요하지 않은 열을 제거하세요:
+
+```py
+>>> encoded_minds = minds.map(prepare_dataset, remove_columns=minds.column_names["train"], num_proc=4)
+```
+
+🤗 Transformers에는 자동 음성 인식용 데이터 콜레이터가 없으므로 예제 배치를 생성하려면 [`DataCollatorWithPadding`]을 조정해야 합니다. 이렇게 하면 데이터 콜레이터는 텍스트와 레이블을 배치에서 가장 긴 요소의 길이에 동적으로 패딩하여 길이를 균일하게 합니다. `tokenizer` 함수에서 `padding=True`를 설정하여 텍스트를 패딩할 수 있지만, 동적 패딩이 더 효율적입니다.
+
+다른 데이터 콜레이터와 달리 이 특정 데이터 콜레이터는 `input_values`와 `labels`에 대해 다른 패딩 방법을 적용해야 합니다.
+
+```py
+>>> import torch
+
+>>> from dataclasses import dataclass, field
+>>> from typing import Any, Dict, List, Optional, Union
+
+
+>>> @dataclass
+... class DataCollatorCTCWithPadding:
+...     processor: AutoProcessor
+...     padding: Union[bool, str] = "longest"
+
+...     def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+...         # 입력과 레이블을 분할합니다
+...         # 길이가 다르고, 각각 다른 패딩 방법을 사용해야 하기 때문입니다
+...         input_features = [{"input_values": feature["input_values"][0]} for feature in features]
+...         label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+...         batch = self.processor.pad(input_features, padding=self.padding, return_tensors="pt")
+
+...         labels_batch = self.processor.pad(labels=label_features, padding=self.padding, return_tensors="pt")
+
+...         # 패딩에 대해 손실을 적용하지 않도록 -100으로 대체합니다
+...         labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+...         batch["labels"] = labels
+
+...         return batch
+```
+
+이제 `DataCollatorForCTCWithPadding`을 인스턴스화합니다:
+
+```py
+>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding="longest")
+```
+
+## 평가하기[[evaluate]]
+
+훈련 중에 평가 지표를 포함하면 모델의 성능을 평가하는 데 도움이 되는 경우가 많습니다. 🤗 [Evaluate](https://huggingface.co/docs/evaluate/index) 라이브러리를 사용하면 평가 방법을 빠르게 불러올 수 있습니다. 
+이 작업에서는 [단어 오류율(Word Error Rate, WER)](https://huggingface.co/spaces/evaluate-metric/wer) 평가 지표를 가져옵니다.
+(평가 지표를 불러오고 계산하는 방법은 🤗 Evaluate [둘러보기](https://huggingface.co/docs/evaluate/a_quick_tour)를 참조하세요):
+
+```py
+>>> import evaluate
+
+>>> wer = evaluate.load("wer")
+```
+
+그런 다음 예측값과 레이블을 [`~evaluate.EvaluationModule.compute`]에 전달하여 WER을 계산하는 함수를 만듭니다:
+
+```py
+>>> import numpy as np
+
+
+>>> def compute_metrics(pred):
+...     pred_logits = pred.predictions
+...     pred_ids = np.argmax(pred_logits, axis=-1)
+
+...     pred.label_ids[pred.label_ids == -100] = processor.tokenizer.pad_token_id
+
+...     pred_str = processor.batch_decode(pred_ids)
+...     label_str = processor.batch_decode(pred.label_ids, group_tokens=False)
+
+...     wer = wer.compute(predictions=pred_str, references=label_str)
+
+...     return {"wer": wer}
+```
+
+이제 `compute_metrics` 함수를 사용할 준비가 되었으며, 훈련을 설정할 때 이 함수로 되돌아올 것입니다.
+
+## 훈련하기[[train]]
+
+<frameworkcontent>
+<pt>
+<Tip>
+
+[`Trainer`]로 모델을 미세 조정하는 것이 익숙하지 않다면, [여기](../training#train-with-pytorch-trainer)에서 기본 튜토리얼을 확인해보세요!
+
+</Tip>
+
+이제 모델 훈련을 시작할 준비가 되었습니다! [`AutoModelForCTC`]로 Wav2Vec2를 가져오세요. `ctc_loss_reduction` 매개변수로 CTC 손실에 적용할 축소(reduction) 방법을 지정하세요. 기본값인 합계 대신 평균을 사용하는 것이 더 좋은 경우가 많습니다:
+
+```py
+>>> from transformers import AutoModelForCTC, TrainingArguments, Trainer
+
+>>> model = AutoModelForCTC.from_pretrained(
+...     "facebook/wav2vec2-base",
+...     ctc_loss_reduction="mean",
+...     pad_token_id=processor.tokenizer.pad_token_id,
+... )
+```
+
+이제 세 단계만 남았습니다:
+
+1. [`TrainingArguments`]에서 훈련 하이퍼파라미터를 정의하세요. `output_dir`은 모델을 저장할 경로를 지정하는 유일한 필수 매개변수입니다. `push_to_hub=True`를 설정하여 모델을 Hub에 업로드 할 수 있습니다(모델을 업로드하려면 Hugging Face에 로그인해야 합니다). [`Trainer`]는 각 에폭마다 WER을 평가하고 훈련 체크포인트를 저장합니다.
+2. 모델, 데이터 세트, 토크나이저, 데이터 콜레이터, `compute_metrics` 함수와 함께 [`Trainer`]에 훈련 인수를 전달하세요.
+3. [`~Trainer.train`]을 호출하여 모델을 미세 조정하세요.
+
+```py
+>>> training_args = TrainingArguments(
+...     output_dir="my_awesome_asr_mind_model",
+...     per_device_train_batch_size=8,
+...     gradient_accumulation_steps=2,
+...     learning_rate=1e-5,
+...     warmup_steps=500,
+...     max_steps=2000,
+...     gradient_checkpointing=True,
+...     fp16=True,
+...     group_by_length=True,
+...     evaluation_strategy="steps",
+...     per_device_eval_batch_size=8,
+...     save_steps=1000,
+...     eval_steps=1000,
+...     logging_steps=25,
+...     load_best_model_at_end=True,
+...     metric_for_best_model="wer",
+...     greater_is_better=False,
+...     push_to_hub=True,
+... )
+
+>>> trainer = Trainer(
+...     model=model,
+...     args=training_args,
+...     train_dataset=encoded_minds["train"],
+...     eval_dataset=encoded_minds["test"],
+...     tokenizer=processor.feature_extractor,
+...     data_collator=data_collator,
+...     compute_metrics=compute_metrics,
+... )
+
+>>> trainer.train()
+```
+
+훈련이 완료되면 모두가 모델을 사용할 수 있도록 [`~transformers.Trainer.push_to_hub`] 메소드를 사용하여 모델을 Hub에 공유하세요:
+
+```py
+>>> trainer.push_to_hub()
+```
+</pt>
+</frameworkcontent>
+
+<Tip>
+
+자동 음성 인식을 위해 모델을 미세 조정하는 더 자세한 예제는 영어 자동 음성 인식을 위한 [블로그 포스트](https://huggingface.co/blog/fine-tune-wav2vec2-english)와 다국어 자동 음성 인식을 위한 [포스트](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2)를 참조하세요.
+
+</Tip>
+
+## 추론하기[[inference]]
+
+좋아요, 이제 모델을 미세 조정했으니 추론에 사용할 수 있습니다!
+
+추론에 사용할 오디오 파일을 가져오세요. 필요한 경우 오디오 파일의 샘플링 비율을 모델의 샘플링 레이트에 맞게 리샘플링하는 것을 잊지 마세요!
+
+```py
+>>> from datasets import load_dataset, Audio
+
+>>> dataset = load_dataset("PolyAI/minds14", "en-US", split="train")
+>>> dataset = dataset.cast_column("audio", Audio(sampling_rate=16000))
+>>> sampling_rate = dataset.features["audio"].sampling_rate
+>>> audio_file = dataset[0]["audio"]["path"]
+```
+
+추론을 위해 미세 조정된 모델을 시험해보는 가장 간단한 방법은 [`pipeline`]을 사용하는 것입니다. 모델을 사용하여 자동 음성 인식을 위한 `pipeline`을 인스턴스화하고 오디오 파일을 전달하세요:
+
+```py
+>>> from transformers import pipeline
+
+>>> transcriber = pipeline("automatic-speech-recognition", model="stevhliu/my_awesome_asr_minds_model")
+>>> transcriber(audio_file)
+{'text': 'I WOUD LIKE O SET UP JOINT ACOUNT WTH Y PARTNER'}
+```
+
+<Tip>
+
+텍스트로 변환된 결과가 꽤 괜찮지만 더 좋을 수도 있습니다! 더 나은 결과를 얻으려면 더 많은 예제로 모델을 미세 조정하세요!
+
+</Tip>
+
+`pipeline`의 결과를 수동으로 재현할 수도 있습니다:
+
+<frameworkcontent>
+<pt>
+오디오 파일과 텍스트를 전처리하고 PyTorch 텐서로 `input`을 반환할 프로세서를 가져오세요:
+
+```py
+>>> from transformers import AutoProcessor
+
+>>> processor = AutoProcessor.from_pretrained("stevhliu/my_awesome_asr_mind_model")
+>>> inputs = processor(dataset[0]["audio"]["array"], sampling_rate=sampling_rate, return_tensors="pt")
+```
+
+입력을 모델에 전달하고 로짓을 반환하세요:
+
+```py
+>>> from transformers import AutoModelForCTC
+
+>>> model = AutoModelForCTC.from_pretrained("stevhliu/my_awesome_asr_mind_model")
+>>> with torch.no_grad():
+...     logits = model(**inputs).logits
+```
+
+가장 높은 확률의 `input_ids`를 예측하고, 프로세서를 사용하여 예측된 `input_ids`를 다시 텍스트로 디코딩하세요:
+
+```py
+>>> import torch
+
+>>> predicted_ids = torch.argmax(logits, dim=-1)
+>>> transcription = processor.batch_decode(predicted_ids)
+>>> transcription
+['I WOUL LIKE O SET UP JOINT ACOUNT WTH Y PARTNER']
+```
+</pt>
+</frameworkcontent>

docs/source/ko/tasks/language_modeling.mdx

@@ -0,0 +1,413 @@
+<!--Copyright 2023 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.
+-->
+
+# 인과 언어 모델링[[causal-language-modeling]]
+
+[[open-in-colab]]
+
+언어 모델링은 인과적 언어 모델링과 마스크드 언어 모델링, 두 가지 유형으로 나뉩니다. 이 가이드에서는 인과적 언어 모델링을 설명합니다.
+인과 언어 모델은 텍스트 생성에 자주 사용됩니다. 또 창의적인 방향으로 응용할 수 있습니다.
+직접 사용하며 재미있는 탐구를 해보거나, Copilot 또는 CodeParrot와 같은 지능형 코딩 어시스턴트의 기반이 되기도 합니다.
+
+<Youtube id="Vpjb1lu0MDk"/>
+
+인과 언어 모델링은 토큰 시퀀스에서 다음 토큰을 예측하며, 모델은 왼쪽의 토큰에만 접근할 수 있습니다.
+이는 모델이 미래의 토큰을 볼 수 없다는 것을 의미합니다. 인과 언어 모델의 예로 GPT-2가 있죠.
+
+이 가이드에서는 다음 작업을 수행하는 방법을 안내합니다:
+
+1. [DistilGPT2](https://huggingface.co/distilgpt2) 모델을 [ELI5](https://huggingface.co/datasets/eli5) 데이터 세트의 [r/askscience](https://www.reddit.com/r/askscience/) 하위 집합으로 미세 조정
+2. 미세 조정된 모델을 추론에 사용
+
+<Tip>
+이 안내서의 단계와 동일한 방법으로 인과 언어 모델링을 위해 다른 아키텍처를 미세 조정할 수 있습니다.
+다음 아키텍처 중 하나를 선택하세요:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+[BART](../model_doc/bart), [BERT](../model_doc/bert), [Bert Generation](../model_doc/bert-generation), [BigBird](../model_doc/big_bird), [BigBird-Pegasus](../model_doc/bigbird_pegasus), [BioGpt](../model_doc/biogpt), [Blenderbot](../model_doc/blenderbot), [BlenderbotSmall](../model_doc/blenderbot-small), [BLOOM](../model_doc/bloom), [CamemBERT](../model_doc/camembert), [CodeGen](../model_doc/codegen), [CPM-Ant](../model_doc/cpmant), [CTRL](../model_doc/ctrl), [Data2VecText](../model_doc/data2vec-text), [ELECTRA](../model_doc/electra), [ERNIE](../model_doc/ernie), [GIT](../model_doc/git), [GPT-Sw3](../model_doc/gpt-sw3), [OpenAI GPT-2](../model_doc/gpt2), [GPTBigCode](../model_doc/gpt_bigcode), [GPT Neo](../model_doc/gpt_neo), [GPT NeoX](../model_doc/gpt_neox), [GPT NeoX Japanese](../model_doc/gpt_neox_japanese), [GPT-J](../model_doc/gptj), [LLaMA](../model_doc/llama), [Marian](../model_doc/marian), [mBART](../model_doc/mbart), [MEGA](../model_doc/mega), [Megatron-BERT](../model_doc/megatron-bert), [MVP](../model_doc/mvp), [OpenLlama](../model_doc/open-llama), [OpenAI GPT](../model_doc/openai-gpt), [OPT](../model_doc/opt), [Pegasus](../model_doc/pegasus), [PLBart](../model_doc/plbart), [ProphetNet](../model_doc/prophetnet), [QDQBert](../model_doc/qdqbert), [Reformer](../model_doc/reformer), [RemBERT](../model_doc/rembert), [RoBERTa](../model_doc/roberta), [RoBERTa-PreLayerNorm](../model_doc/roberta-prelayernorm), [RoCBert](../model_doc/roc_bert), [RoFormer](../model_doc/roformer), [RWKV](../model_doc/rwkv), [Speech2Text2](../model_doc/speech_to_text_2), [Transformer-XL](../model_doc/transfo-xl), [TrOCR](../model_doc/trocr), [XGLM](../model_doc/xglm), [XLM](../model_doc/xlm), [XLM-ProphetNet](../model_doc/xlm-prophetnet), [XLM-RoBERTa](../model_doc/xlm-roberta), [XLM-RoBERTa-XL](../model_doc/xlm-roberta-xl), [XLNet](../model_doc/xlnet), [X-MOD](../model_doc/xmod)
+
+
+<!--End of the generated tip-->
+
+</Tip>
+
+시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
+
+```bash
+pip install transformers datasets evaluate
+```
+
+커뮤니티에 모델을 업로드하고 공유하기 위해 Hugging Face 계정에 로그인하는 것을 권장합니다. 알림이 표시되면 토큰을 입력하여 로그인하세요:
+
+```py
+>>> from huggingface_hub import notebook_login
+
+>>> notebook_login()
+```
+
+## ELI5 데이터 세트 불러오기[[load-eli5-dataset]]
+
+먼저, 🤗 Datasets 라이브러리에서 r/askscience의 작은 하위 집합인 ELI5 데이터 세트를 불러옵니다.
+이를 통해 전체 데이터 세트에서 학습하는 데 더 많은 시간을 투자하기 전에, 실험해봄으로써 모든 것이 작동하는지 확인할 수 있습니다.
+
+```py
+>>> from datasets import load_dataset
+
+>>> eli5 = load_dataset("eli5", split="train_asks[:5000]")
+```
+
+데이터 세트의 `train_asks` 분할을 [`~datasets.Dataset.train_test_split`] 메소드를 사용하여 학습 및 테스트 세트로 분할합니다:
+
+```py
+>>> eli5 = eli5.train_test_split(test_size=0.2)
+```
+
+그런 다음 예제를 살펴보세요:
+
+```py
+>>> eli5["train"][0]
+{'answers': {'a_id': ['c3d1aib', 'c3d4lya'],
+  'score': [6, 3],
+  'text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
+   "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"]},
+ 'answers_urls': {'url': []},
+ 'document': '',
+ 'q_id': 'nyxfp',
+ 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
+ 'selftext_urls': {'url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg']},
+ 'subreddit': 'askscience',
+ 'title': 'Few questions about this space walk photograph.',
+ 'title_urls': {'url': []}}
+```
+
+많아 보일 수 있지만, 실제로는 `text` 필드만 중요합니다. 언어 모델링 작업의 장점은 레이블이 필요하지 않다는 것입니다. 다음 단어 *자체가* 레이블입니다. (이렇게 레이블을 제공하지 않아도 되는 학습을 비지도 학습이라고 일컫습니다)
+
+## 전처리[[preprocess]]
+
+<Youtube id="ma1TrR7gE7I"/>
+
+다음 단계는 `text` 필드를 전처리하기 위해 DistilGPT2 토크나이저를 불러오는 것입니다.
+
+```py
+>>> from transformers import AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+```
+
+위의 예제에서 알 수 있듯이, `text` 필드는 `answers` 아래에 중첩되어 있습니다. 따라서 [`flatten`](https://huggingface.co/docs/datasets/process.html#flatten) 메소드를 사용하여 중첩 구조에서 `text` 하위 필드를 추출해야 합니다.
+
+```py
+>>> eli5 = eli5.flatten()
+>>> eli5["train"][0]
+{'answers.a_id': ['c3d1aib', 'c3d4lya'],
+ 'answers.score': [6, 3],
+ 'answers.text': ["The velocity needed to remain in orbit is equal to the square root of Newton's constant times the mass of earth divided by the distance from the center of the earth. I don't know the altitude of that specific mission, but they're usually around 300 km. That means he's going 7-8 km/s.\n\nIn space there are no other forces acting on either the shuttle or the guy, so they stay in the same position relative to each other. If he were to become unable to return to the ship, he would presumably run out of oxygen, or slowly fall into the atmosphere and burn up.",
+  "Hope you don't mind me asking another question, but why aren't there any stars visible in this photo?"],
+ 'answers_urls.url': [],
+ 'document': '',
+ 'q_id': 'nyxfp',
+ 'selftext': '_URL_0_\n\nThis was on the front page earlier and I have a few questions about it. Is it possible to calculate how fast the astronaut would be orbiting the earth? Also how does he stay close to the shuttle so that he can return safely, i.e is he orbiting at the same speed and can therefore stay next to it? And finally if his propulsion system failed, would he eventually re-enter the atmosphere and presumably die?',
+ 'selftext_urls.url': ['http://apod.nasa.gov/apod/image/1201/freeflyer_nasa_3000.jpg'],
+ 'subreddit': 'askscience',
+ 'title': 'Few questions about this space walk photograph.',
+ 'title_urls.url': []}
+```
+
+각 하위 필드는 이제 `answers` 접두사를 가진 별도의 열로 나뉘었으며, `text` 필드는 이제 리스트입니다. 각 문장을 개별적으로 토큰화하는 대신, 먼저 리스트를 문자열로 변환하여 한꺼번에 토큰화할 수 있습니다.
+
+다음은 문자열 리스트를 결합하고 결과를 토큰화하는 첫 번째 전처리 함수입니다:
+
+```py
+>>> def preprocess_function(examples):
+...     return tokenizer([" ".join(x) for x in examples["answers.text"]])
+```
+
+이 전처리 함수를 전체 데이터 세트에 적용하려면 🤗 Datasets [`~datasets.Dataset.map`] 메소드를 사용하세요. `batched=True`로 설정하여 데이터셋의 여러 요소를 한 번에 처리하고, `num_proc`를 증가시켜 프로세스 수를 늘릴 수 있습니다. 필요 없는 열은 제거하세요:
+
+```py
+>>> tokenized_eli5 = eli5.map(
+...     preprocess_function,
+...     batched=True,
+...     num_proc=4,
+...     remove_columns=eli5["train"].column_names,
+... )
+```
+
+이제 데이터 세트는 시퀀스가 토큰화됐지만, 일부 시퀀스는 모델의 최대 입력 길이보다 길 수 있습니다.
+
+이제 두 번째 전처리 함수를 사용하여
+- 모든 시퀀스를 연결하고,
+- `block_size`로 정의된 길이로 연결된 시퀀스를 여러 개의 짧은 묶음으로 나눕니다. 이 값은 최대 입력 길이와 GPU RAM을 고려해 충분히 짧아야 합니다.
+
+```py
+>>> block_size = 128
+
+
+>>> def group_texts(examples):
+...     # Concatenate all texts.
+...     concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+...     total_length = len(concatenated_examples[list(examples.keys())[0]])
+...     # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
+...     # customize this part to your needs.
+...     if total_length >= block_size:
+...         total_length = (total_length // block_size) * block_size
+...     # Split by chunks of block_size.
+...     result = {
+...         k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
+...         for k, t in concatenated_examples.items()
+...     }
+...     result["labels"] = result["input_ids"].copy()
+...     return result
+```
+
+전체 데이터 세트에 `group_texts` 함수를 적용하세요:
+
+```py
+>>> lm_dataset = tokenized_eli5.map(group_texts, batched=True, num_proc=4)
+```
+
+그런 다음 [`DataCollatorForLanguageModeling`]을 사용하여 예제의 배치를 만듭니다. 데이터 세트 전체를 최대 길이로 패딩하는 것보다, 취합 단계에서 각 배치의 최대 길이로 문장을 *동적으로 패딩*하는 것이 더 효율적입니다.
+
+<frameworkcontent>
+<pt>
+패딩 토큰으로 종결 토큰을 사용하고 `mlm=False`로 설정하세요. 이렇게 하면 입력을 오른쪽으로 한 칸씩 시프트한 값을 레이블로 사용합니다:
+
+```py
+>>> from transformers import DataCollatorForLanguageModeling
+
+>>> tokenizer.pad_token = tokenizer.eos_token
+>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+```
+
+</pt>
+<tf>
+패딩 토큰으로 종결 토큰을 사용하고 `mlm=False`로 설정하세요. 이렇게 하면 입력을 오른쪽으로 한 칸씩 시프트한 값을 레이블로 사용합니다:
+
+```py
+>>> from transformers import DataCollatorForLanguageModeling
+
+>>> data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False, return_tensors="tf")
+```
+
+</tf>
+</frameworkcontent>
+
+
+## 훈련[[train]]
+
+<frameworkcontent>
+<pt>
+<Tip>
+
+[`Trainer`]를 사용하여 모델을 미세 조정하는 방법을 잘 모르신다면 [기본 튜토리얼](../training#train-with-pytorch-trainer)을 확인해보세요!
+
+</Tip>
+
+이제 모델을 훈련하기 준비가 되었습니다! [`AutoModelForCausalLM`]를 사용하여 DistilGPT2를 불러옵니다:
+
+```py
+>>> from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
+
+>>> model = AutoModelForCausalLM.from_pretrained("distilgpt2")
+```
+
+여기까지 진행하면 세 단계만 남았습니다:
+
+1. [`TrainingArguments`]에서 훈련 하이퍼파라미터를 정의하세요. `output_dir`은 유일한 필수 매개변수로, 모델을 저장할 위치를 지정합니다. (먼저 Hugging Face에 로그인 필수) `push_to_hub=True`로 설정하여 이 모델을 허브에 업로드할 수 있습니다.
+2. 훈련 인수를 [`Trainer`]에 모델, 데이터 세트 및 데이터 콜레이터와 함께 전달하세요.
+3. [`~Trainer.train`]을 호출하여 모델을 미세 조정하세요.
+
+```py
+>>> training_args = TrainingArguments(
+...     output_dir="my_awesome_eli5_clm-model",
+...     evaluation_strategy="epoch",
+...     learning_rate=2e-5,
+...     weight_decay=0.01,
+...     push_to_hub=True,
+... )
+
+>>> trainer = Trainer(
+...     model=model,
+...     args=training_args,
+...     train_dataset=lm_dataset["train"],
+...     eval_dataset=lm_dataset["test"],
+...     data_collator=data_collator,
+... )
+
+>>> trainer.train()
+```
+
+훈련이 완료되면 [`~transformers.Trainer.evaluate`] 메소드를 사용하여 모델을 평가하고 퍼플렉서티를 얻을 수 있습니다:
+
+```py
+>>> import math
+
+>>> eval_results = trainer.evaluate()
+>>> print(f"Perplexity: {math.exp(eval_results['eval_loss']):.2f}")
+Perplexity: 49.61
+```
+
+그런 다음 [`~transformers.Trainer.push_to_hub`] 메소드를 사용하여 모델을 허브에 공유하세요. 이렇게 하면 누구나 모델을 사용할 수 있습니다:
+
+```py
+>>> trainer.push_to_hub()
+```
+</pt>
+<tf>
+<Tip>
+
+Keras를 사용하여 모델을 미세 조정하는 방법에 익숙하지 않다면 [기본 튜토리얼](../training#train-a-tensorflow-model-with-keras)을 확인해보세요!
+
+</Tip>
+TensorFlow에서 모델을 미세 조정하려면, 먼저 옵티마이저 함수, 학습률 스케줄 및 일부 훈련 하이퍼파라미터를 설정하세요:
+
+```py
+>>> from transformers import create_optimizer, AdamWeightDecay
+
+>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
+```
+
+그런 다음 [`TFAutoModelForCausalLM`]를 사용하여 DistilGPT2를 불러옵니다:
+
+```py
+>>> from transformers import TFAutoModelForCausalLM
+
+>>> model = TFAutoModelForCausalLM.from_pretrained("distilgpt2")
+```
+
+[`~transformers.TFPreTrainedModel.prepare_tf_dataset`]을 사용하여 데이터 세트를 `tf.data.Dataset` 형식으로 변환하세요:
+
+```py
+>>> tf_train_set = model.prepare_tf_dataset(
+...     lm_dataset["train"],
+...     shuffle=True,
+...     batch_size=16,
+...     collate_fn=data_collator,
+... )
+
+>>> tf_test_set = model.prepare_tf_dataset(
+...     lm_dataset["test"],
+...     shuffle=False,
+...     batch_size=16,
+...     collate_fn=data_collator,
+... )
+```
+
+[`compile`](https://keras.io/api/models/model_training_apis/#compile-method)을 사용하여 모델을 훈련하기 위해 구성하세요. Transformers 모델은 모두 기본적인 작업 관련 손실 함수를 가지고 있으므로, 원한다면 별도로 지정하지 않아도 됩니다:
+
+```py
+>>> import tensorflow as tf
+
+>>> model.compile(optimizer=optimizer)  # 별도로 loss 인자를 넣지 않았어요!
+```
+
+[`~transformers.PushToHubCallback`]에서 모델과 토크나이저를 업로드할 위치를 지정할 수 있습니다:
+
+```py
+>>> from transformers.keras_callbacks import PushToHubCallback
+
+>>> callback = PushToHubCallback(
+...     output_dir="my_awesome_eli5_clm-model",
+...     tokenizer=tokenizer,
+... )
+```
+
+마지막으로, 모델을 훈련하기 위해 [`fit`](https://keras.io/api/models/model_training_apis/#fit-method)을 호출하세요. 훈련 데이터 세트, 검증 데이터 세트, 에폭 수 및 콜백을 전달하세요:
+
+```py
+>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3, callbacks=[callback])
+```
+
+훈련이 완료되면 모델이 자동으로 허브에 업로드되어 모두가 사용할 수 있습니다!
+</tf>
+</frameworkcontent>
+
+<Tip>
+
+인과 언어 모델링을 위해 모델을 미세 조정하는 더 자세한 예제는 해당하는 [PyTorch 노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling.ipynb) 또는 [TensorFlow 노트북](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/language_modeling-tf.ipynb)을 참조하세요.
+
+</Tip>
+
+## 추론[[inference]]
+
+좋아요, 이제 모델을 미세 조정했으므로 추론에 사용할 수 있습니다!
+
+생성할 텍스트를 위한 프롬프트를 만들어보세요:
+
+```py
+>>> prompt = "Somatic hypermutation allows the immune system to"
+```
+
+추론을 위해 미세 조정된 모델을 간단히 사용하는 가장 간단한 방법은 [`pipeline`]에서 사용하는 것입니다. 모델과 함께 텍스트 생성을 위한 `pipeline`을 인스턴스화하고 텍스트를 전달하세요:
+
+```py
+>>> from transformers import pipeline
+
+>>> generator = pipeline("text-generation", model="my_awesome_eli5_clm-model")
+>>> generator(prompt)
+[{'generated_text': "Somatic hypermutation allows the immune system to be able to effectively reverse the damage caused by an infection.\n\n\nThe damage caused by an infection is caused by the immune system's ability to perform its own self-correcting tasks."}]
+```
+
+<frameworkcontent>
+<pt>
+텍스트를 토큰화하고 `input_ids`를 PyTorch 텐서로 반환하세요:
+
+```py
+>>> from transformers import AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model")
+>>> inputs = tokenizer(prompt, return_tensors="pt").input_ids
+```
+
+[`~transformers.generation_utils.GenerationMixin.generate`] 메소드를 사용하여 텍스트를 생성하세요. 생성을 제어하는 다양한 텍스트 생성 전략과 매개변수에 대한 자세한 내용은 [텍스트 생성 전략](../generation_strategies) 페이지를 확인하세요.
+
+```py
+>>> from transformers import AutoModelForCausalLM
+
+>>> model = AutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model")
+>>> outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
+```
+
+생성된 토큰 ID를 다시 텍스트로 디코딩하세요:
+
+```py
+>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+["Somatic hypermutation allows the immune system to react to drugs with the ability to adapt to a different environmental situation. In other words, a system of 'hypermutation' can help the immune system to adapt to a different environmental situation or in some cases even a single life. In contrast, researchers at the University of Massachusetts-Boston have found that 'hypermutation' is much stronger in mice than in humans but can be found in humans, and that it's not completely unknown to the immune system. A study on how the immune system"]
+```
+</pt>
+<tf>
+텍스트를 토큰화하고 `input_ids`를 TensorFlow 텐서로 반환하세요:
+
+```py
+>>> from transformers import AutoTokenizer
+
+>>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_eli5_clm-model")
+>>> inputs = tokenizer(prompt, return_tensors="tf").input_ids
+```
+
+[`~transformers.generation_tf_utils.TFGenerationMixin.generate`] 메소드를 사용하여 요약을 생성하세요. 생성을 제어하는 다양한 텍스트 생성 전략과 매개변수에 대한 자세한 내용은 [텍스트 생성 전략](../generation_strategies) 페이지를 확인하세요.
+
+```py
+>>> from transformers import TFAutoModelForCausalLM
+
+>>> model = TFAutoModelForCausalLM.from_pretrained("my_awesome_eli5_clm-model")
+>>> outputs = model.generate(input_ids=inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
+```
+
+생성된 토큰 ID를 다시 텍스트로 디코딩하세요:
+
+```py
+>>> tokenizer.batch_decode(outputs, skip_special_tokens=True)
+['Somatic hypermutation allows the immune system to detect the presence of other viruses as they become more prevalent. Therefore, researchers have identified a high proportion of human viruses. The proportion of virus-associated viruses in our study increases with age. Therefore, we propose a simple algorithm to detect the presence of these new viruses in our samples as a sign of improved immunity. A first study based on this algorithm, which will be published in Science on Friday, aims to show that this finding could translate into the development of a better vaccine that is more effective for']
+```
+</tf>
+</frameworkcontent>

docs/source/ko/tasks/monocular_depth_estimation.mdx

@@ -0,0 +1,145 @@
+<!--Copyright 2023 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.
+-->
+
+# 단일 영상 기반 깊이 추정[[depth-estimation-pipeline]]
+
+단일 영상 기반 깊이 추정은 한 장면의 단일 이미지에서 장면의 깊이 정보를 예측하는 컴퓨터 비전 작업입니다.
+즉, 단일 카메라 시점의 장면에 있는 물체의 거리를 예측하는 과정입니다.
+
+단일 영상 기반 깊이 추정은 3D 재구성, 증강 현실, 자율 주행, 로봇 공학 등 다양한 분야에서 응용됩니다. 
+조명 조건, 가려짐, 텍스처와 같은 요소의 영향을 받을 수 있는 장면 내 물체와 해당 깊이 정보 간의 복잡한 관계를 모델이 이해해야 하므로 까다로운 작업입니다.
+
+
+<Tip>
+이 튜토리얼에서 다루는 작업은 다음 모델 아키텍처에서 지원됩니다:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[DPT](../model_doc/dpt), [GLPN](../model_doc/glpn)
+
+<!--End of the generated tip-->
+
+</Tip>
+
+이번 가이드에서 배울 내용은 다음과 같습니다:
+
+* 깊이 추정 파이프라인 만들기
+* 직접 깊이 추정 추론하기
+
+시작하기 전에, 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
+
+```bash
+pip install -q transformers
+```
+
+## 깊이 추정 파이프라인[[depth-estimation-inference-by-hand]]
+
+깊이 추정을 추론하는 가장 간단한 방법은 해당 기능을 제공하는 [`pipeline`]을 사용하는 것입니다.
+[Hugging Face Hub 체크포인트](https://huggingface.co/models?pipeline_tag=depth-estimation&sort=downloads)에서 파이프라인을 초기화합니다:
+
+```py
+>>> from transformers import pipeline
+
+>>> checkpoint = "vinvino02/glpn-nyu"
+>>> depth_estimator = pipeline("depth-estimation", model=checkpoint)
+```
+
+
+다음으로, 분석할 이미지를 한 장 선택하세요:
+
+```py
+>>> from PIL import Image
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-estimation-example.jpg" alt="Photo of a busy street"/>
+</div>
+
+이미지를 파이프라인으로 전달합니다.
+
+```py
+>>> predictions = depth_estimator(image)
+```
+
+파이프라인은 두 개의 항목을 가지는 딕셔너리를 반환합니다.
+첫 번째는 `predicted_depth`로 각 픽셀의 깊이를 미터로 표현한 값을 가지는 텐서입니다.
+두 번째는 `depth`로 깊이 추정 결과를 시각화하는 PIL 이미지입니다.
+
+이제 시각화한 결과를 살펴보겠습니다:
+
+```py
+>>> predictions["depth"]
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>
+</div>
+
+## 직접 깊이 추정 추론하기[[depth-estimation-inference-by-hand]]
+
+이제 깊이 추정 파이프라인 사용법을 살펴보았으니 동일한 결과를 복제하는 방법을 살펴보겠습니다.
+[Hugging Face Hub 체크포인트](https://huggingface.co/models?pipeline_tag=depth-estimation&sort=downloads)에서 모델과 관련 프로세서를 가져오는 것부터 시작합니다.
+여기서 이전에 사용한 체크포인트와 동일한 것을 사용합니다:
+
+```py
+>>> from transformers import AutoImageProcessor, AutoModelForDepthEstimation
+
+>>> checkpoint = "vinvino02/glpn-nyu"
+
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
+>>> model = AutoModelForDepthEstimation.from_pretrained(checkpoint)
+```
+
+필요한 이미지 변환을 처리하는 `image_processor`를 사용하여 모델에 대한 이미지 입력을 준비합니다.
+`image_processor`는 크기 조정 및 정규화 등 필요한 이미지 변환을 처리합니다:
+
+```py
+>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
+```
+
+준비한 입력을 모델로 전달합니다:
+
+```py
+>>> import torch
+
+>>> with torch.no_grad():
+...     outputs = model(pixel_values)
+...     predicted_depth = outputs.predicted_depth
+```
+
+결과를 시각화합니다:
+
+```py
+>>> import numpy as np
+
+>>> # 원본 사이즈로 복원
+>>> prediction = torch.nn.functional.interpolate(
+...     predicted_depth.unsqueeze(1),
+...     size=image.size[::-1],
+...     mode="bicubic",
+...     align_corners=False,
+... ).squeeze()
+>>> output = prediction.numpy()
+
+>>> formatted = (output * 255 / np.max(output)).astype("uint8")
+>>> depth = Image.fromarray(formatted)
+>>> depth
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/depth-visualization.png" alt="Depth estimation visualization"/>
+</div>

docs/source/ko/tasks/object_detection.mdx

@@ -0,0 +1,585 @@
+<!--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.
+-->
+
+# 객체 탐지 [[object-detection]]
+
+[[open-in-colab]]
+
+객체 탐지는 이미지에서 인스턴스(예: 사람, 건물 또는 자동차)를 감지하는 컴퓨터 비전 작업입니다. 객체 탐지 모델은 이미지를 입력으로 받고 탐지된 바운딩 박스의 좌표와 관련된 레이블을 출력합니다. 
+하나의 이미지에는 여러 객체가 있을 수 있으며 각각은 자체적인 바운딩 박스와 레이블을 가질 수 있습니다(예: 차와 건물이 있는 이미지). 
+또한 각 객체는 이미지의 다른 부분에 존재할 수 있습니다(예: 이미지에 여러 대의 차가 있을 수 있음). 
+이 작업은 보행자, 도로 표지판, 신호등과 같은 것들을 감지하는 자율 주행에 일반적으로 사용됩니다. 
+다른 응용 분야로는 이미지 내 객체 수 계산 및 이미지 검색 등이 있습니다.
+
+이 가이드에서 다음을 배울 것입니다:
+
+ 1. 합성곱 백본(인풋 데이터의 특성을 추출하는 합성곱 네트워크)과 인코더-디코더 트랜스포머 모델을 결합한 [DETR](https://huggingface.co/docs/transformers/model_doc/detr) 모델을 [CPPE-5](https://huggingface.co/datasets/cppe-5) 데이터 세트에 대해 미세조정 하기
+ 2. 미세조정 한 모델을 추론에 사용하기.
+
+<Tip>
+이 튜토리얼의 태스크는 다음 모델 아키텍처에서 지원됩니다:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[Conditional DETR](../model_doc/conditional_detr), [Deformable DETR](../model_doc/deformable_detr), [DETA](../model_doc/deta), [DETR](../model_doc/detr), [Table Transformer](../model_doc/table-transformer), [YOLOS](../model_doc/yolos)
+
+<!--End of the generated tip-->
+
+</Tip>
+
+시작하기 전에 필요한 모든 라이브러리가 설치되어 있는지 확인하세요:
+```bash
+pip install -q datasets transformers evaluate timm albumentations
+```
+
+허깅페이스 허브에서 데이터 세트를 가져오기 위한 🤗 Datasets과 모델을 학습하기 위한 🤗 Transformers, 데이터를 증강하기 위한 `albumentations`를 사용합니다. 
+DETR 모델의 합성곱 백본을 가져오기 위해서는 현재 `timm`이 필요합니다.
+
+커뮤니티에 모델을 업로드하고 공유할 수 있도록 Hugging Face 계정에 로그인하는 것을 권장합니다. 프롬프트가 나타나면 토큰을 입력하여 로그인하세요:
+
+```py
+>>> from huggingface_hub import notebook_login
+
+>>> notebook_login()
+```
+
+## CPPE-5 데이터 세트 가져오기 [[load-the-CPPE-5-dataset]]
+
+[CPPE-5](https://huggingface.co/datasets/cppe-5) 데이터 세트는 COVID-19 대유행 상황에서 의료 전문인력 보호 장비(PPE)를 식별하는 어노테이션이 포함된 이미지를 담고 있습니다.
+
+데이터 세트를 가져오세요:
+
+```py
+>>> from datasets import load_dataset
+
+>>> cppe5 = load_dataset("cppe-5")
+>>> cppe5
+DatasetDict({
+    train: Dataset({
+        features: ['image_id', 'image', 'width', 'height', 'objects'],
+        num_rows: 1000
+    })
+    test: Dataset({
+        features: ['image_id', 'image', 'width', 'height', 'objects'],
+        num_rows: 29
+    })
+})
+```
+
+이 데이터 세트는 학습 세트 이미지 1,000개와 테스트 세트 이미지 29개를 갖고 있습니다.
+
+데이터에 익숙해지기 위해, 예시가 어떻게 구성되어 있는지 살펴보세요.
+
+```py
+>>> cppe5["train"][0]
+{'image_id': 15,
+ 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=943x663 at 0x7F9EC9E77C10>,
+ 'width': 943,
+ 'height': 663,
+ 'objects': {'id': [114, 115, 116, 117],
+  'area': [3796, 1596, 152768, 81002],
+  'bbox': [[302.0, 109.0, 73.0, 52.0],
+   [810.0, 100.0, 57.0, 28.0],
+   [160.0, 31.0, 248.0, 616.0],
+   [741.0, 68.0, 202.0, 401.0]],
+  'category': [4, 4, 0, 0]}}
+```
+
+데이터 세트에 있는 예시는 다음의 영역을 가지고 있습니다:
+
+- `image_id`: 예시 이미지 id
+- `image`: 이미지를 포함하는 `PIL.Image.Image` 객체
+- `width`: 이미지의 너비
+- `height`: 이미지의 높이
+- `objects`: 이미지 안의 객체들의 바운딩 박스 메타데이터를 포함하는 딕셔너리:
+  - `id`: 어노테이션 id
+  - `area`: 바운딩 박스의 면적
+  - `bbox`: 객체의 바운딩 박스 ([COCO 포맷](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco)으로)
+  - `category`: 객체의 카테고리, 가능한 값으로는 `Coverall (0)`, `Face_Shield (1)`, `Gloves (2)`, `Goggles (3)` 및 `Mask (4)` 가 포함됩니다.
+
+`bbox` 필드가 DETR 모델이 요구하는 COCO 형식을 따른다는 것을 알 수 있습니다. 
+그러나 `objects` 내부의 필드 그룹은 DETR이 요구하는 어노테이션 형식과 다릅니다. 따라서 이 데이터를 학습에 사용하기 전에 전처리를 적용해야 합니다.
+
+데이터를 더 잘 이해하기 위해서 데이터 세트에서 한 가지 예시를 시각화하세요.
+
+```py
+>>> import numpy as np
+>>> import os
+>>> from PIL import Image, ImageDraw
+
+>>> image = cppe5["train"][0]["image"]
+>>> annotations = cppe5["train"][0]["objects"]
+>>> draw = ImageDraw.Draw(image)
+
+>>> categories = cppe5["train"].features["objects"].feature["category"].names
+
+>>> id2label = {index: x for index, x in enumerate(categories, start=0)}
+>>> label2id = {v: k for k, v in id2label.items()}
+
+>>> for i in range(len(annotations["id"])):
+...     box = annotations["bbox"][i - 1]
+...     class_idx = annotations["category"][i - 1]
+...     x, y, w, h = tuple(box)
+...     draw.rectangle((x, y, x + w, y + h), outline="red", width=1)
+...     draw.text((x, y), id2label[class_idx], fill="white")
+
+>>> image
+```
+
+<div class="flex justify-center">
+    <img src="https://i.imgur.com/TdaqPJO.png" alt="CPPE-5 Image Example"/>
+</div>
+
+바운딩 박스와 연결된 레이블을 시각화하려면 데이터 세트의 메타 데이터, 특히 `category` 필드에서 레이블을 가져와야 합니다. 
+또한 레이블 ID를 레이블 클래스에 매핑하는 `id2label`과 반대로 매핑하는 `label2id` 딕셔너리를 만들어야 합니다. 
+모델을 설정할 때 이러한 매핑을 사용할 수 있습니다. 이러한 매핑은 허깅페이스 허브에서 모델을 공유했을 때 다른 사람들이 재사용할 수 있습니다.
+
+데이터를 더 잘 이해하기 위한 최종 단계로, 잠재적인 문제를 찾아보세요. 
+객체 감지를 위한 데이터 세트에서 자주 발생하는 문제 중 하나는 바운딩 박스가 이미지의 가장자리를 넘어가는 것입니다. 
+이러한 바운딩 박스를 "넘어가는 것(run away)"은 훈련 중에 오류를 발생시킬 수 있기에 이 단계에서 처리해야 합니다. 
+이 데이터 세트에도 같은 문제가 있는 몇 가지 예가 있습니다. 이 가이드에서는 간단하게하기 위해 데이터에서 이러한 이미지를 제거합니다.
+
+```py
+>>> remove_idx = [590, 821, 822, 875, 876, 878, 879]
+>>> keep = [i for i in range(len(cppe5["train"])) if i not in remove_idx]
+>>> cppe5["train"] = cppe5["train"].select(keep)
+```
+
+## 데이터 전처리하기 [[preprocess-the-data]]
+
+모델을 미세 조정 하려면, 미리 학습된 모델에서 사용한 전처리 방식과 정확하게 일치하도록 사용할 데이터를 전처리해야 합니다. 
+[`AutoImageProcessor`]는 이미지 데이터를 처리하여 DETR 모델이 학습에 사용할 수 있는 `pixel_values`, `pixel_mask`, 그리고 `labels`를 생성하는 작업을 담당합니다. 
+이 이미지 프로세서에는 걱정하지 않아도 되는 몇 가지 속성이 있습니다:
+
+- `image_mean = [0.485, 0.456, 0.406 ]`
+- `image_std = [0.229, 0.224, 0.225]`
+
+
+이 값들은 모델 사전 훈련 중 이미지를 정규화하는 데 사용되는 평균과 표준 편차입니다. 
+이 값들은 추론 또는 사전 훈련된 이미지 모델을 세밀하게 조정할 때 복제해야 하는 중요한 값입니다.
+
+사전 훈련된 모델과 동일한 체크포인트에서 이미지 프로세서를 인스턴스화합니다.
+
+```py
+>>> from transformers import AutoImageProcessor
+
+>>> checkpoint = "facebook/detr-resnet-50"
+>>> image_processor = AutoImageProcessor.from_pretrained(checkpoint)
+```
+
+`image_processor`에 이미지를 전달하기 전에, 데이터 세트에 두 가지 전처리를 적용해야 합니다:
+
+- 이미지 증강
+- DETR 모델의 요구에 맞게 어노테이션을 다시 포맷팅
+
+첫째로, 모델이 학습 데이터에 과적합 되지 않도록 데이터 증강 라이브러리 중 아무거나 사용하여 변환을 적용할 수 있습니다. 여기에서는 [Albumentations](https://albumentations.ai/docs/) 라이브러리를 사용합니다...
+이 라이브러리는 변환을 이미지에 적용하고 바운딩 박스를 적절하게 업데이트하도록 보장합니다.
+🤗 Datasets 라이브러리 문서에는 [객체 탐지를 위해 이미지를 보강하는 방법에 대한 자세한 가이드](https://huggingface.co/docs/datasets/object_detection)가 있으며, 
+이 예제와 정확히 동일한 데이터 세트를 사용합니다. 여기서는 각 이미지를 (480, 480) 크기로 조정하고, 좌우로 뒤집고, 밝기를 높이는 동일한 접근법을 적용합니다:
+
+
+```py
+>>> import albumentations
+>>> import numpy as np
+>>> import torch
+
+>>> transform = albumentations.Compose(
+...     [
+...         albumentations.Resize(480, 480),
+...         albumentations.HorizontalFlip(p=1.0),
+...         albumentations.RandomBrightnessContrast(p=1.0),
+...     ],
+...     bbox_params=albumentations.BboxParams(format="coco", label_fields=["category"]),
+... )
+```
+
+이미지 프로세서는 어노테이션이 다음과 같은 형식일 것으로 예상합니다: `{'image_id': int, 'annotations': List[Dict]}`, 여기서 각 딕셔너리는 COCO 객체 어노테이션입니다. 단일 예제에 대해 어노테이션의 형식을 다시 지정하는 함수를 추가해 보겠습니다:
+
+```py
+>>> def formatted_anns(image_id, category, area, bbox):
+...     annotations = []
+...     for i in range(0, len(category)):
+...         new_ann = {
+...             "image_id": image_id,
+...             "category_id": category[i],
+...             "isCrowd": 0,
+...             "area": area[i],
+...             "bbox": list(bbox[i]),
+...         }
+...         annotations.append(new_ann)
+
+...     return annotations
+```
+
+이제 이미지와 어노테이션 전처리 변환을 결합하여 예제 배치에 사용할 수 있습니다:
+
+```py
+>>> # transforming a batch
+>>> def transform_aug_ann(examples):
+...     image_ids = examples["image_id"]
+...     images, bboxes, area, categories = [], [], [], []
+...     for image, objects in zip(examples["image"], examples["objects"]):
+...         image = np.array(image.convert("RGB"))[:, :, ::-1]
+...         out = transform(image=image, bboxes=objects["bbox"], category=objects["category"])
+
+...         area.append(objects["area"])
+...         images.append(out["image"])
+...         bboxes.append(out["bboxes"])
+...         categories.append(out["category"])
+
+...     targets = [
+...         {"image_id": id_, "annotations": formatted_anns(id_, cat_, ar_, box_)}
+...         for id_, cat_, ar_, box_ in zip(image_ids, categories, area, bboxes)
+...     ]
+
+...     return image_processor(images=images, annotations=targets, return_tensors="pt")
+```
+
+이전 단계에서 만든 전처리 함수를 🤗 Datasets의 [`~datasets.Dataset.with_transform`] 메소드를 사용하여 데이터 세트 전체에 적용합니다.
+이 메소드는 데이터 세트의 요소를 가져올 때마다 전처리 함수를 적용합니다.
+
+이 시점에서는 전처리 후 데이터 세트에서 예시 하나를 가져와서 변환 후 모양이 어떻게 되는지 확인해 볼 수 있습니다.
+이때, `pixel_values` 텐서, `pixel_mask` 텐서, 그리고 `labels`로 구성된 텐서가 있어야 합니다.
+
+```py
+>>> cppe5["train"] = cppe5["train"].with_transform(transform_aug_ann)
+>>> cppe5["train"][15]
+{'pixel_values': tensor([[[ 0.9132,  0.9132,  0.9132,  ..., -1.9809, -1.9809, -1.9809],
+          [ 0.9132,  0.9132,  0.9132,  ..., -1.9809, -1.9809, -1.9809],
+          [ 0.9132,  0.9132,  0.9132,  ..., -1.9638, -1.9638, -1.9638],
+          ...,
+          [-1.5699, -1.5699, -1.5699,  ..., -1.9980, -1.9980, -1.9980],
+          [-1.5528, -1.5528, -1.5528,  ..., -1.9980, -1.9809, -1.9809],
+          [-1.5528, -1.5528, -1.5528,  ..., -1.9980, -1.9809, -1.9809]],
+
+         [[ 1.3081,  1.3081,  1.3081,  ..., -1.8431, -1.8431, -1.8431],
+          [ 1.3081,  1.3081,  1.3081,  ..., -1.8431, -1.8431, -1.8431],
+          [ 1.3081,  1.3081,  1.3081,  ..., -1.8256, -1.8256, -1.8256],
+          ...,
+          [-1.3179, -1.3179, -1.3179,  ..., -1.8606, -1.8606, -1.8606],
+          [-1.3004, -1.3004, -1.3004,  ..., -1.8606, -1.8431, -1.8431],
+          [-1.3004, -1.3004, -1.3004,  ..., -1.8606, -1.8431, -1.8431]],
+
+         [[ 1.4200,  1.4200,  1.4200,  ..., -1.6476, -1.6476, -1.6476],
+          [ 1.4200,  1.4200,  1.4200,  ..., -1.6476, -1.6476, -1.6476],
+          [ 1.4200,  1.4200,  1.4200,  ..., -1.6302, -1.6302, -1.6302],
+          ...,
+          [-1.0201, -1.0201, -1.0201,  ..., -1.5604, -1.5604, -1.5604],
+          [-1.0027, -1.0027, -1.0027,  ..., -1.5604, -1.5430, -1.5430],
+          [-1.0027, -1.0027, -1.0027,  ..., -1.5604, -1.5430, -1.5430]]]),
+ 'pixel_mask': tensor([[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],
+         [1, 1, 1,  ..., 1, 1, 1]]),
+ 'labels': {'size': tensor([800, 800]), 'image_id': tensor([756]), 'class_labels': tensor([4]), 'boxes': tensor([[0.7340, 0.6986, 0.3414, 0.5944]]), 'area': tensor([519544.4375]), 'iscrowd': tensor([0]), 'orig_size': tensor([480, 480])}}
+```
+
+각각의 이미지를 성공적으로 증강하고 이미지의 어노테이션을 준비했습니다. 
+그러나 전처리는 아직 끝나지 않았습니다. 마지막 단계로, 이미지를 배치로 만들 사용자 정의 `collate_fn`을 생성합니다.
+해당 배치에서 가장 큰 이미지에 이미지(현재 `pixel_values` 인)를 패드하고, 실제 픽셀(1)과 패딩(0)을 나타내기 위해 그에 해당하는 새로운 `pixel_mask`를 생성해야 합니다.
+
+```py
+>>> def collate_fn(batch):
+...     pixel_values = [item["pixel_values"] for item in batch]
+...     encoding = image_processor.pad_and_create_pixel_mask(pixel_values, return_tensors="pt")
+...     labels = [item["labels"] for item in batch]
+...     batch = {}
+...     batch["pixel_values"] = encoding["pixel_values"]
+...     batch["pixel_mask"] = encoding["pixel_mask"]
+...     batch["labels"] = labels
+...     return batch
+```
+
+## DETR 모델 학습시키기 [[training-the-DETR-model]]
+
+이전 섹션에서 대부분의 작업을 수행하여 이제 모델을 학습할 준비가 되었습니다!
+이 데이터 세트의 이미지는 리사이즈 후에도 여전히 용량이 크기 때문에, 이 모델을 미세 조정 하려면 적어도 하나의 GPU가 필요합니다.
+
+학습은 다음의 단계를 수행합니다:
+
+1. [`AutoModelForObjectDetection`]을 사용하여 전처리와 동일한 체크포인트를 사용하여 모델을 가져옵니다.
+2. [`TrainingArguments`]에서 학습 하이퍼파라미터를 정의합니다.
+3. 모델, 데이터 세트, 이미지 프로세서 및 데이터 콜레이터와 함께 [`Trainer`]에 훈련 인수를 전달합니다.
+4. [`~Trainer.train`]를 호출하여 모델을 미세 조정 합니다.
+
+전처리에 사용한 체크포인트와 동일한 체크포인트에서 모델을 가져올 때, 데이터 세트의 메타데이터에서 만든 `label2id`와 `id2label` 매핑을 전달해야 합니다. 
+또한, `ignore_mismatched_sizes=True`를 지정하여 기존 분류 헤드(모델에서 분류에 사용되는 마지막 레이어)를 새 분류 헤드로 대체합니다.
+
+```py
+>>> from transformers import AutoModelForObjectDetection
+
+>>> model = AutoModelForObjectDetection.from_pretrained(
+...     checkpoint,
+...     id2label=id2label,
+...     label2id=label2id,
+...     ignore_mismatched_sizes=True,
+... )
+```
+
+[`TrainingArguments`]에서 `output_dir`을 사용하여 모델을 저장할 위치를 지정한 다음, 필요에 따라 하이퍼파라미터를 구성하세요.
+사용하지 않는 열을 제거하지 않도록 주의해야 합니다. 만약 `remove_unused_columns`가 `True`일 경우 이미지 열이 삭제됩니다. 
+이미지 열이 없는 경우 `pixel_values`를 생성할 수 없기 때문에 `remove_unused_columns`를 `False`로 설정해야 합니다.
+모델을 Hub에 업로드하여 공유하려면 `push_to_hub`를 `True`로 설정하십시오(허깅페이스에 로그인하여 모델을 업로드해야 합니다).
+
+
+```py
+>>> from transformers import TrainingArguments
+
+>>> training_args = TrainingArguments(
+...     output_dir="detr-resnet-50_finetuned_cppe5",
+...     per_device_train_batch_size=8,
+...     num_train_epochs=10,
+...     fp16=True,
+...     save_steps=200,
+...     logging_steps=50,
+...     learning_rate=1e-5,
+...     weight_decay=1e-4,
+...     save_total_limit=2,
+...     remove_unused_columns=False,
+...     push_to_hub=True,
+... )
+```
+
+마지막으로 `model`, `training_args`, `collate_fn`, `image_processor`와 데이터 세트(`cppe5`)를 모두 가져온 후, [`~transformers.Trainer.train`]를 호출합니다.
+
+```py
+>>> from transformers import Trainer
+
+>>> trainer = Trainer(
+...     model=model,
+...     args=training_args,
+...     data_collator=collate_fn,
+...     train_dataset=cppe5["train"],
+...     tokenizer=image_processor,
+... )
+
+>>> trainer.train()
+```
+
+`training_args`에서 `push_to_hub`를 `True`로 설정한 경우, 학습 체크포인트는 허깅페이스 허브에 업로드됩니다. 
+학습 완료 후, [`~transformers.Trainer.push_to_hub`] 메소드를 호출하여 최종 모델을 허깅페이스 허브에 업로드합니다.
+
+```py
+>>> trainer.push_to_hub()
+```
+
+## 평가하기 [[evaluate]]
+
+객체 탐지 모델은 일반적으로 일련의 <a href="https://cocodataset.org/#detection-eval">COCO-스타일 지표</a>로 평가됩니다. 
+기존에 구현된 평가 지표 중 하나를 사용할 수도 있지만, 여기에서는 허깅페이스 허브에 푸시한 최종 모델을 평가하는 데 `torchvision`에서 제공하는 평가 지표를 사용합니다.
+
+`torchvision` 평가자(evaluator)를 사용하려면 실측값인 COCO 데이터 세트를 준비해야 합니다. 
+COCO 데이터 세트를 빌드하는 API는 데이터를 특정 형식으로 저장해야 하므로, 먼저 이미지와 어노테이션을 디스크에 저장해야 합니다. 
+학습을 위해 데이터를 준비할 때와 마찬가지로, cppe5["test"]에서의 어노테이션은 포맷을 맞춰야 합니다. 그러나 이미지는 그대로 유지해야 합니다.
+
+평가 단계는 약간의 작업이 필요하지만, 크게 세 가지 주요 단계로 나눌 수 있습니다. 
+먼저, `cppe5["test"]` 세트를 준비합니다: 어노테이션을 포맷에 맞게 만들고 데이터를 디스크에 저장합니다.
+
+```py
+>>> import json
+
+
+>>> # format annotations the same as for training, no need for data augmentation
+>>> def val_formatted_anns(image_id, objects):
+...     annotations = []
+...     for i in range(0, len(objects["id"])):
+...         new_ann = {
+...             "id": objects["id"][i],
+...             "category_id": objects["category"][i],
+...             "iscrowd": 0,
+...             "image_id": image_id,
+...             "area": objects["area"][i],
+...             "bbox": objects["bbox"][i],
+...         }
+...         annotations.append(new_ann)
+
+...     return annotations
+
+
+>>> # Save images and annotations into the files torchvision.datasets.CocoDetection expects
+>>> def save_cppe5_annotation_file_images(cppe5):
+...     output_json = {}
+...     path_output_cppe5 = f"{os.getcwd()}/cppe5/"
+
+...     if not os.path.exists(path_output_cppe5):
+...         os.makedirs(path_output_cppe5)
+
+...     path_anno = os.path.join(path_output_cppe5, "cppe5_ann.json")
+...     categories_json = [{"supercategory": "none", "id": id, "name": id2label[id]} for id in id2label]
+...     output_json["images"] = []
+...     output_json["annotations"] = []
+...     for example in cppe5:
+...         ann = val_formatted_anns(example["image_id"], example["objects"])
+...         output_json["images"].append(
+...             {
+...                 "id": example["image_id"],
+...                 "width": example["image"].width,
+...                 "height": example["image"].height,
+...                 "file_name": f"{example['image_id']}.png",
+...             }
+...         )
+...         output_json["annotations"].extend(ann)
+...     output_json["categories"] = categories_json
+
+...     with open(path_anno, "w") as file:
+...         json.dump(output_json, file, ensure_ascii=False, indent=4)
+
+...     for im, img_id in zip(cppe5["image"], cppe5["image_id"]):
+...         path_img = os.path.join(path_output_cppe5, f"{img_id}.png")
+...         im.save(path_img)
+
+...     return path_output_cppe5, path_anno
+```
+
+다음으로, `cocoevaluator`와 함께 사용할 수 있는 `CocoDetection` 클래스의 인스턴스를 준비합니다.
+
+```py
+>>> import torchvision
+
+
+>>> class CocoDetection(torchvision.datasets.CocoDetection):
+...     def __init__(self, img_folder, feature_extractor, ann_file):
+...         super().__init__(img_folder, ann_file)
+...         self.feature_extractor = feature_extractor
+
+...     def __getitem__(self, idx):
+...         # read in PIL image and target in COCO format
+...         img, target = super(CocoDetection, self).__getitem__(idx)
+
+...         # preprocess image and target: converting target to DETR format,
+...         # resizing + normalization of both image and target)
+...         image_id = self.ids[idx]
+...         target = {"image_id": image_id, "annotations": target}
+...         encoding = self.feature_extractor(images=img, annotations=target, return_tensors="pt")
+...         pixel_values = encoding["pixel_values"].squeeze()  # remove batch dimension
+...         target = encoding["labels"][0]  # remove batch dimension
+
+...         return {"pixel_values": pixel_values, "labels": target}
+
+
+>>> im_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5")
+
+>>> path_output_cppe5, path_anno = save_cppe5_annotation_file_images(cppe5["test"])
+>>> test_ds_coco_format = CocoDetection(path_output_cppe5, im_processor, path_anno)
+```
+
+마지막으로, 평가 지표를 가져와서 평가를 실행합니다.
+
+```py
+>>> import evaluate
+>>> from tqdm import tqdm
+
+>>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5")
+>>> module = evaluate.load("ybelkada/cocoevaluate", coco=test_ds_coco_format.coco)
+>>> val_dataloader = torch.utils.data.DataLoader(
+...     test_ds_coco_format, batch_size=8, shuffle=False, num_workers=4, collate_fn=collate_fn
+... )
+
+>>> with torch.no_grad():
+...     for idx, batch in enumerate(tqdm(val_dataloader)):
+...         pixel_values = batch["pixel_values"]
+...         pixel_mask = batch["pixel_mask"]
+
+...         labels = [
+...             {k: v for k, v in t.items()} for t in batch["labels"]
+...         ]  # these are in DETR format, resized + normalized
+
+...         # forward pass
+...         outputs = model(pixel_values=pixel_values, pixel_mask=pixel_mask)
+
+...         orig_target_sizes = torch.stack([target["orig_size"] for target in labels], dim=0)
+...         results = im_processor.post_process(outputs, orig_target_sizes)  # convert outputs of model to COCO api
+
+...         module.add(prediction=results, reference=labels)
+...         del batch
+
+>>> results = module.compute()
+>>> print(results)
+Accumulating evaluation results...
+DONE (t=0.08s).
+IoU metric: bbox
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.150
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.280
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.130
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.038
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.036
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.182
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.166
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.317
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.335
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.104
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.146
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.382
+```
+
+이러한 결과는 [`~transformers.TrainingArguments`]의 하이퍼파라미터를 조정하여 더욱 개선될 수 있습니다. 한번 시도해 보세요!
+
+## 추론하기 [[inference]]
+
+DETR 모델을 미세 조정 및 평가하고, 허깅페이스 허브에 업로드 했으므로 추론에 사용할 수 있습니다. 
+
+미세 조정된 모델을 추론에 사용하는 가장 간단한 방법은 [`pipeline`]에서 모델을 사용하는 것입니다. 
+모델과 함께 객체 탐지를 위한 파이프라인을 인스턴스화하고, 이미지를 전달하세요:
+
+```py
+>>> from transformers import pipeline
+>>> import requests
+
+>>> url = "https://i.imgur.com/2lnWoly.jpg"
+>>> image = Image.open(requests.get(url, stream=True).raw)
+
+>>> obj_detector = pipeline("object-detection", model="MariaK/detr-resnet-50_finetuned_cppe5")
+>>> obj_detector(image)
+```
+
+만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다:
+
+```py
+>>> image_processor = AutoImageProcessor.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5")
+>>> model = AutoModelForObjectDetection.from_pretrained("MariaK/detr-resnet-50_finetuned_cppe5")
+
+>>> with torch.no_grad():
+...     inputs = image_processor(images=image, return_tensors="pt")
+...     outputs = model(**inputs)
+...     target_sizes = torch.tensor([image.size[::-1]])
+...     results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[0]
+
+>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+...     box = [round(i, 2) for i in box.tolist()]
+...     print(
+...         f"Detected {model.config.id2label[label.item()]} with confidence "
+...         f"{round(score.item(), 3)} at location {box}"
+...     )
+Detected Coverall with confidence 0.566 at location [1215.32, 147.38, 4401.81, 3227.08]
+Detected Mask with confidence 0.584 at location [2449.06, 823.19, 3256.43, 1413.9]
+```
+
+결과를 시각화하겠습니다:
+```py
+>>> draw = ImageDraw.Draw(image)
+
+>>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+...     box = [round(i, 2) for i in box.tolist()]
+...     x, y, x2, y2 = tuple(box)
+...     draw.rectangle((x, y, x2, y2), outline="red", width=1)
+...     draw.text((x, y), model.config.id2label[label.item()], fill="white")
+
+>>> image
+```
+
+<div class="flex justify-center">
+    <img src="https://i.imgur.com/4QZnf9A.png" alt="Object detection result on a new image"/>
+</div>
+

docs/source/ko/tasks/video_classification.mdx

@@ -0,0 +1,494 @@
+<!--Copyright 2023 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.
+-->
+
+# 영상 분류 [[video-classification]]
+
+[[open-in-colab]]
+
+
+영상 분류는 영상 전체에 레이블 또는 클래스를 지정하는 작업입니다. 각 영상에는 하나의 클래스가 있을 것으로 예상됩니다. 영상 분류 모델은 영상을 입력으로 받아 어느 클래스에 속하는지에 대한 예측을 반환합니다. 이러한 모델은 영상이 어떤 내용인지 분류하는 데 사용될 수 있습니다. 영상 분류의 실제 응용 예는 피트니스 앱에서 유용한 동작 / 운동 인식 서비스가 있습니다. 이는 또한 시각 장애인이 이동할 때 보조하는데 사용될 수 있습니다
+
+이 가이드에서는 다음을 수행하는 방법을 보여줍니다:
+
+1. [UCF101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합을 통해 [VideoMAE](https://huggingface.co/docs/transformers/main/en/model_doc/videomae) 모델을 미세 조정하기.
+2. 미세 조정한 모델을 추론에 사용하기.
+
+<Tip>
+
+이 튜토리얼에서 설명하는 작업은 다음 모델 아키텍처에서 지원됩니다:
+
+<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
+
+[TimeSformer](../model_doc/timesformer), [VideoMAE](../model_doc/videomae)
+
+<!--End of the generated tip-->
+
+</Tip>
+
+
+시작하기 전에 필요한 모든 라이브러리가 설치되었는지 확인하세요:
+```bash
+pip install -q pytorchvideo transformers evaluate
+```
+
+영상을 처리하고 준비하기 위해 [PyTorchVideo](https://pytorchvideo.org/)(이하 `pytorchvideo`)를 사용합니다.
+
+커뮤니티에 모델을 업로드하고 공유할 수 있도록 Hugging Face 계정에 로그인하는 것을 권장합니다. 프롬프트가 나타나면 토큰을 입력하여 로그인하세요:
+
+```py
+>>> from huggingface_hub import notebook_login
+
+>>> notebook_login()
+```
+
+## UCF101 데이터셋 불러오기 [[load-ufc101-dataset]]
+
+[UCF-101](https://www.crcv.ucf.edu/data/UCF101.php) 데이터 세트의 하위 집합(subset)을 불러오는 것으로 시작할 수 있습니다. 전체 데이터 세트를 학습하는데 더 많은 시간을 할애하기 전에 데이터의 하위 집합을 불러와 모든 것이 잘 작동하는지 실험하고 확인할 수 있습니다.
+
+```py
+>>> from huggingface_hub import hf_hub_download
+
+>>> hf_dataset_identifier = "sayakpaul/ucf101-subset"
+>>> filename = "UCF101_subset.tar.gz"
+>>> file_path = hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type="dataset")
+```
+
+데이터 세트의 하위 집합이 다운로드 되면, 압축된 파일의 압축을 해제해야 합니다:
+```py 
+>>> import tarfile
+
+>>> with tarfile.open(file_path) as t:
+...      t.extractall(".")
+```
+
+전체 데이터 세트는 다음과 같이 구성되어 있습니다.
+
+```bash
+UCF101_subset/
+    train/
+        BandMarching/
+            video_1.mp4
+            video_2.mp4
+            ...
+        Archery
+            video_1.mp4
+            video_2.mp4
+            ...
+        ...
+    val/
+        BandMarching/
+            video_1.mp4
+            video_2.mp4
+            ...
+        Archery
+            video_1.mp4
+            video_2.mp4
+            ...
+        ...
+    test/
+        BandMarching/
+            video_1.mp4
+            video_2.mp4
+            ...
+        Archery
+            video_1.mp4
+            video_2.mp4
+            ...
+        ...
+```
+
+
+정렬된 영상의 경로는 다음과 같습니다:
+
+```bash
+...
+'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c04.avi',
+'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c06.avi',
+'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c01.avi',
+'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c02.avi',
+'UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c06.avi'
+...
+```
+
+동일한 그룹/장면에 속하는 영상 클립은 파일 경로에서 `g`로 표시되어 있습니다. 예를 들면, `v_ApplyEyeMakeup_g07_c04.avi`와 `v_ApplyEyeMakeup_g07_c06.avi` 이 있습니다. 이 둘은 같은 그룹입니다.
+
+검증 및 평가 데이터 분할을 할 때, [데이터 누출(data leakage)](https://www.kaggle.com/code/alexisbcook/data-leakage)을 방지하기 위해 동일한 그룹 / 장면의 영상 클립을 사용하지 않아야 합니다. 이 튜토리얼에서 사용하는 하위 집합은 이러한 정보를 고려하고 있습니다.
+
+그 다음으로, 데이터 세트에 존재하는 라벨을 추출합니다. 또한, 모델을 초기화할 때 도움이 될 딕셔너리(dictionary data type)를 생성합니다.
+
+* `label2id`: 클래스 이름을 정수에 매핑합니다.
+* `id2label`: 정수를 클래스 이름에 매핑합니다. 
+
+```py 
+>>> class_labels = sorted({str(path).split("/")[2] for path in all_video_file_paths})
+>>> label2id = {label: i for i, label in enumerate(class_labels)}
+>>> id2label = {i: label for label, i in label2id.items()}
+
+>>> print(f"Unique classes: {list(label2id.keys())}.")
+
+# Unique classes: ['ApplyEyeMakeup', 'ApplyLipstick', 'Archery', 'BabyCrawling', 'BalanceBeam', 'BandMarching', 'BaseballPitch', 'Basketball', 'BasketballDunk', 'BenchPress'].
+```
+
+이 데이터 세트에는 총 10개의 고유한 클래스가 있습니다. 각 클래스마다 30개의 영상이 훈련 세트에 있습니다
+
+## 미세 조정하기 위해 모델 가져오기 [[load-a-model-to-fine-tune]]
+
+사전 훈련된 체크포인트와 체크포인트에 연관된 이미지 프로세서를 사용하여 영상 분류 모델을 인스턴스화합니다. 모델의 인코더에는 미리 학습된 매개변수가 제공되며, 분류 헤드(데이터를 분류하는 마지막 레이어)는 무작위로 초기화됩니다. 데이터 세트의 전처리 파이프라인을 작성할 때는 이미지 프로세서가 유용합니다.
+
+```py 
+>>> from transformers import VideoMAEImageProcessor, VideoMAEForVideoClassification
+
+>>> model_ckpt = "MCG-NJU/videomae-base"
+>>> image_processor = VideoMAEImageProcessor.from_pretrained(model_ckpt)
+>>> model = VideoMAEForVideoClassification.from_pretrained(
+...     model_ckpt,
+...     label2id=label2id,
+...     id2label=id2label,
+...     ignore_mismatched_sizes=True,  # provide this in case you're planning to fine-tune an already fine-tuned checkpoint
+... )
+```
+
+모델을 가져오는 동안, 다음과 같은 경고를 마주칠 수 있습니다:
+
+```bash
+Some weights of the model checkpoint at MCG-NJU/videomae-base were not used when initializing VideoMAEForVideoClassification: [..., 'decoder.decoder_layers.1.attention.output.dense.bias', 'decoder.decoder_layers.2.attention.attention.key.weight']
+- This IS expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
+- This IS NOT expected if you are initializing VideoMAEForVideoClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
+Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: ['classifier.bias', 'classifier.weight']
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+```
+
+
+위 경고는 우리가 일부 가중치(예: `classifier` 층의 가중치와 편향)를 버리고 새로운 `classifier` 층의 가중치와 편향을 무작위로 초기화하고 있다는 것을 알려줍니다. 이 경우에는 미리 학습된 가중치가 없는 새로운 헤드를 추가하고 있으므로, 라이브러리가 모델을 추론에 사용하기 전에 미세 조정하라고 경고를 보내는 것은 당연합니다. 그리고 이제 우리는 이 모델을 미세 조정할 예정입니다.
+
+**참고** 이 [체크포인트](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics)는 도메인이 많이 중첩된 유사한 다운스트림 작업에 대해 미세 조정하여 얻은 체크포인트이므로 이 작업에서 더 나은 성능을 보일 수 있습니다. `MCG-NJU/videomae-base-finetuned-kinetics` 데이터 세트를 미세 조정하여 얻은 [체크포인트](https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset)도 있습니다.
+
+## 훈련을 위한 데이터 세트 준비하기[[prepare-the-datasets-for-training]]
+
+영상 전처리를 위해 [PyTorchVideo 라이브러리](https://pytorchvideo.org/)를 활용할 것입니다. 필요한 종속성을 가져오는 것으로 시작하세요.
+
+```py 
+>>> import pytorchvideo.data
+
+>>> from pytorchvideo.transforms import (
+...     ApplyTransformToKey,
+...     Normalize,
+...     RandomShortSideScale,
+...     RemoveKey,
+...     ShortSideScale,
+...     UniformTemporalSubsample,
+... )
+
+>>> from torchvision.transforms import (
+...     Compose,
+...     Lambda,
+...     RandomCrop,
+...     RandomHorizontalFlip,
+...     Resize,
+... )
+```
+
+학습 데이터 세트 변환에는 '균일한 시간 샘플링(uniform temporal subsampling)', '픽셀 정규화(pixel normalization)', '랜덤 잘라내기(random cropping)' 및 '랜덤 수평 뒤집기(random horizontal flipping)'의 조합을 사용합니다. 검증 및 평가 데이터 세트 변환에는 '랜덤 잘라내기'와 '랜덤 뒤집기'를 제외한 동일한 변환 체인을 유지합니다. 이러한 변환에 대해 자세히 알아보려면 [PyTorchVideo 공식 문서](https://pytorchvideo.org)를 확인하세요.
+
+사전 훈련된 모델과 관련된 이미지 프로세서를 사용하여 다음 정보를 얻을 수 있습니다:
+
+* 영상 프레임 픽셀을 정규화하는 데 사용되는 이미지 평균과 표준 편차
+* 영상 프레임이 조정될 공간 해상도
+
+
+먼저, 몇 가지 상수를 정의합니다.
+
+```py
+>>> mean = image_processor.image_mean
+>>> std = image_processor.image_std
+>>> if "shortest_edge" in image_processor.size:
+...     height = width = image_processor.size["shortest_edge"]
+>>> else:
+...     height = image_processor.size["height"]
+...     width = image_processor.size["width"]
+>>> resize_to = (height, width)
+
+>>> num_frames_to_sample = model.config.num_frames
+>>> sample_rate = 4
+>>> fps = 30
+>>> clip_duration = num_frames_to_sample * sample_rate / fps
+```
+
+이제 데이터 세트에 특화된 전처리(transform)과 데이터 세트 자체를 정의합니다. 먼저 훈련 데이터 세트로 시작합니다:
+
+```py 
+>>> train_transform = Compose(
+...     [
+...         ApplyTransformToKey(
+...             key="video",
+...             transform=Compose(
+...                 [
+...                     UniformTemporalSubsample(num_frames_to_sample),
+...                     Lambda(lambda x: x / 255.0),
+...                     Normalize(mean, std),
+...                     RandomShortSideScale(min_size=256, max_size=320),
+...                     RandomCrop(resize_to),
+...                     RandomHorizontalFlip(p=0.5),
+...                 ]
+...             ),
+...         ),
+...     ]
+... )
+
+>>> train_dataset = pytorchvideo.data.Ucf101(
+...     data_path=os.path.join(dataset_root_path, "train"),
+...     clip_sampler=pytorchvideo.data.make_clip_sampler("random", clip_duration),
+...     decode_audio=False,
+...     transform=train_transform,
+... )
+```
+
+같은 방식의 작업 흐름을 검증과 평가 세트에도 적용할 수 있습니다.
+
+```py 
+>>> val_transform = Compose(
+...     [
+...         ApplyTransformToKey(
+...             key="video",
+...             transform=Compose(
+...                 [
+...                     UniformTemporalSubsample(num_frames_to_sample),
+...                     Lambda(lambda x: x / 255.0),
+...                     Normalize(mean, std),
+...                     Resize(resize_to),
+...                 ]
+...             ),
+...         ),
+...     ]
+... )
+
+>>> val_dataset = pytorchvideo.data.Ucf101(
+...     data_path=os.path.join(dataset_root_path, "val"),
+...     clip_sampler=pytorchvideo.data.make_clip_sampler("uniform", clip_duration),
+...     decode_audio=False,
+...     transform=val_transform,
+... )
+
+>>> test_dataset = pytorchvideo.data.Ucf101(
+...     data_path=os.path.join(dataset_root_path, "test"),
+...     clip_sampler=pytorchvideo.data.make_clip_sampler("uniform", clip_duration),
+...     decode_audio=False,
+...     transform=val_transform,
+... )
+```
+
+
+**참고**: 위의 데이터 세트의 파이프라인은 [공식 파이토치 예제](https://pytorchvideo.org/docs/tutorial_classification#dataset)에서 가져온 것입니다. 우리는 UCF-101 데이터셋에 맞게 [`pytorchvideo.data.Ucf101()`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.Ucf101) 함수를 사용하고 있습니다. 내부적으로 이 함수는 [`pytorchvideo.data.labeled_video_dataset.LabeledVideoDataset`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.LabeledVideoDataset) 객체를 반환합니다. `LabeledVideoDataset` 클래스는 PyTorchVideo 데이터셋에서 모든 영상 관련 작업의 기본 클래스입니다. 따라서 PyTorchVideo에서 미리 제공하지 않는 사용자 지정 데이터 세트를 사용하려면, 이 클래스를 적절하게 확장하면 됩니다. 더 자세한 사항이 알고 싶다면 `data` API [문서](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html) 를 참고하세요. 또한 위의 예시와 유사한 구조를 갖는 데이터 세트를 사용하고 있다면, `pytorchvideo.data.Ucf101()` 함수를 사용하는 데 문제가 없을 것입니다.
+
+데이터 세트에 영상의 개수를 알기 위해 `num_videos` 인수에 접근할 수 있습니다.
+
+```py
+>>> print(train_dataset.num_videos, val_dataset.num_videos, test_dataset.num_videos)
+# (300, 30, 75)
+```
+
+## 더 나은 디버깅을 위해 전처리 영상 시각화하기[[visualize-the-preprocessed-video-for-better-debugging]]
+
+```py 
+>>> import imageio
+>>> import numpy as np
+>>> from IPython.display import Image
+
+>>> def unnormalize_img(img):
+...     """Un-normalizes the image pixels."""
+...     img = (img * std) + mean
+...     img = (img * 255).astype("uint8")
+...     return img.clip(0, 255)
+
+>>> def create_gif(video_tensor, filename="sample.gif"):
+...     """Prepares a GIF from a video tensor.
+...     
+...     The video tensor is expected to have the following shape:
+...     (num_frames, num_channels, height, width).
+...     """
+...     frames = []
+...     for video_frame in video_tensor:
+...         frame_unnormalized = unnormalize_img(video_frame.permute(1, 2, 0).numpy())
+...         frames.append(frame_unnormalized)
+...     kargs = {"duration": 0.25}
+...     imageio.mimsave(filename, frames, "GIF", **kargs)
+...     return filename
+
+>>> def display_gif(video_tensor, gif_name="sample.gif"):
+...     """Prepares and displays a GIF from a video tensor."""
+...     video_tensor = video_tensor.permute(1, 0, 2, 3)
+...     gif_filename = create_gif(video_tensor, gif_name)
+...     return Image(filename=gif_filename)
+
+>>> sample_video = next(iter(train_dataset))
+>>> video_tensor = sample_video["video"]
+>>> display_gif(video_tensor)
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sample_gif.gif" alt="Person playing basketball"/>
+</div>
+
+## 모델 훈련하기[[train-the-model]] 
+
+🤗 Transformers의 [`Trainer`](https://huggingface.co/docs/transformers/main_classes/trainer)를 사용하여 모델을 훈련시켜보세요. `Trainer`를 인스턴스화하려면 훈련 설정과 평가 지표를 정의해야 합니다.  가장 중요한 것은 [`TrainingArguments`](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments)입니다. 이 클래스는 훈련을 구성하는 모든 속성을 포함하며, 훈련 중 체크포인트를 저장할 출력 폴더 이름을 필요로 합니다. 또한 🤗 Hub의 모델 저장소의 모든 정보를 동기화하는 데 도움이 됩니다.
+
+대부분의 훈련 인수는 따로 설명할 필요는 없습니다. 하지만 여기에서 중요한 인수는 `remove_unused_columns=False` 입니다. 이 인자는 모델의 호출 함수에서 사용되지 않는 모든 속성 열(columns)을 삭제합니다. 기본값은 일반적으로 True입니다. 이는 사용되지 않는 기능 열을 삭제하는 것이 이상적이며, 입력을 모델의 호출 함수로 풀기(unpack)가 쉬워지기 때문입니다. 하지만 이 경우에는 `pixel_values`(모델의 입력으로 필수적인 키)를 생성하기 위해 사용되지 않는 기능('video'가 특히 그렇습니다)이 필요합니다. 따라서 remove_unused_columns을 False로 설정해야 합니다.
+
+```py 
+>>> from transformers import TrainingArguments, Trainer
+
+>>> model_name = model_ckpt.split("/")[-1]
+>>> new_model_name = f"{model_name}-finetuned-ucf101-subset"
+>>> num_epochs = 4
+
+>>> args = TrainingArguments(
+...     new_model_name,
+...     remove_unused_columns=False,
+...     evaluation_strategy="epoch",
+...     save_strategy="epoch",
+...     learning_rate=5e-5,
+...     per_device_train_batch_size=batch_size,
+...     per_device_eval_batch_size=batch_size,
+...     warmup_ratio=0.1,
+...     logging_steps=10,
+...     load_best_model_at_end=True,
+...     metric_for_best_model="accuracy",
+...     push_to_hub=True,
+...     max_steps=(train_dataset.num_videos // batch_size) * num_epochs,
+... )
+```
+
+`pytorchvideo.data.Ucf101()` 함수로 반환되는 데이터 세트는 `__len__` 메소드가 이식되어 있지 않습니다. 따라서,  `TrainingArguments`를 인스턴스화할 때 `max_steps`를 정의해야 합니다.
+
+다음으로, 평가지표를 불러오고, 예측값에서 평가지표를 계산할 함수를 정의합니다. 필요한 전처리 작업은 예측된 로짓(logits)에 argmax 값을 취하는 것뿐입니다:
+
+```py
+import evaluate
+
+metric = evaluate.load("accuracy")
+
+
+def compute_metrics(eval_pred):
+    predictions = np.argmax(eval_pred.predictions, axis=1)
+    return metric.compute(predictions=predictions, references=eval_pred.label_ids)
+```
+
+**평가에 대한 참고사항**:
+
+[VideoMAE 논문](https://arxiv.org/abs/2203.12602)에서 저자는 다음과 같은 평가 전략을 사용합니다. 테스트 영상에서 여러 클립을 선택하고 그 클립에 다양한 크롭을 적용하여 집계 점수를 보고합니다. 그러나 이번 튜토리얼에서는 간단함과 간결함을 위해 해당 전략을 고려하지 않습니다.
+
+또한, 예제를 묶어서 배치를 형성하는 `collate_fn`을 정의해야합니다. 각 배치는 `pixel_values`와 `labels`라는 2개의 키로 구성됩니다.
+
+```py 
+>>> def collate_fn(examples):
+...     # permute to (num_frames, num_channels, height, width)
+...     pixel_values = torch.stack(
+...         [example["video"].permute(1, 0, 2, 3) for example in examples]
+...     )
+...     labels = torch.tensor([example["label"] for example in examples])
+...     return {"pixel_values": pixel_values, "labels": labels}
+```
+
+그런 다음 이 모든 것을 데이터 세트와 함께 `Trainer`에 전달하기만 하면 됩니다:
+
+```py 
+>>> trainer = Trainer(
+...     model,
+...     args,
+...     train_dataset=train_dataset,
+...     eval_dataset=val_dataset,
+...     tokenizer=image_processor,
+...     compute_metrics=compute_metrics,
+...     data_collator=collate_fn,
+... )
+```
+
+데이터를 이미 처리했는데도 불구하고 `image_processor`를 토크나이저 인수로 넣은 이유는 JSON으로 저장되는 이미지 프로세서 구성 파일이 Hub의 저장소에 업로드되도록 하기 위함입니다.
+
+`train` 메소드를 호출하여 모델을 미세 조정하세요:
+
+```py 
+>>> train_results = trainer.train()
+```
+
+학습이 완료되면, 모델을 [`~transformers.Trainer.push_to_hub`] 메소드를 사용하여 허브에 공유하여 누구나 모델을 사용할 수 있도록 합니다:
+```py
+>>> trainer.push_to_hub()
+```
+
+## 추론하기[[inference]]
+
+좋습니다. 이제 미세 조정된 모델을 추론하는 데 사용할 수 있습니다.
+
+추론에 사용할 영상을 불러오세요:
+```py 
+>>> sample_test_video = next(iter(test_dataset))
+```
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/sample_gif_two.gif" alt="Teams playing basketball"/>
+</div>
+
+미세 조정된 모델을 추론에 사용하는 가장 간단한 방법은 [`pipeline`](https://huggingface.co/docs/transformers/main/en/main_classes/pipelines#transformers.VideoClassificationPipeline)에서 모델을 사용하는 것입니다. 모델로 영상 분류를 하기 위해 `pipeline`을 인스턴스화하고 영상을 전달하세요:
+
+```py
+>>> from transformers import pipeline
+
+>>> video_cls = pipeline(model="my_awesome_video_cls_model")
+>>> video_cls("https://huggingface.co/datasets/sayakpaul/ucf101-subset/resolve/main/v_BasketballDunk_g14_c06.avi")
+[{'score': 0.9272987842559814, 'label': 'BasketballDunk'},
+ {'score': 0.017777055501937866, 'label': 'BabyCrawling'},
+ {'score': 0.01663011871278286, 'label': 'BalanceBeam'},
+ {'score': 0.009560945443809032, 'label': 'BandMarching'},
+ {'score': 0.0068979403004050255, 'label': 'BaseballPitch'}]
+```
+
+만약 원한다면 수동으로 `pipeline`의 결과를 재현할 수 있습니다:
+
+
+```py
+>>> def run_inference(model, video):
+...     # (num_frames, num_channels, height, width)
+...     perumuted_sample_test_video = video.permute(1, 0, 2, 3)
+...     inputs = {
+...         "pixel_values": perumuted_sample_test_video.unsqueeze(0),
+...         "labels": torch.tensor(
+...             [sample_test_video["label"]]
+...         ),  # this can be skipped if you don't have labels available.
+...     }
+
+...     device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+...     inputs = {k: v.to(device) for k, v in inputs.items()}
+...     model = model.to(device)
+
+...     # forward pass
+...     with torch.no_grad():
+...         outputs = model(**inputs)
+...         logits = outputs.logits
+
+...     return logits
+```
+
+모델에 입력값을 넣고 `logits`을 반환받으세요:
+
+```
+>>> logits = run_inference(trained_model, sample_test_video["video"])
+```
+
+`logits`을 디코딩하면, 우리는 다음 결과를 얻을 수 있습니다:
+
+```py 
+>>> predicted_class_idx = logits.argmax(-1).item()
+>>> print("Predicted class:", model.config.id2label[predicted_class_idx])
+# Predicted class: BasketballDunk
+```

docs/source/ko/tasks/zero_shot_object_detection.mdx

@@ -0,0 +1,303 @@
+<!--Copyright 2023 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.
+-->
+
+# 제로샷(zero-shot) 객체 탐지[[zeroshot-object-detection]]
+
+[[open-in-colab]]
+
+일반적으로 [객체 탐지](object_detection)에 사용되는 모델을 학습하기 위해서는 레이블이 지정된 이미지 데이터 세트가 필요합니다.
+그리고 학습 데이터에 존재하는 클래스(레이블)만 탐지할 수 있다는 한계점이 있습니다.
+
+다른 방식을 사용하는 [OWL-ViT](../model_doc/owlvit) 모델로 제로샷 객체 탐지가 가능합니다.
+OWL-ViT는 개방형 어휘(open-vocabulary) 객체 탐지기입니다.
+즉, 레이블이 지정된 데이터 세트에 미세 조정하지 않고 자유 텍스트 쿼리를 기반으로 이미지에서 객체를 탐지할 수 있습니다.
+
+OWL-ViT 모델은 멀티 모달 표현을 활용해 개방형 어휘 탐지(open-vocabulary detection)를 수행합니다.
+[CLIP](../model_doc/clip) 모델에 경량화(lightweight)된 객체 분류와 지역화(localization) 헤드를 결합합니다.
+개방형 어휘 탐지는 CLIP의 텍스트 인코더로 free-text 쿼리를 임베딩하고, 객체 분류와 지역화 헤드의 입력으로 사용합니다.
+이미지와 해당 텍스트 설명을 연결하면 ViT가 이미지 패치(image patches)를 입력으로 처리합니다.
+OWL-ViT 모델의 저자들은 CLIP 모델을 처음부터 학습(scratch learning)한 후에, bipartite matching loss를 사용하여 표준 객체 인식 데이터셋으로 OWL-ViT 모델을 미세 조정했습니다.
+
+이 접근 방식을 사용하면 모델은 레이블이 지정된 데이터 세트에 대한 사전 학습 없이도 텍스트 설명을 기반으로 객체를 탐지할 수 있습니다.
+
+이번 가이드에서는 OWL-ViT 모델의 사용법을 다룰 것입니다:
+- 텍스트 프롬프트 기반 객체 탐지
+- 일괄 객체 탐지
+- 이미지 가이드 객체 탐지
+
+시작하기 전에 필요한 라이브러리가 모두 설치되어 있는지 확인하세요:
+```bash
+pip install -q transformers
+```
+
+## 제로샷(zero-shot) 객체 탐지 파이프라인[[zeroshot-object-detection-pipeline]]
+
+[`pipeline`]을 활용하면 가장 간단하게 OWL-ViT 모델을 추론해볼 수 있습니다.
+[Hugging Face Hub에 업로드된 체크포인트](https://huggingface.co/models?pipeline_tag=zero-shot-image-classification&sort=downloads)에서 제로샷(zero-shot) 객체 탐지용 파이프라인을 인스턴스화합니다:
+
+```python
+>>> from transformers import pipeline
+
+>>> checkpoint = "google/owlvit-base-patch32"
+>>> detector = pipeline(model=checkpoint, task="zero-shot-object-detection")
+```
+
+다음으로, 객체를 탐지하고 싶은 이미지를 선택하세요.
+여기서는 [NASA](https://www.nasa.gov/multimedia/imagegallery/index.html) Great Images 데이터 세트의 일부인 우주비행사 에일린 콜린스(Eileen Collins) 사진을 사용하겠습니다.
+
+```py
+>>> import skimage
+>>> import numpy as np
+>>> from PIL import Image
+
+>>> image = skimage.data.astronaut()
+>>> image = Image.fromarray(np.uint8(image)).convert("RGB")
+
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_1.png" alt="Astronaut Eileen Collins"/>
+</div>
+
+이미지와 해당 이미지의 후보 레이블을 파이프라인으로 전달합니다.
+여기서는 이미지를 직접 전달하지만, 컴퓨터에 저장된 이미지의 경로나 url로 전달할 수도 있습니다.
+candidate_labels는 이 예시처럼 간단한 단어일 수도 있고 좀 더 설명적인 단어일 수도 있습니다.
+또한, 이미지를 검색(query)하려는 모든 항목에 대한 텍스트 설명도 전달합니다.
+
+```py
+>>> predictions = detector(
+...     image,
+...     candidate_labels=["human face", "rocket", "nasa badge", "star-spangled banner"],
+... )
+>>> predictions
+[{'score': 0.3571370542049408,
+  'label': 'human face',
+  'box': {'xmin': 180, 'ymin': 71, 'xmax': 271, 'ymax': 178}},
+ {'score': 0.28099656105041504,
+  'label': 'nasa badge',
+  'box': {'xmin': 129, 'ymin': 348, 'xmax': 206, 'ymax': 427}},
+ {'score': 0.2110239565372467,
+  'label': 'rocket',
+  'box': {'xmin': 350, 'ymin': -1, 'xmax': 468, 'ymax': 288}},
+ {'score': 0.13790413737297058,
+  'label': 'star-spangled banner',
+  'box': {'xmin': 1, 'ymin': 1, 'xmax': 105, 'ymax': 509}},
+ {'score': 0.11950037628412247,
+  'label': 'nasa badge',
+  'box': {'xmin': 277, 'ymin': 338, 'xmax': 327, 'ymax': 380}},
+ {'score': 0.10649408400058746,
+  'label': 'rocket',
+  'box': {'xmin': 358, 'ymin': 64, 'xmax': 424, 'ymax': 280}}]
+```
+
+이제 예측값을 시각화해봅시다:
+
+```py
+>>> from PIL import ImageDraw
+
+>>> draw = ImageDraw.Draw(image)
+
+>>> for prediction in predictions:
+...     box = prediction["box"]
+...     label = prediction["label"]
+...     score = prediction["score"]
+
+...     xmin, ymin, xmax, ymax = box.values()
+...     draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+...     draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="white")
+
+>>> image
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_2.png" alt="Visualized predictions on NASA image"/>
+</div>
+
+## 텍스트 프롬프트 기반 객체 탐지[[textprompted-zeroshot-object-detection-by-hand]]
+
+제로샷 객체 탐지 파이프라인 사용법에 대해 살펴보았으니, 이제 동일한 결과를 복제해보겠습니다.
+
+[Hugging Face Hub에 업로드된 체크포인트](https://huggingface.co/models?other=owlvit)에서 관련 모델과 프로세서를 가져오는 것으로 시작합니다.
+여기서는 이전과 동일한 체크포인트를 사용하겠습니다:
+
+```py
+>>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+
+>>> model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint)
+>>> processor = AutoProcessor.from_pretrained(checkpoint)
+```
+
+다른 이미지를 사용해 보겠습니다:
+
+```py
+>>> import requests
+
+>>> url = "https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640"
+>>> im = Image.open(requests.get(url, stream=True).raw)
+>>> im
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_3.png" alt="Beach photo"/>
+</div>
+
+프로세서를 사용해 모델의 입력을 준비합니다.
+프로세서는 모델의 입력으로 사용하기 위해 이미지 크기를 변환하고 정규화하는 이미지 프로세서와 텍스트 입력을 처리하는 [`CLIPTokenizer`]로 구성됩니다.
+
+```py
+>>> text_queries = ["hat", "book", "sunglasses", "camera"]
+>>> inputs = processor(text=text_queries, images=im, return_tensors="pt")
+```
+
+모델에 입력을 전달하고 결과를 후처리 및 시각화합니다.
+이미지 프로세서가 모델에 이미지를 입력하기 전에 이미지 크기를 조정했기 때문에, [`~OwlViTImageProcessor.post_process_object_detection`] 메소드를 사용해
+예측값의 바운딩 박스(bounding box)가 원본 이미지의 좌표와 상대적으로 동일한지 확인해야 합니다.
+
+```py
+>>> import torch
+
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+...     target_sizes = torch.tensor([im.size[::-1]])
+...     results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)[0]
+
+>>> draw = ImageDraw.Draw(im)
+
+>>> scores = results["scores"].tolist()
+>>> labels = results["labels"].tolist()
+>>> boxes = results["boxes"].tolist()
+
+>>> for box, score, label in zip(boxes, scores, labels):
+...     xmin, ymin, xmax, ymax = box
+...     draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+...     draw.text((xmin, ymin), f"{text_queries[label]}: {round(score,2)}", fill="white")
+
+>>> im
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_4.png" alt="Beach photo with detected objects"/>
+</div>
+
+## 일괄 처리[[batch-processing]]
+
+여러 이미지와 텍스트 쿼리를 전달하여 여러 이미지에서 서로 다른(또는 동일한) 객체를 검색할 수 있습니다.
+일괄 처리를 위해서 텍스트 쿼리는 이중 리스트로, 이미지는 PIL 이미지, PyTorch 텐서, 또는 NumPy 배열로 이루어진 리스트로 프로세서에 전달해야 합니다.
+
+```py
+>>> images = [image, im]
+>>> text_queries = [
+...     ["human face", "rocket", "nasa badge", "star-spangled banner"],
+...     ["hat", "book", "sunglasses", "camera"],
+... ]
+>>> inputs = processor(text=text_queries, images=images, return_tensors="pt")
+```
+
+이전에는 후처리를 위해 단일 이미지의 크기를 텐서로 전달했지만, 튜플을 전달할 수 있고, 여러 이미지를 처리하는 경우에는 튜플로 이루어진 리스트를 전달할 수도 있습니다.
+아래 두 예제에 대한 예측을 생성하고, 두 번째 이미지(`image_idx = 1`)를 시각화해 보겠습니다.
+
+```py
+>>> with torch.no_grad():
+...     outputs = model(**inputs)
+...     target_sizes = [x.size[::-1] for x in images]
+...     results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)
+
+>>> image_idx = 1
+>>> draw = ImageDraw.Draw(images[image_idx])
+
+>>> scores = results[image_idx]["scores"].tolist()
+>>> labels = results[image_idx]["labels"].tolist()
+>>> boxes = results[image_idx]["boxes"].tolist()
+
+>>> for box, score, label in zip(boxes, scores, labels):
+...     xmin, ymin, xmax, ymax = box
+...     draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+...     draw.text((xmin, ymin), f"{text_queries[image_idx][label]}: {round(score,2)}", fill="white")
+
+>>> images[image_idx]
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_4.png" alt="Beach photo with detected objects"/>
+</div>
+
+## 이미지 가이드 객체 탐지[[imageguided-object-detection]]
+
+텍스트 쿼리를 이용한 제로샷 객체 탐지 외에도 OWL-ViT 모델은 이미지 가이드 객체 탐지 기능을 제공합니다.
+이미지를 쿼리로 사용해 대상 이미지에서 유사한 객체를 찾을 수 있다는 의미입니다.
+텍스트 쿼리와 달리 하나의 예제 이미지에서만 가능합니다.
+
+소파에 고양이 두 마리가 있는 이미지를 대상 이미지(target image)로, 고양이 한 마리가 있는 이미지를 쿼리로 사용해보겠습니다:
+
+```py
+>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+>>> image_target = Image.open(requests.get(url, stream=True).raw)
+
+>>> query_url = "http://images.cocodataset.org/val2017/000000524280.jpg"
+>>> query_image = Image.open(requests.get(query_url, stream=True).raw)
+```
+
+다음 이미지를 살펴보겠습니다:
+
+```py
+>>> import matplotlib.pyplot as plt
+
+>>> fig, ax = plt.subplots(1, 2)
+>>> ax[0].imshow(image_target)
+>>> ax[1].imshow(query_image)
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_5.png" alt="Cats"/>
+</div>
+
+전처리 단계에서 텍스트 쿼리 대신에 `query_images`를 사용합니다:
+
+```py
+>>> inputs = processor(images=image_target, query_images=query_image, return_tensors="pt")
+```
+
+예측의 경우, 모델에 입력을 전달하는 대신 [`~OwlViTForObjectDetection.image_guided_detection`]에 전달합니다.
+레이블이 없다는 점을 제외하면 이전과 동일합니다.
+이전과 동일하게 이미지를 시각화합니다.
+
+```py
+>>> with torch.no_grad():
+...     outputs = model.image_guided_detection(**inputs)
+...     target_sizes = torch.tensor([image_target.size[::-1]])
+...     results = processor.post_process_image_guided_detection(outputs=outputs, target_sizes=target_sizes)[0]
+
+>>> draw = ImageDraw.Draw(image_target)
+
+>>> scores = results["scores"].tolist()
+>>> boxes = results["boxes"].tolist()
+
+>>> for box, score, label in zip(boxes, scores, labels):
+...     xmin, ymin, xmax, ymax = box
+...     draw.rectangle((xmin, ymin, xmax, ymax), outline="white", width=4)
+
+>>> image_target
+```
+
+<div class="flex justify-center">
+     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_6.png" alt="Cats with bounding boxes"/>
+</div>
+
+OWL-ViT 모델을 추론하고 싶다면 아래 데모를 확인하세요:
+
+<iframe
+	src="https://adirik-owl-vit.hf.space"
+	frameborder="0"
+	width="850"
+	height="450"
+></iframe>

docs/source/ko/tasks_explained.mdx

@@ -0,0 +1,291 @@
+<!--Copyright 2023 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.
+-->
+
+# 🤗 Transformers로 작업을 해결하는 방법[[how-transformers-solve-tasks]]
+
+[🤗 Transformers로 할 수 있는 작업](task_summary)에서 자연어 처리(NLP), 음성 및 오디오, 컴퓨터 비전 작업 등의 중요한 응용을 배웠습니다. 이 페이지에서는 모델이 이러한 작업을 어떻게 해결하는지 자세히 살펴보고 내부에서 어떤 일이 일어나는지 설명합니다. 주어진 작업을 해결하는 많은 방법이 있으며, 일부 모델은 특정 기술을 구현하거나 심지어 새로운 방식으로 작업에 접근할 수도 있지만, Transformer 모델의 경우 일반적인 아이디어는 동일합니다. 유연한 아키텍처 덕분에 대부분의 모델은 인코더, 디코더 또는 인코더-디코더 구조의 변형입니다. Transformer 모델뿐만 아니라 우리의 라이브러리에는 오늘날 컴퓨터 비전 작업에 사용되는 몇 가지 합성곱 신경망(CNNs)도 있습니다. 또한, 우리는 현대 CNN의 작동 방식에 대해 설명할 것입니다.
+
+작업이 어떻게 해결되는지 설명하기 위해, 유용한 예측을 출력하고자 모델 내부에서 어떤 일이 일어나는지 살펴봅니다.
+
+- 오디오 분류 및 자동 음성 인식(ASR)을 위한 [Wav2Vec2](model_doc/wav2vec2)
+- 이미지 분류를 위한 [Vision Transformer (ViT)](model_doc/vit) 및 [ConvNeXT](model_doc/convnext)
+- 객체 탐지를 위한 [DETR](model_doc/detr)
+- 이미지 분할을 위한 [Mask2Former](model_doc/mask2former)
+- 깊이 추정을 위한 [GLPN](model_doc/glpn)
+- 인코더를 사용하는 텍스트 분류, 토큰 분류 및 질의응답과 같은 NLP 작업을 위한 [BERT](model_doc/bert)
+- 디코더를 사용하는 텍스트 생성과 같은 NLP 작업을 위한 [GPT2](model_doc/gpt2)
+- 인코더-디코더를 사용하는 요약 및 번역과 같은 NLP 작업을 위한 [BART](model_doc/bart)
+
+<Tip>
+
+더 나아가기 전에, 기존 Transformer 아키텍처에 대한 기본적인 지식을 숙지하는 것이 좋습니다. 인코더, 디코더 및 어텐션의 작동 방식을 알면 다양한 Transformer 모델이 어떻게 작동하는지 이해하는 데 도움이 됩니다. 시작 단계거나 복습이 필요한 경우, 더 많은 정보를 위해 [코스](https://huggingface.co/course/chapter1/4?fw=pt)를 확인하세요!
+
+</Tip>
+
+## 음성 및 오디오[[speech-and-audio]]
+
+[Wav2Vec2](model_doc/wav2vec2)는 레이블이 지정되지 않은 음성 데이터에 대해 사전훈련된 모델로, 오디오 분류 및 자동 음성 인식을 위해 레이블이 지정된 데이터로 미세 조정합니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/wav2vec2_architecture.png"/>
+</div>
+
+이 모델에는 4가지 주요 구성 요소가 있습니다:
+
+1. *특징 인코더(feature encoder)*는 원시 오디오 파형(raw audio waveform)을 가져와서 제로 평균 및 단위 분산으로 표준화하고, 각각 20ms 길이의 특징 벡터의 시퀀스로 변환합니다.
+
+2. 오디오 파형은 본질적으로 연속적이기 때문에, 텍스트 시퀀스를 단어로 나누는 것과 같이 분할할 수 없습니다. 그래서 *양자화 모듈(quantization module)*로 전달되는 특징 벡터는 이산형 음성 단위를 학습하기 위한 것입니다. 음성 단위는 *코드북(codebook)*(어휘집이라고 생각할 수 있습니다)이라는 코드단어(codewords) 콜렉션에서 선택됩니다. 코드북에서 연속적인 오디오 입력을 가장 잘 나타내는 벡터 또는 음성 단위가 선택되어 모델을 통과합니다.
+
+3. 특징 벡터의 절반은 무작위로 마스크가 적용되며, 마스크된 특징 벡터는 *상대적 위치 임베딩*을 추가하는 Transformer 인코더인 *문맥 네트워크(context network)*로 전달됩니다.
+
+4. 문맥 네트워크의 사전훈련 목표는 *대조적 작업(contrastive task)*입니다. 모델은 잘못된 예측 시퀀스에서 마스크된 예측의 실제 양자화된 음성 표현을 예측하며, 모델이 가장 유사한 컨텍스트 벡터와 양자화된 음성 단위(타겟 레이블)를 찾도록 권장합니다.
+
+이제 wav2vec2가 사전훈련되었으므로, 오디오 분류 또는 자동 음성 인식을 위해 데이터에 맞춰 미세 조정할 수 있습니다!
+
+### 오디오 분류[[audio-classification]]
+
+사전훈련된 모델을 오디오 분류에 사용하려면, 기본 Wav2Vec2 모델 상단에 시퀀스 분류 헤드를 추가하면 됩니다. 분류 헤드는 인코더의 은닉 상태(hidden states)를 받는 선형 레이어입니다. 은닉 상태는 각각 길이가 다른 오디오 프레임에서 학습된 특징을 나타냅니다. 고정 길이의 벡터 하나를 만들기 위해, 은닉 상태는 먼저 풀링되고, 클래스 레이블에 대한 로짓으로 변환됩니다. 가장 가능성이 높은 클래스를 찾기 위해 로짓과 타겟 사이의 교차 엔트로피 손실이 계산됩니다.
+
+오디오 분류에 직접 도전할 준비가 되셨나요? 완전한 [오디오 분류 가이드](tasks/audio_classification)를 확인하여 Wav2Vec2를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+### 자동 음성 인식[[automatic-speech-recognition]]
+
+사전훈련된 모델을 자동 음성 인식에 사용하려면, [연결주의적 시간 분류(CTC, Connectionist Temporal Classification)](glossary#connectionist-temporal-classification-ctc)를 위해 기본 Wav2Vec2 모델 상단에 언어 모델링 헤드를 추가합니다. 언어 모델링 헤드는 인코더의 은닉 상태를 받아서 로짓으로 변환합니다. 각 로짓은 토큰 클래스(토큰 수는 작업의 어휘에서 나타납니다)를 나타냅니다. CTC 손실은 텍스트로 디코딩된 토큰에서 가장 가능성이 높은 토큰 시퀀스를 찾기 위해 로짓과 타겟 사이에서 계산됩니다. 
+
+자동 음성 인식에 직접 도전할 준비가 되셨나요? 완전한 [자동 음성 인식 가이드](tasks/asr)를 확인하여 Wav2Vec2를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+## 컴퓨터 비전[[computer-vision]]
+
+컴퓨터 비전 작업에 접근하는 2가지 방법이 있습니다:
+
+1. 이미지를 패치 시퀀스로 분리하고 Transformer로 병렬 처리합니다.
+2. [ConvNeXT](model_doc/convnext)와 같은 현대 CNN을 사용합니다. 이는 합성곱 레이어를 기반으로 하지만 현대 네트워크 설계를 적용합니다.
+
+<Tip>
+
+세 번째 방법은 Transformer와 합성곱(예를 들어, [Convolutional Vision Transformer](model_doc/cvt) 또는 [LeViT](model_doc/levit))을 결합하는 것입니다. 우리는 살펴볼 두 가지 방법만 결합하기 때문에 여기서 이 방법을 다루지 않습니다.
+
+</Tip>
+
+ViT와 ConvNeXT는 일반적으로 이미지 분류에서 사용되지만, 물체 감지, 분할, 깊이 추정과 같은 다른 비전 작업에는 각각 DETR, Mask2Former, GLPN이 더 적합하므로 이러한 모델을 살펴보겠습니다.
+
+### 이미지 분류[[image-classification]]
+
+ViT와 ConvNeXT 모두 이미지 분류에 사용될 수 있지만, ViT는 어텐션 메커니즘을, ConvNeXT는 합성곱을 사용하는 것이 주된 차이입니다.
+
+#### Transformer[[transformer]]
+
+[ViT](model_doc/vit)은 합성곱을 전적으로 순수 Transformer 아키텍처로 대체합니다. 기존 Transformer에 익숙하다면, ViT를 이해하는 방법의 대부분을 이미 파악했다고 볼 수 있습니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/vit_architecture.jpg"/>
+</div>
+
+ViT가 도입한 주요 변경 사항은 이미지가 Transformer로 어떻게 전달되는지에 있습니다:
+
+1. 이미지는 서로 중첩되지 않는 정사각형 패치로 분할되고, 각 패치는 벡터 또는 *패치 임베딩(patch embedding)*으로 변환됩니다. 패치 임베딩은 적절한 입력 차원을 만드는 2D 합성곱 계층에서 생성됩니다(기본 Transformer의 경우 각 패치의 임베딩마다 768개의 값이 필요합니다). 224x224 픽셀 이미지가 있다면, 16x16 이미지 패치 196개로 분할할 수 있습니다. 텍스트가 단어로 토큰화되는 것처럼, 이미지도 패치 시퀀스로 "토큰화"됩니다.
+
+2. *학습 가능한 임베딩(learnable embedding)*(특수한 `[CLS]` 토큰)이 BERT와 같이 패치 임베딩의 시작 부분에 추가됩니다. `[CLS]` 토큰의 마지막 은닉 상태는 부착된 분류 헤드의 입력으로 사용되고, 다른 출력은 무시됩니다. 이 토큰은 모델이 이미지의 표현을 인코딩하는 방법을 학습하는 데 도움이 됩니다.
+
+3. 패치와 학습 가능한 임베딩에 마지막으로 추가할 것은 *위치 임베딩*입니다. 왜냐하면 모델은 이미지 패치의 순서를 모르기 때문입니다. 위치 임베딩도 학습 가능하며, 패치 임베딩과 동일한 크기를 가집니다. 최종적으로, 모든 임베딩이 Transformer 인코더에 전달됩니다.
+
+4. `[CLS]` 토큰을 포함한 출력은 다층 퍼셉트론 헤드(MLP)에 전달됩니다. ViT의 사전훈련 목표는 단순히 분류입니다. 다른 분류 헤드와 같이, MLP 헤드는 출력을 클래스 레이블에 대해 로짓으로 변환하고 교차 엔트로피 손실을 계산하여 가장 가능성이 높은 클래스를 찾습니다.
+
+이미지 분류에 직접 도전할 준비가 되셨나요? 완전한 [이미지 분류 가이드](tasks/image_classification)를 확인하여 ViT를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+#### CNN[[cnn]]
+
+<Tip>
+
+이 섹션에서는 합성곱에 대해 간략하게 설명합니다. 그러나 이미지의 모양과 크기가 어떻게 변화하는지에 대한 사전 이해가 있다면 도움이 될 것입니다. 합성곱에 익숙하지 않은 경우, fastai book의 [합성곱 신경망 챕터](https://github.com/fastai/fastbook/blob/master/13_convolutions.ipynb)를 확인하세요!
+
+</Tip>
+
+[ConvNeXT](model_doc/convnext)는 성능을 높이기 위해 새로운 현대 네트워크 설계를 적용한 CNN 구조입니다. 그러나 합성곱은 여전히 모델의 핵심입니다. 높은 수준의 관점에서 볼 때, [합성곱](glossary#convolution)은 작은 행렬(*커널*)에 이미지 픽셀의 작은 윈도우를 곱하는 연산입니다. 이는 특정 텍스쳐(texture)이나 선의 곡률과 같은 일부 특징을 계산합니다. 그러고 다음 픽셀 윈도우로 넘어가는데, 여기서 합성곱이 이동하는 거리를 *보폭(stride)*이라고 합니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convolution.gif"/>
+</div>
+
+<small>패딩이나 보폭이 없는 기본 합성곱, <a href="https://arxiv.org/abs/1603.07285">딥러닝을 위한 합성곱 연산 가이드</a></small>
+
+이 출력을 다른 합성곱 레이어에 전달할 수 있으며, 각 연속적인 레이어를 통해 네트워크는 핫도그나 로켓과 같이 더 복잡하고 추상적인 것을 학습합니다. 합성곱 레이어 사이에 풀링 레이어를 추가하여 차원을 줄이고 특징의 위치 변화에 대해 모델을 더 견고하게 만드는 것이 일반적입니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/convnext_architecture.png"/>
+</div>
+
+ConvNeXT는 CNN을 5가지 방식으로 현대화합니다:
+
+1. 각 단계의 블록 수를 변경하고 더 큰 보폭과 그에 대응하는 커널 크기로 이미지를 "패치화(patchify)"합니다. 겹치지 않는 슬라이딩 윈도우는 ViT가 이미지를 패치로 분할하는 방법과 유사하게 이 패치화 전략을 만듭니다.
+
+2. *병목(bottleneck)* 레이어는 채널 수를 줄였다가 다시 복원합니다. 왜냐하면 1x1 합성곱을 수행하는 것이 더 빠르고, 깊이를 늘릴 수 있기 때문입니다. 역 병목(inverted bottlenect)은 채널 수를 확장하고 축소함으로써 그 반대로 수행하므로, 메모리 효율이 더 높습니다.
+
+3. 병목 레이어의 일반적인 3x3 합성곱 레이어를 각 입력 채널에 개별적으로 합성곱을 적용한 다음 마지막에 쌓는 *깊이별 합성곱(depthwise convolution)*으로 대체합니다. 이는 네트워크 폭이 넓혀 성능이 향상됩니다.
+
+4. ViT는 어텐션 메커니즘 덕분에 한 번에 더 많은 이미지를 볼 수 있는 전역 수신 필드를 가지고 있습니다. ConvNeXT는 커널 크기를 7x7로 늘려 이 효과를 재현하려고 시도합니다.
+
+5. 또한 ConvNeXT는 Transformer 모델을 모방하는 몇 가지 레이어 설계를 변경합니다. 활성화 및 정규화 레이어가 더 적고, 활성화 함수가 ReLU 대신 GELU로 전환되고, BatchNorm 대신 LayerNorm을 사용합니다.
+
+합성곱 블록의 출력은 분류 헤드로 전달되며, 분류 헤드는 출력을 로짓으로 변환하고 교차 엔트로피 손실을 계산하여 가장 가능성이 높은 레이블을 찾습니다.
+
+### 객체 탐지[[object-detection]]
+
+[DETR](model_doc/detr), *DEtection TRansformer*는 CNN과 Transformer 인코더-디코더를 결합한 종단간(end-to-end) 객체 탐지 모델입니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/detr_architecture.png"/>
+</div>
+
+1. 사전훈련된 CNN *백본(backbone)*은 픽셀 값으로 나타낸 이미지를 가져와 저해상도 특징 맵을 만듭니다. 특징 맵에 대해 1x1 합성곱을 적용하여 차원을 줄이고, 고수준 이미지 표현을 가진 새로운 특징 맵을 생성합니다. Transformer는 시퀀스 모델이기 때문에 특징 맵을 위치 임베딩과 결합된 특징 벡터의 시퀀스로 평탄화합니다.
+
+2. 특징 벡터는 어텐션 레이어를 사용하여 이미지 표현을 학습하는 인코더에 전달됩니다. 다음으로, 인코더의 은닉 상태는 디코더에서 *객체 쿼리*와 결합됩니다. 객체 쿼리는 이미지의 다른 영역에 초점을 맞춘 학습된 임베딩으로 학습되고, 각 어텐션 레이어를 진행하면서 갱신됩니다. 디코더의 은닉 상태는 각 객체 쿼리에 대한 바운딩 박스 좌표와 클래스 레이블을 예측하는 순방향 네트워크에 전달되며, 객체가 없는 경우 `no object`가 출력됩니다.
+
+    DETR은 각 객체 쿼리를 병렬로 디코딩하여 *N* 개의 최종 예측을 출력합니다. 여기서 *N*은 쿼리 수입니다. 한 번에 하나의 요소를 예측하는 일반적인 자기회귀 모델과 달리, 객체 탐지는 한 번에 *N* 개의 예측을 수행하는 집합 예측 작업(`바운딩 박스`, `클래스 레이블`)입니다.
+
+3. DETR은 훈련 중 *이분 매칭 손실(bipartite matching loss)*을 사용하여 고정된 수의 예측과 고정된 실제 정답 레이블(ground truth labels) 세트를 비교합니다. *N*개의 레이블 세트에 실제 정답 레이블보다 적은 경우, `no object` 클래스로 패딩됩니다. 이 손실 함수는 DETR이 예측과 실제 정답 레이블 간 1:1 대응을 찾도록 권장합니다. 바운딩 박스 또는 클래스 레이블 중 하나라도 잘못된 경우, 손실이 발생합니다. 마찬가지로, 존재하지 않는 객체를 예측하는 경우, 패널티를 받습니다. 이로 인해 DETR은 이미지에서 눈에 잘 띄는 물체 하나에 집중하는 대신, 다른 객체를 찾도록 권장됩니다.
+
+객체 탐지 헤드가 DETR 상단에 추가되어 클래스 레이블과 바운딩 박스의 좌표를 찾습니다. 객체 탐지 헤드에는 두 가지 구성 요소가 있습니다: 디코더 은닉 상태를 클래스 레이블의 로짓으로 변환하는 선형 레이어 및 바운딩 박스를 예측하는 MLP
+
+객체 탐지에 직접 도전할 준비가 되셨나요? 완전한 [객체 탐지 가이드](tasks/object_detection)를 확인하여 DETR을 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+### 이미지 분할[[image-segmentation]]
+
+[Mask2Former](model_doc/mask2former)는 모든 유형의 이미지 분할 작업을 해결하는 범용 아키텍처입니다. 전통적인 분할 모델은 일반적으로 시멘틱(semantic) 또는 파놉틱(panoptic) 분할과 같은 이미지 분할의 특정 하위 작업에 맞춰 조정됩니다. Mask2Former는 모든 작업을 *마스크 분류* 문제로 구성합니다. 마스크 분류는 픽셀을 *N*개 세그먼트로 그룹화하고, 주어진 이미지에 대해 *N*개의 마스크와 그에 대응하는 클래스 레이블을 예측합니다. 이 섹션에서 Mask2Former의 작동 방법을 설명한 다음, 마지막에 SegFormer를 미세 조정해볼 수 있습니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/mask2former_architecture.png"/>
+</div>
+
+Mask2Former에는 3가지 주요 구성 요소가 있습니다:
+
+1. [Swin](model_doc/swin) 백본이 이미지를 받아 3개의 연속된 3x3 합성곱에서 저해상도 이미지 특징 맵을 생성합니다.
+
+2. 특징 맵은 *픽셀 디코더*에 전달됩니다. 이 디코더는 저해상도 특징을 고해상도 픽셀 임베딩으로 점진적으로 업샘플링합니다. 픽셀 디코더는 실제로 원본 이미지의 1/32, 1/16, 1/8 해상도의 다중 스케일 특징(저해상도 및 고해상도 특징 모두 포함)을 생성합니다.
+
+3. 이러한 서로 다른 크기의 특징 맵은 고해상도 특징에서 작은 객체를 포착하기 위해 한 번에 하나의 Transformer 디코더 레이어에 연속적으로 공급됩니다. Mask2Former의 핵심은 디코더의 *마스크 어텐션* 메커니즘입니다. 전체 이미지를 참조할 수 있는 크로스 어텐션(cross-attention)과 달리, 마스크 어텐션은 이미지의 특정 영역에만 집중합니다. 이는 이미지의 지역적 특징만으로 모델이 충분히 학습할 수 있기 때문에 더 빠르고 성능이 우수합니다.
+
+4. [DETR](tasks_explained#object-detection)과 같이, Mask2Former는 학습된 객체 쿼리를 사용하고 이를 픽셀 디코더에서의 이미지 특징과 결합하여 예측 집합(`클래스 레이블`, `마스크 예측`)을 생성합니다. 디코더의 은닉 상태는 선형 레이어로 전달되어 클래스 레이블에 대한 로짓으로 변환됩니다. 로짓과 클래스 레이블 사이의 교차 엔트로피 손실을 계산하여 가장 가능성이 높은 것을 찾습니다.
+
+    마스크 예측은 픽셀 임베딩과 최종 디코더 은닉 상태를 결합하여 생성됩니다. 시그모이드 교차 엔트로피 및 Dice 손실은 로짓과 실제 정답 마스크(ground truth mask) 사이에서 계산되어 가장 가능성이 높은 마스크를 찾습니다.
+
+이미지 분할에 직접 도전할 준비가 되셨나요? 완전한 [이미지 분할 가이드](tasks/semantic_segmentation)를 확인하여 SegFormer를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+### 깊이 추정[[depth-estimation]]
+
+[GLPN](model_doc/glpn), *Global-Local Path Network*는 [SegFormer](model_doc/segformer) 인코더와 경량 디코더를 결합한 깊이 추정을 위한 Transformer입니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/glpn_architecture.jpg"/>
+</div>
+
+1. ViT와 같이, 이미지는 패치 시퀀스로 분할되지만, 이미지 패치가 더 작다는 점이 다릅니다. 이는 세그멘테이션이나 깊이 추정과 같은 밀도 예측 작업에 더 적합합니다. 이미지 패치는 패치 임베딩으로 변환되어(패치 임베딩이 생성되는 방법은 [이미지 분류](#image-classification) 섹션을 참조하세요), 인코더로 전달됩니다.
+
+2. 인코더는 패치 임베딩을 받아, 여러 인코더 블록에 전달합니다. 각 블록은 어텐션 및 Mix-FFN 레이어로 구성됩니다. 후자의 목적은 위치 정보를 제공하는 것입니다. 각 인코더 블록의 끝에는 계층적 표현을 생성하기 위한 *패치 병합(patch merging)* 레이어가 있습니다. 각 인접한 패치 그룹의 특징은 연결되고, 연결된 특징에 선형 레이어가 적용되어 패치 수를 1/4의 해상도로 줄입니다. 이는 다음 인코더 블록의 입력이 되며, 이러한 전체 프로세스는 1/8, 1/16, 1/32 해상도의 이미지 특징을 가질 때까지 반복됩니다.
+
+3. 경량 디코더는 인코더에서 마지막 특징 맵(1/32 크기)을 가져와 1/16 크기로 업샘플링합니다. 여기서, 특징은 *선택적 특징 융합(SFF, Selective Feature Fusion)* 모듈로 전달됩니다. 이 모듈은 각 특징에 대해 어텐션 맵에서 로컬 및 전역 특징을 선택하고 결합한 다음, 1/8로 업샘플링합니다. 이 프로세스는 디코딩된 특성이 원본 이미지와 동일한 크기가 될 때까지 반복됩니다. 출력은 두 개의 합성곱 레이어를 거친 다음, 시그모이드 활성화가 적용되어 각 픽셀의 깊이를 예측합니다.
+
+## 자연어처리[[natural-language-processing]]
+
+Transformer는 초기에 기계 번역을 위해 설계되었고, 그 이후로는 사실상 모든 NLP 작업을 해결하기 위한 기본 아키텍처가 되었습니다. 어떤 작업은 Transformer의 인코더 구조에 적합하며, 다른 작업은 디코더에 더 적합합니다. 또 다른 작업은 Transformer의 인코더-디코더 구조를 모두 활용합니다.
+
+### 텍스트 분류[[text-classification]]
+
+[BERT](model_doc/bert)는 인코더 전용 모델이며, 텍스트의 풍부한 표현을 학습하기 위해 양방향의 단어에 주목함으로써 심층 양방향성(deep bidirectionality)을 효과적으로 구현한 최초의 모델입니다.
+
+1. BERT는 [WordPiece](tokenizer_summary#wordpiece) 토큰화를 사용하여 문장의 토큰 임베딩을 생성합니다. 단일 문장과 한 쌍의 문장을 구분하기 위해 특수한 `[SEP]` 토큰이 추가됩니다. 모든 텍스트 시퀀스의 시작 부분에는 특수한 `[CLS]` 토큰이 추가됩니다. `[CLS]` 토큰이 있는 최종 출력은 분류 작업을 위한 분류 헤드로 입력에 사용됩니다. BERT는 또한 한 쌍의 문장에서 각 토큰이 첫 번째 문장인지 두 번째 문장에 속하는지 나타내는 세그먼트 임베딩(segment embedding)을 추가합니다.
+
+2. BERT는 마스크드 언어 모델링과 다음 문장 예측, 두 가지 목적으로 사전훈련됩니다. 마스크드 언어 모델링에서는 입력 토큰의 일부가 무작위로 마스킹되고, 모델은 이를 예측해야 합니다. 이는 모델이 모든 단어를 보고 다음 단어를 "예측"할 수 있는 양방향성 문제를 해결합니다. 예측된 마스크 토큰의 최종 은닉 상태는 어휘에 대한 소프트맥스가 있는 순방향 네트워크로 전달되어 마스크된 단어를 예측합니다.
+
+    두 번째 사전훈련 대상은 다음 문장 예측입니다. 모델은 문장 B가 문장 A 다음에 오는지 예측해야 합니다. 문장 B가 다음 문장인 경우와 무작위 문장인 경우 각각 50%의 확률로 발생합니다. 다음 문장인지 아닌지에 대한 예측은 두 개의 클래스(`IsNext` 및 `NotNext`)에 대한 소프트맥스가 있는 순방향 네트워크로 전달됩니다.
+
+3. 입력 임베딩은 여러 인코더 레이어를 거쳐서 최종 은닉 상태를 출력합니다.
+
+사전훈련된 모델을 텍스트 분류에 사용하려면, 기본 BERT 모델 상단에 시퀀스 분류 헤드를 추가합니다. 시퀀스 분류 헤드는 최종 은닉 상태를 받는 선형 레이어이며, 로짓으로 변환하기 위해 선형 변환을 수행합니다. 교차 엔트로피 손실은 로짓과 타겟 간에 계산되어 가장 가능성이 높은 레이블을 찾습니다. 
+
+텍스트 분류에 직접 도전할 준비가 되셨나요? 완전한 [텍스트 분류 가이드](tasks/sequence_classification)를 확인하여 DistilBERT를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+### 토큰 분류[[token-classification]]
+
+개체명 인식(Named Entity Recognition, NER)과 같은 토큰 분류 작업에 BERT를 사용하려면, 기본 BERT 모델 상단에 토큰 분류 헤드를 추가합니다. 토큰 분류 헤드는 최종 은닉 상태를 받는 선형 레이어이며, 로짓으로 변환하기 위해 선형 변환을 수행합니다. 교차 엔트로피 손실은 로짓과 각 토큰 간에 계산되어 가장 가능성이 높은 레이블을 찾습니다. 
+
+토큰 분류에 직접 도전할 준비가 되셨나요? 완전한 [토큰 분류 가이드](tasks/token_classification)를 확인하여 DistilBERT를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+### 질의응답[[question-answering]]
+
+질의응답에 BERT를 사용하려면, 기본 BERT 모델 위에 스팬(span) 분류 헤드를 추가합니다. 이 선형 레이어는 최종 은닉 상태를 받고, 답변에 대응하는 `스팬`의 시작과 끝 로그를 계산하기 위해 선형 변환을 수행합니다. 교차 엔트로피 손실은 로짓과 각 레이블 위치 간에 계산되어 답변에 대응하는 가장 가능성이 높은 텍스트의 스팬을 찾습니다. 
+
+질의응답에 직접 도전할 준비가 되셨나요? 완전한 [질의응답 가이드](tasks/question_answering)를 확인하여 DistilBERT를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+<Tip>
+
+💡 사전훈련된 BERT를 다양한 작업에 사용하는 것이 얼마나 쉬운지 주목하세요. 사전훈련된 모델에 특정 헤드를 추가하기만 하면 은닉 상태를 원하는 출력으로 조작할 수 있습니다!
+
+</Tip>
+
+### 텍스트 생성[[text-generation]]
+
+[GPT-2](model_doc/gpt2)는 대량의 텍스트에 대해 사전훈련된 디코딩 전용 모델입니다. 프롬프트를 주어지면 설득력 있는 (항상 사실은 아니지만!) 텍스트를 생성하고 명시적으로 훈련되지 않았음에도 불구하고 질의응답과 같은 다른 NLP 작업을 완수할 수 있습니다.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gpt2_architecture.png"/>
+</div>
+
+1. GPT-2는 단어를 토큰화하고 토큰 임베딩을 생성하기 위해 [바이트 페어 인코딩(BPE, byte pair encoding)](tokenizer_summary#bytepair-encoding-bpe)을 사용합니다. 위치 인코딩은 시퀀스에서 각 토큰의 위치를 나타내기 위해 토큰 임베딩에 추가됩니다. 입력 임베딩은 여러 디코더 블록을 거쳐 일부 최종 은닉 상태를 출력합니다. 각 디코더 블록 내에서 GPT-2는 *마스크드 셀프 어텐션(masked self-attention)* 레이어를 사용합니다. 이는 GPT-2가 이후 토큰(future tokens)에 주의를 기울일 수 없도록 합니다. 왼쪽에 있는 토큰에만 주의를 기울일 수 있습니다. 마스크드 셀프 어텐션에서는 어텐션 마스크를 사용하여 이후 토큰에 대한 점수(score)를 `0`으로 설정하기 때문에 BERT의 [`mask`] 토큰과 다릅니다.
+
+2. 디코더의 출력은 언어 모델링 헤드에 전달되며, 언어 모델링 헤드는 은닉 상태를 로짓으로 선형 변환을 수행합니다. 레이블은 시퀀스의 다음 토큰으로, 로짓을 오른쪽으로 하나씩 이동하여 생성됩니다. 교차 엔트로피 손실은 이동된 로짓과 레이블 간에 계산되어 가장 가능성이 높은 다음 토큰을 출력합니다.
+
+GPT-2의 사전훈련 목적은 전적으로 [인과적 언어 모델링](glossary#causal-language-modeling)에 기반하여, 시퀀스에서 다음 단어를 예측하는 것입니다. 이는 GPT-2가 텍스트 생성에 관련된 작업에 특히 우수하도록 합니다.
+
+텍스트 생성에 직접 도전할 준비가 되셨나요? 완전한 [인과적 언어 모델링 가이드](tasks/language_modeling#causal-language-modeling)를 확인하여 DistilGPT-2를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+<Tip>
+
+텍스트 생성에 대한 자세한 내용은 [텍스트 생성 전략](generation_strategies) 가이드를 확인하세요!
+
+</Tip>
+
+### 요약[[summarization]]
+
+[BART](model_doc/bart) 및 [T5](model_doc/t5)와 같은 인코더-디코더 모델은 요약 작업의 시퀀스-투-시퀀스 패턴을 위해 설계되었습니다. 이 섹션에서 BART의 작동 방법을 설명한 다음, 마지막에 T5를 미세 조정해볼 수 있습니다. 
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bart_architecture.png"/>
+</div>
+
+1. BART의 인코더 아키텍처는 BERT와 매우 유사하며 텍스트의 토큰 및 위치 임베딩을 받습니다. BART는 입력을 변형시키고 디코더로 재구성하여 사전훈련됩니다. 특정 변형 기법이 있는 다른 인코더와는 달리, BART는 모든 유형의 변형을 적용할 수 있습니다. 그러나 *text infilling* 변형 기법이 가장 잘 작동합니다. Text Infiling에서는 여러 텍스트 스팬을 **단일** [`mask`] 토큰으로 대체합니다. 이는 모델이 마스크된 토큰을 예측해야 하고, 모델에 누락된 토큰의 수를 예측하도록 가르치기 때문에 중요합니다. 입력 임베딩과 마스크된 스팬이 인코더를 거쳐 최종 은닉 상태를 출력하지만, BERT와 달리 BART는 마지막에 단어를 예측하는 순방향 네트워크를 추가하지 않습니다.
+
+2. 인코더의 출력은 디코더로 전달되며, 디코더는 인코더의 출력에서 마스크 토큰과 변형되지 않은 토큰을 예측해야 합니다. 이는 디코더가 원본 텍스트를 복원하는 데 도움이 되는 추가적인 문맥을 얻도록 합니다. 디코더의 출력은 언어 모델링 헤드에 전달되며, 언어 모델링 헤드는 은닉 상태를 로짓으로 선형 변환을 수행합니다. 교차 엔트로피 손실은 로짓과 토큰이 오른쪽으로 이동된 레이블 간에 계산됩니다.
+
+요약에 직접 도전할 준비가 되셨나요? 완전한 [요약 가이드](tasks/summarization)를 확인하여 T5를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+<Tip>
+
+텍스트 생성에 대한 자세한 내용은 [텍스트 생성 전략](generation_strategies) 가이드를 확인하세요!
+
+</Tip>
+
+### 번역[[translation]]
+
+번역은 시퀀스-투-시퀀스 작업의 또 다른 예로, [BART](model_doc/bart) 또는 [T5](model_doc/t5)와 같은 인코더-디코더 모델을 사용할 수 있습니다. 이 섹션에서 BART의 작동 방법을 설명한 다음, 마지막에 T5를 미세 조정해볼 수 있습니다. 
+
+BART는 원천 언어를 타겟 언어로 디코딩할 수 있는 입력에 매핑하기 위해 무작위로 초기화된 별도의 인코더를 추가하여 번역에 적용합니다. 이 새로운 인코더의 임베딩은 원본 단어 임베딩 대신 사전훈련된 인코더로 전달됩니다. 원천 인코더는 모델 출력의 교차 엔트로피 손실로부터 원천 인코더, 위치 임베딩, 입력 임베딩을 갱신하여 훈련됩니다. 첫 번째 단계에서는 모델 파라미터가 고정되고, 두 번째 단계에서는 모든 모델 파라미터가 함께 훈련됩니다.
+
+BART는 이후 번역을 위해 다양한 언어로 사전훈련된 다국어 버전의 mBART로 확장되었습니다.
+
+번역에 직접 도전할 준비가 되셨나요? 완전한 [번역 가이드](tasks/summarization)를 확인하여 T5를 미세 조정하고 추론에 사용하는 방법을 학습하세요!
+
+<Tip>
+
+텍스트 생성에 대한 자세한 내용은 [텍스트 생성 전략](generation_strategies) 가이드를 확인하세요!
+
+</Tip>

docs/source/ko/troubleshooting.mdx

@@ -0,0 +1,194 @@
+<!---
+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.
+-->
+
+# 문제 해결[[troubleshoot]]
+
+때때로 오류가 발생할 수 있지만, 저희가 도와드리겠습니다! 이 가이드는 현재까지 확인된 가장 일반적인 문제 몇 가지와 그것들을 해결하는 방법에 대해 다룹니다. 그러나 이 가이드는 모든 🤗 Transformers 문제를 포괄적으로 다루고 있지 않습니다. 문제 해결에 더 많은 도움을 받으려면 다음을 시도해보세요:
+
+<Youtube id="S2EEG3JIt2A"/>
+
+1. [포럼](https://discuss.huggingface.co/)에서 도움을 요청하세요. [Beginners](https://discuss.huggingface.co/c/beginners/5) 또는 [🤗 Transformers](https://discuss.huggingface.co/c/transformers/9)와 같은 특정 카테고리에 질문을 게시할 수 있습니다. 재현 가능한 코드와 함께 잘 서술된 포럼 게시물을 작성하여 여러분의 문제가 해결될 가능성을 극대화하세요!
+
+<Youtube id="_PAli-V4wj0"/>
+
+2. 라이브러리와 관련된 버그이면 🤗 Transformers 저장소에서 [이슈](https://github.com/huggingface/transformers/issues/new/choose)를 생성하세요. 버그에 대해 설명하는 정보를 가능한 많이 포함하려고 노력하여, 무엇이 잘못 되었는지와 어떻게 수정할 수 있는지 더 잘 파악할 수 있도록 도와주세요.
+
+3. 이전 버전의 🤗 Transformers을 사용하는 경우 중요한 변경 사항이 버전 사이에 도입되었기 때문에 [마이그레이션](migration) 가이드를 확인하세요.
+
+문제 해결 및 도움 매뉴얼에 대한 자세한 내용은 Hugging Face 강좌의 [8장](https://huggingface.co/course/chapter8/1?fw=pt)을 참조하세요.
+
+
+## 방화벽 환경[[firewalled-environments]]
+
+클라우드 및 내부망(intranet) 설정의 일부 GPU 인스턴스는 외부 연결에 대한 방화벽으로 차단되어 연결 오류가 발생할 수 있습니다. 스크립트가 모델 가중치나 데이터를 다운로드하려고 할 때, 다운로드가 중단되고 다음 메시지와 함께 시간 초과됩니다: 
+
+```
+ValueError: Connection error, and we cannot find the requested files in the cached path.
+Please try again or make sure your Internet connection is on.
+```
+
+이 경우에는 연결 오류를 피하기 위해 🤗 Transformers를 [오프라인 모드](installation#offline-mode)로 실행해야 합니다.
+
+## CUDA 메모리 부족(CUDA out of memory)[[cuda-out-of-memory]]
+
+수백만 개의 매개변수로 대규모 모델을 훈련하는 것은 적절한 하드웨어 없이 어려울 수 있습니다. GPU 메모리가 부족한 경우 발생할 수 있는 일반적인 오류는 다음과 같습니다:
+
+```
+CUDA out of memory. Tried to allocate 256.00 MiB (GPU 0; 11.17 GiB total capacity; 9.70 GiB already allocated; 179.81 MiB free; 9.85 GiB reserved in total by PyTorch)
+```
+
+다음은 메모리 사용을 줄이기 위해 시도해 볼 수 있는 몇 가지 잠재적인 해결책입니다:
+
+- [`TrainingArguments`]의 [`per_device_train_batch_size`](main_classes/trainer#transformers.TrainingArguments.per_device_train_batch_size) 값을 줄이세요.
+- [`TrainingArguments`]의 [`gradient_accumulation_steps`](main_classes/trainer#transformers.TrainingArguments.gradient_accumulation_steps)은 전체 배치 크기를 효과적으로 늘리세요.
+
+<Tip>
+
+메모리 절약 기술에 대한 자세한 내용은 성능 [가이드](performance)를 참조하세요.
+
+</Tip>
+
+## 저장된 TensorFlow 모델을 가져올 수 없습니다(Unable to load a saved TensorFlow model)[[unable-to-load-a-saved-uensorFlow-model]]
+
+TensorFlow의 [model.save](https://www.tensorflow.org/tutorials/keras/save_and_load#save_the_entire_model) 메소드는 아키텍처, 가중치, 훈련 구성 등 전체 모델을 단일 파일에 저장합니다. 그러나 모델 파일을 다시 가져올 때 🤗 Transformers는 모델 파일에 있는 모든 TensorFlow 관련 객체를 가져오지 않을 수 있기 때문에 오류가 발생할 수 있습니다. TensorFlow 모델 저장 및 가져오기 문제를 피하려면 다음을 권장합니다:
+
+- 모델 가중치를 `h5` 파일 확장자로 [`model.save_weights`](https://www.tensorflow.org/tutorials/keras/save_and_load#save_the_entire_model)로 저장한 다음 [`~TFPreTrainedModel.from_pretrained`]로 모델을 다시 가져옵니다:
+
+```py
+>>> from transformers import TFPreTrainedModel
+>>> from tensorflow import keras
+
+>>> model.save_weights("some_folder/tf_model.h5")
+>>> model = TFPreTrainedModel.from_pretrained("some_folder")
+```
+
+- 모델을 [`~TFPretrainedModel.save_pretrained`]로 저장하고 [`~TFPreTrainedModel.from_pretrained`]로 다시 가져옵니다:
+
+```py
+>>> from transformers import TFPreTrainedModel
+
+>>> model.save_pretrained("path_to/model")
+>>> model = TFPreTrainedModel.from_pretrained("path_to/model")
+```
+
+## ImportError[[importerror]]
+
+특히 최신 모델인 경우 만날 수 있는 다른 일반적인 오류는 `ImportError`입니다:
+
+```
+ImportError: cannot import name 'ImageGPTImageProcessor' from 'transformers' (unknown location)
+```
+
+이러한 오류 유형의 경우 최신 모델에 액세스할 수 있도록 최신 버전의 🤗 Transformers가 설치되어 있는지 확인하세요:
+
+```bash
+pip install transformers --upgrade
+```
+
+## CUDA error: device-side assert triggered[[cuda-error-deviceside-assert-triggered]]
+
+때때로 장치 코드 오류에 대한 일반적인 CUDA 오류가 발생할 수 있습니다.
+
+```
+RuntimeError: CUDA error: device-side assert triggered
+```
+
+더 자세한 오류 메시지를 얻으려면 우선 코드를 CPU에서 실행합니다. 다음 환경 변수를 코드의 시작 부분에 추가하여 CPU로 전환하세요:
+
+```py
+>>> import os
+
+>>> os.environ["CUDA_VISIBLE_DEVICES"] = ""
+```
+
+또 다른 옵션은 GPU에서 더 나은 역추적(traceback)을 얻는 것입니다. 다음 환경 변수를 코드의 시작 부분에 추가하여 역추적이 오류가 발생한 소스를 가리키도록 하세요:
+
+```py
+>>> import os
+
+>>> os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
+```
+
+## 패딩 토큰이 마스킹되지 않은 경우 잘못된 출력(Incorrect output when padding tokens aren't masked)[[incorrect-output-when-padding-tokens-arent-masked]]
+
+경우에 따라 `input_ids`에 패딩 토큰이 포함된 경우 `hidden_state` 출력이 올바르지 않을 수 있습니다. 데모를 위해 모델과 토크나이저를 가져오세요. 모델의 `pad_token_id`에 액세스하여 해당 값을 확인할 수 있습니다. 일부 모델의 경우 `pad_token_id`가 `None`일 수 있지만 언제든지 수동으로 설정할 수 있습니다.
+
+```py
+>>> from transformers import AutoModelForSequenceClassification
+>>> import torch
+
+>>> model = AutoModelForSequenceClassification.from_pretrained("bert-base-uncased")
+>>> model.config.pad_token_id
+0
+```
+
+다음 예제는 패딩 토큰을 마스킹하지 않은 출력을 보여줍니다:
+
+```py
+>>> input_ids = torch.tensor([[7592, 2057, 2097, 2393, 9611, 2115], [7592, 0, 0, 0, 0, 0]])
+>>> output = model(input_ids)
+>>> print(output.logits)
+tensor([[ 0.0082, -0.2307],
+        [ 0.1317, -0.1683]], grad_fn=<AddmmBackward0>)
+```
+
+다음은 두 번째 시퀀스의 실제 출력입니다:
+
+```py
+>>> input_ids = torch.tensor([[7592]])
+>>> output = model(input_ids)
+>>> print(output.logits)
+tensor([[-0.1008, -0.4061]], grad_fn=<AddmmBackward0>)
+```
+
+대부분의 경우 모델에 `attention_mask`를 제공하여 패딩 토큰을 무시해야 이러한 조용한 오류를 방지할 수 있습니다. 이제 두 번째 시퀀스의 출력이 실제 출력과 일치합니다:
+
+<Tip>
+
+일반적으로 토크나이저는 특정 토크나이저의 기본 값을 기준으로 사용자에 대한 'attention_mask'를 만듭니다.
+
+</Tip>
+
+```py
+>>> attention_mask = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 0, 0, 0, 0, 0]])
+>>> output = model(input_ids, attention_mask=attention_mask)
+>>> print(output.logits)
+tensor([[ 0.0082, -0.2307],
+        [-0.1008, -0.4061]], grad_fn=<AddmmBackward0>)
+```
+
+🤗 Transformers는 패딩 토큰이 제공된 경우 패딩 토큰을 마스킹하기 위한 `attention_mask`를 자동으로 생성하지 않습니다. 그 이유는 다음과 같습니다:
+
+- 일부 모델에는 패딩 토큰이 없습니다.
+- 일부 사용 사례의 경우 사용자가 모델이 패딩 토큰을 관리하기를 원합니다.
+
+## ValueError: 이 유형의 AutoModel에 대해 인식할 수 없는 XYZ 구성 클래스(ValueError: Unrecognized configuration class XYZ for this kind of AutoModel)[[valueerror-unrecognized-configuration-class-xyz-for-this-kind-of-automodel]]
+
+일반적으로, 사전 학습된 모델의 인스턴스를 가져오기 위해 [`AutoModel`] 클래스를 사용하는 것이 좋습니다.
+이 클래스는 구성에 따라 주어진 체크포인트에서 올바른 아키텍처를 자동으로 추론하고 가져올 수 있습니다.
+모델을 체크포인트에서 가져올 때 이 `ValueError`가 발생하면, 이는 Auto 클래스가 주어진 체크포인트의 구성에서 
+가져오려는 모델 유형과 매핑을 찾을 수 없다는 것을 의미합니다. 가장 흔하게 발생하는 경우는 
+체크포인트가 주어진 태스크를 지원하지 않을 때입니다.
+예를 들어, 다음 예제에서 질의응답에 대한 GPT2가 없기 때문에 오류가 발생합니다:
+
+```py
+>>> from transformers import AutoProcessor, AutoModelForQuestionAnswering
+
+>>> processor = AutoProcessor.from_pretrained("gpt2-medium")
+>>> model = AutoModelForQuestionAnswering.from_pretrained("gpt2-medium")
+ValueError: Unrecognized configuration class <class 'transformers.models.gpt2.configuration_gpt2.GPT2Config'> for this kind of AutoModel: AutoModelForQuestionAnswering.
+Model type should be one of AlbertConfig, BartConfig, BertConfig, BigBirdConfig, BigBirdPegasusConfig, BloomConfig, ...
+```

docs/source/ms/_toctree.yml

@@ -0,0 +1,688 @@
+- sections:
+    - local: index
+      title: 🤗 Transformers
+    - local: quicktour
+      title: Lawatan cepat
+    - local: installation
+      title: Pemasangan
+  title: Mulakan
+- sections:
+    - local: pipeline_tutorial
+      title: Jalankan inferens dengan saluran paip
+    - local: autoclass_tutorial
+      title: Tulis kod mudah alih dengan AutoClass
+    - local: preprocessing
+      title: Praproses data
+    - local: training
+      title: Perhalusi model yang telah dilatih
+    - local: run_scripts
+      title: Latih dengan skrip
+    - local: accelerate
+      title: Sediakan latihan yang diedarkan dengan 🤗 Accelerate
+    - local: model_sharing
+      title: Kongsi model anda
+    - local: transformers_agents
+      title: Ejen
+  title: Tutorials
+- sections:
+    - sections:
+        - local: tasks/sequence_classification
+          title: Klasifikasi teks
+        - local: tasks/token_classification
+          title: Klasifikasi token
+        - local: tasks/question_answering
+          title: Soalan menjawab
+        - local: tasks/language_modeling
+          title: Pemodelan bahasa sebab-akibat
+        - local: tasks/masked_language_modeling
+          title: Pemodelan bahasa Masked
+        - local: tasks/translation
+          title: Terjemahan
+        - local: tasks/summarization
+          title: Rumusan
+        - local: tasks/multiple_choice
+          title: Pilihan
+      title: Natural Language Processing
+      isExpanded: false
+    - sections:
+        - local: tasks/audio_classification
+          title: Klasifikasi audio
+        - local: tasks/asr
+          title: Pengecaman pertuturan automatik
+      title: Audio
+      isExpanded: false
+    - sections:
+        - local: tasks/image_classification
+          title: Klasifikasi imej
+        - local: tasks/semantic_segmentation
+          title: Segmentasi semantik
+        - local: tasks/video_classification
+          title: Klasifikasi video
+        - local: tasks/object_detection
+          title: Pengesanan objek
+        - local: tasks/zero_shot_object_detection
+          title: Pengesanan objek Zero-Shot
+        - local: tasks/zero_shot_image_classification
+          title: Klasifikasi imej tangkapan Zero-Shot
+        - local: tasks/monocular_depth_estimation
+          title: Anggaran kedalaman
+      title: Visi komputer
+      isExpanded: false
+    - sections:
+        - local: tasks/image_captioning
+          title: Kapsyen imej
+        - local: tasks/document_question_answering
+          title: Menjawab Soalan Dokumen
+        - local: tasks/text-to-speech
+          title: Teks kepada ucapan
+      title: Multimodal
+      isExpanded: false
+  title: Panduan Tugasan
+- sections:
+    - local: fast_tokenizers
+      title: Gunakan tokenizer cepat dari 🤗 Tokenizers
+    - local: multilingual
+      title: Jalankan inferens dengan model berbilang bahasa
+    - local: generation_strategies
+      title: Sesuaikan strategi penjanaan teks
+    - local: create_a_model
+      title: Gunakan API khusus model
+    - local: custom_models
+      title: Kongsi model tersuai
+    - local: sagemaker
+      title: Jalankan latihan di Amazon SageMaker
+    - local: serialization
+      title: Eksport ke ONNX
+    - local: torchscript
+      title: Eksport ke TorchScript
+    - local: benchmarks
+      title: Penanda aras
+    - local: Buku nota dengan contoh
+      title: Notebooks with examples
+    - local: Sumber komuniti
+      title: Community resources
+    - local: Sumber komuniti
+      title: Custom Tools and Prompts
+    - local: Alat dan Gesaan Tersuai
+      title: Selesaikan masalah
+  title: Panduan Developer
+- sections:
+    - local: performance
+      title: Gambaran keseluruhan
+    - local: perf_train_gpu_one
+      title: Latihan pada satu GPU
+    - local: perf_train_gpu_many
+      title: Latihan pada banyak GPU
+    - local: perf_train_cpu
+      title: Latihan mengenai CPU
+    - local: perf_train_cpu_many
+      title: Latihan pada banyak CPU
+    - local: perf_train_tpu
+      title: Latihan mengenai TPU
+    - local: perf_train_tpu_tf
+      title: Latihan tentang TPU dengan TensorFlow
+    - local: perf_train_special
+      title: Latihan mengenai Perkakasan Khusus
+    - local: perf_infer_cpu
+      title: Inferens pada CPU
+    - local: perf_infer_gpu_one
+      title: Inferens pada satu GPU
+    - local: perf_infer_gpu_many
+      title: Inferens pada banyak GPUs
+    - local: perf_infer_special
+      title: Inferens pada Perkakasan Khusus
+    - local: perf_hardware
+      title: Perkakasan tersuai untuk latihan
+    - local: big_models
+      title: Menghidupkan model besar
+    - local: debugging
+      title: Penyahpepijatan
+    - local: hpo_train
+      title: Carian Hiperparameter menggunakan API Pelatih
+    - local: tf_xla
+      title: Penyepaduan XLA untuk Model TensorFlow
+  title: Prestasi dan kebolehskalaan
+- sections:
+    - local: contributing
+      title: Bagaimana untuk menyumbang kepada transformer?
+    - local: add_new_model
+      title: Bagaimana untuk menambah model pada 🤗 Transformers?
+    - local: add_tensorflow_model
+      title: Bagaimana untuk menukar model Transformers kepada TensorFlow?
+    - local: add_new_pipeline
+      title: Bagaimana untuk menambah saluran paip ke 🤗 Transformers?
+    - local: testing
+      title: Ujian
+    - local: pr_checks
+      title: Menyemak Permintaan Tarik
+  title: Sumbangkan
+
+- sections:
+    - local: philosophy
+      title: Falsafah
+    - local: glossary
+      title: Glosari
+    - local: task_summary
+      title: Apa 🤗 Transformers boleh buat
+    - local: tasks_explained
+      title: Bagaimana 🤗 Transformers menyelesaikan tugasan
+    - local: model_summary
+      title: Keluarga model Transformer
+    - local: tokenizer_summary
+      title: Ringkasan tokenizer
+    - local: attention
+      title: Mekanisme perhatian
+    - local: pad_truncation
+      title: Padding dan pemotongan
+    - local: bertology
+      title: BERTology
+    - local: perplexity
+      title: Kekeliruan model panjang tetap
+    - local: pipeline_webserver
+      title: Saluran paip untuk inferens pelayan web
+  title: Panduan konsep
+- sections:
+    - sections:
+        - local: main_classes/agent
+          title: Ejen dan Alat
+        - local: model_doc/auto
+          title: Kelas Auto
+        - local: main_classes/callback
+          title: Panggilan balik
+        - local: main_classes/configuration
+          title: Configuration
+        - local: main_classes/data_collator
+          title: Data Collator
+        - local: main_classes/keras_callbacks
+          title: Keras callbacks
+        - local: main_classes/logging
+          title: Logging
+        - local: main_classes/model
+          title: Models
+        - local: main_classes/text_generation
+          title: Text Generation
+        - local: main_classes/onnx
+          title: ONNX
+        - local: main_classes/optimizer_schedules
+          title: Optimization
+        - local: main_classes/output
+          title: Model outputs
+        - local: main_classes/pipelines
+          title: Pipelines
+        - local: main_classes/processors
+          title: Processors
+        - local: main_classes/quantization
+          title: Quantization
+        - local: main_classes/tokenizer
+          title: Tokenizer
+        - local: main_classes/trainer
+          title: Trainer
+        - local: main_classes/deepspeed
+          title: DeepSpeed Integration
+        - local: main_classes/feature_extractor
+          title: Feature Extractor
+        - local: main_classes/image_processor
+          title: Image Processor
+      title: Main Classes
+    - sections:
+        - isExpanded: false
+          sections:
+            - local: model_doc/albert
+              title: ALBERT
+            - local: model_doc/bart
+              title: BART
+            - local: model_doc/barthez
+              title: BARThez
+            - local: model_doc/bartpho
+              title: BARTpho
+            - local: model_doc/bert
+              title: BERT
+            - local: model_doc/bert-generation
+              title: BertGeneration
+            - local: model_doc/bert-japanese
+              title: BertJapanese
+            - local: model_doc/bertweet
+              title: Bertweet
+            - local: model_doc/big_bird
+              title: BigBird
+            - local: model_doc/bigbird_pegasus
+              title: BigBirdPegasus
+            - local: model_doc/biogpt
+              title: BioGpt
+            - local: model_doc/blenderbot
+              title: Blenderbot
+            - local: model_doc/blenderbot-small
+              title: Blenderbot Small
+            - local: model_doc/bloom
+              title: BLOOM
+            - local: model_doc/bort
+              title: BORT
+            - local: model_doc/byt5
+              title: ByT5
+            - local: model_doc/camembert
+              title: CamemBERT
+            - local: model_doc/canine
+              title: CANINE
+            - local: model_doc/codegen
+              title: CodeGen
+            - local: model_doc/convbert
+              title: ConvBERT
+            - local: model_doc/cpm
+              title: CPM
+            - local: model_doc/cpmant
+              title: CPMANT
+            - local: model_doc/ctrl
+              title: CTRL
+            - local: model_doc/deberta
+              title: DeBERTa
+            - local: model_doc/deberta-v2
+              title: DeBERTa-v2
+            - local: model_doc/dialogpt
+              title: DialoGPT
+            - local: model_doc/distilbert
+              title: DistilBERT
+            - local: model_doc/dpr
+              title: DPR
+            - local: model_doc/electra
+              title: ELECTRA
+            - local: model_doc/encoder-decoder
+              title: Encoder Decoder Models
+            - local: model_doc/ernie
+              title: ERNIE
+            - local: model_doc/ernie_m
+              title: ErnieM
+            - local: model_doc/esm
+              title: ESM
+            - local: model_doc/flan-t5
+              title: FLAN-T5
+            - local: model_doc/flan-ul2
+              title: FLAN-UL2
+            - local: model_doc/flaubert
+              title: FlauBERT
+            - local: model_doc/fnet
+              title: FNet
+            - local: model_doc/fsmt
+              title: FSMT
+            - local: model_doc/funnel
+              title: Funnel Transformer
+            - local: model_doc/openai-gpt
+              title: GPT
+            - local: model_doc/gpt_neo
+              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
+              title: GPT2
+            - local: model_doc/gpt_bigcode
+              title: GPTBigCode
+            - local: model_doc/gptsan-japanese
+              title: GPTSAN Japanese
+            - local: model_doc/gpt-sw3
+              title: GPTSw3
+            - local: model_doc/herbert
+              title: HerBERT
+            - local: model_doc/ibert
+              title: I-BERT
+            - local: model_doc/jukebox
+              title: Jukebox
+            - local: model_doc/led
+              title: LED
+            - local: model_doc/llama
+              title: LLaMA
+            - local: model_doc/longformer
+              title: Longformer
+            - local: model_doc/longt5
+              title: LongT5
+            - local: model_doc/luke
+              title: LUKE
+            - local: model_doc/m2m_100
+              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/mega
+              title: MEGA
+            - local: model_doc/megatron-bert
+              title: MegatronBERT
+            - local: model_doc/megatron_gpt2
+              title: MegatronGPT2
+            - local: model_doc/mluke
+              title: mLUKE
+            - local: model_doc/mobilebert
+              title: MobileBERT
+            - local: model_doc/mpnet
+              title: MPNet
+            - local: model_doc/mt5
+              title: MT5
+            - local: model_doc/mvp
+              title: MVP
+            - local: model_doc/nezha
+              title: NEZHA
+            - local: model_doc/nllb
+              title: NLLB
+            - local: model_doc/nllb-moe
+              title: NLLB-MoE
+            - local: model_doc/nystromformer
+              title: Nyströmformer
+            - local: model_doc/open-llama
+              title: Open-Llama
+            - local: model_doc/opt
+              title: OPT
+            - local: model_doc/pegasus
+              title: Pegasus
+            - local: model_doc/pegasus_x
+              title: PEGASUS-X
+            - local: model_doc/phobert
+              title: PhoBERT
+            - local: model_doc/plbart
+              title: PLBart
+            - local: model_doc/prophetnet
+              title: ProphetNet
+            - local: model_doc/qdqbert
+              title: QDQBert
+            - local: model_doc/rag
+              title: RAG
+            - local: model_doc/realm
+              title: REALM
+            - local: model_doc/reformer
+              title: Reformer
+            - local: model_doc/rembert
+              title: RemBERT
+            - local: model_doc/retribert
+              title: RetriBERT
+            - local: model_doc/roberta
+              title: RoBERTa
+            - local: model_doc/roberta-prelayernorm
+              title: RoBERTa-PreLayerNorm
+            - local: model_doc/roc_bert
+              title: RoCBert
+            - local: model_doc/roformer
+              title: RoFormer
+            - local: model_doc/rwkv
+              title: RWKV
+            - local: model_doc/splinter
+              title: Splinter
+            - local: model_doc/squeezebert
+              title: SqueezeBERT
+            - local: model_doc/switch_transformers
+              title: SwitchTransformers
+            - local: model_doc/t5
+              title: T5
+            - local: model_doc/t5v1.1
+              title: T5v1.1
+            - local: model_doc/tapex
+              title: TAPEX
+            - local: model_doc/transfo-xl
+              title: Transformer XL
+            - local: model_doc/ul2
+              title: UL2
+            - local: model_doc/xmod
+              title: X-MOD
+            - local: model_doc/xglm
+              title: XGLM
+            - local: model_doc/xlm
+              title: XLM
+            - local: model_doc/xlm-prophetnet
+              title: XLM-ProphetNet
+            - local: model_doc/xlm-roberta
+              title: XLM-RoBERTa
+            - local: model_doc/xlm-roberta-xl
+              title: XLM-RoBERTa-XL
+            - local: model_doc/xlm-v
+              title: XLM-V
+            - local: model_doc/xlnet
+              title: XLNet
+            - local: model_doc/yoso
+              title: YOSO
+          title: Text models
+        - isExpanded: false
+          sections:
+            - local: model_doc/beit
+              title: BEiT
+            - local: model_doc/bit
+              title: BiT
+            - local: model_doc/conditional_detr
+              title: Conditional DETR
+            - local: model_doc/convnext
+              title: ConvNeXT
+            - local: model_doc/convnextv2
+              title: ConvNeXTV2
+            - local: model_doc/cvt
+              title: CvT
+            - local: model_doc/deformable_detr
+              title: Deformable DETR
+            - local: model_doc/deit
+              title: DeiT
+            - local: model_doc/deta
+              title: DETA
+            - local: model_doc/detr
+              title: DETR
+            - local: model_doc/dinat
+              title: DiNAT
+            - local: model_doc/dit
+              title: DiT
+            - local: model_doc/dpt
+              title: DPT
+            - local: model_doc/efficientformer
+              title: EfficientFormer
+            - local: model_doc/efficientnet
+              title: EfficientNet
+            - local: model_doc/focalnet
+              title: FocalNet
+            - local: model_doc/glpn
+              title: GLPN
+            - local: model_doc/imagegpt
+              title: ImageGPT
+            - local: model_doc/levit
+              title: LeViT
+            - local: model_doc/mask2former
+              title: Mask2Former
+            - local: model_doc/maskformer
+              title: MaskFormer
+            - local: model_doc/mobilenet_v1
+              title: MobileNetV1
+            - local: model_doc/mobilenet_v2
+              title: MobileNetV2
+            - local: model_doc/mobilevit
+              title: MobileViT
+            - local: model_doc/nat
+              title: NAT
+            - local: model_doc/poolformer
+              title: PoolFormer
+            - local: model_doc/regnet
+              title: RegNet
+            - local: model_doc/resnet
+              title: ResNet
+            - local: model_doc/segformer
+              title: SegFormer
+            - local: model_doc/swiftformer
+              title: SwiftFormer
+            - local: model_doc/swin
+              title: Swin Transformer
+            - local: model_doc/swinv2
+              title: Swin Transformer V2
+            - local: model_doc/swin2sr
+              title: Swin2SR
+            - local: model_doc/table-transformer
+              title: Table Transformer
+            - local: model_doc/timesformer
+              title: TimeSformer
+            - local: model_doc/upernet
+              title: UperNet
+            - local: model_doc/van
+              title: VAN
+            - local: model_doc/videomae
+              title: VideoMAE
+            - local: model_doc/vit
+              title: Vision Transformer (ViT)
+            - local: model_doc/vit_hybrid
+              title: ViT Hybrid
+            - local: model_doc/vit_mae
+              title: ViTMAE
+            - local: model_doc/vit_msn
+              title: ViTMSN
+            - local: model_doc/yolos
+              title: YOLOS
+          title: Vision models
+        - isExpanded: false
+          sections:
+            - local: model_doc/audio-spectrogram-transformer
+              title: Audio Spectrogram Transformer
+            - local: model_doc/clap
+              title: CLAP
+            - local: model_doc/hubert
+              title: Hubert
+            - local: model_doc/mctct
+              title: MCTCT
+            - local: model_doc/sew
+              title: SEW
+            - local: model_doc/sew-d
+              title: SEW-D
+            - local: model_doc/speech_to_text
+              title: Speech2Text
+            - local: model_doc/speech_to_text_2
+              title: Speech2Text2
+            - local: model_doc/speecht5
+              title: SpeechT5
+            - local: model_doc/unispeech
+              title: UniSpeech
+            - local: model_doc/unispeech-sat
+              title: UniSpeech-SAT
+            - local: model_doc/wav2vec2
+              title: Wav2Vec2
+            - local: model_doc/wav2vec2-conformer
+              title: Wav2Vec2-Conformer
+            - local: model_doc/wav2vec2_phoneme
+              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
+              title: XLSR-Wav2Vec2
+          title: Audio models
+        - isExpanded: false
+          sections:
+            - local: model_doc/align
+              title: ALIGN
+            - local: model_doc/altclip
+              title: AltCLIP
+            - local: model_doc/blip
+              title: BLIP
+            - local: model_doc/blip-2
+              title: BLIP-2
+            - local: model_doc/bridgetower
+              title: BridgeTower
+            - local: model_doc/chinese_clip
+              title: Chinese-CLIP
+            - local: model_doc/clip
+              title: CLIP
+            - local: model_doc/clipseg
+              title: CLIPSeg
+            - local: model_doc/data2vec
+              title: Data2Vec
+            - local: model_doc/deplot
+              title: DePlot
+            - local: model_doc/donut
+              title: Donut
+            - local: model_doc/flava
+              title: FLAVA
+            - local: model_doc/git
+              title: GIT
+            - local: model_doc/groupvit
+              title: GroupViT
+            - local: model_doc/layoutlm
+              title: LayoutLM
+            - local: model_doc/layoutlmv2
+              title: LayoutLMV2
+            - local: model_doc/layoutlmv3
+              title: LayoutLMV3
+            - local: model_doc/layoutxlm
+              title: LayoutXLM
+            - local: model_doc/lilt
+              title: LiLT
+            - local: model_doc/lxmert
+              title: LXMERT
+            - local: model_doc/matcha
+              title: MatCha
+            - local: model_doc/mgp-str
+              title: MGP-STR
+            - local: model_doc/oneformer
+              title: OneFormer
+            - local: model_doc/owlvit
+              title: OWL-ViT
+            - local: model_doc/perceiver
+              title: Perceiver
+            - local: model_doc/pix2struct
+              title: Pix2Struct
+            - local: model_doc/sam
+              title: Segment Anything
+            - local: model_doc/speech-encoder-decoder
+              title: Speech Encoder Decoder Models
+            - local: model_doc/tapas
+              title: TAPAS
+            - local: model_doc/trocr
+              title: TrOCR
+            - local: model_doc/tvlt
+              title: TVLT
+            - local: model_doc/vilt
+              title: ViLT
+            - local: model_doc/vision-encoder-decoder
+              title: Vision Encoder Decoder Models
+            - local: model_doc/vision-text-dual-encoder
+              title: Vision Text Dual Encoder
+            - local: model_doc/visual_bert
+              title: VisualBERT
+            - local: model_doc/xclip
+              title: X-CLIP
+          title: Multimodal models
+        - isExpanded: false
+          sections:
+            - local: model_doc/decision_transformer
+              title: Decision Transformer
+            - local: model_doc/trajectory_transformer
+              title: Trajectory Transformer
+          title: Reinforcement learning models
+        - isExpanded: false
+          sections:
+            - local: model_doc/informer
+              title: Informer
+            - local: model_doc/time_series_transformer
+              title: Time Series Transformer
+          title: Time series models
+        - isExpanded: false
+          sections:
+            - local: model_doc/graphormer
+              title: Graphormer
+          title: Graph models
+      title: Models
+    - sections:
+        - local: internal/modeling_utils
+          title: Custom Layers and Utilities
+        - local: internal/pipelines_utils
+          title: Utilities for pipelines
+        - local: internal/tokenization_utils
+          title: Utilities for Tokenizers
+        - local: internal/trainer_utils
+          title: Utilities for Trainer
+        - local: internal/generation_utils
+          title: Utilities for Generation
+        - local: internal/image_processing_utils
+          title: Utilities for Image Processors
+        - local: internal/audio_utils
+          title: Utilities for Audio processing
+        - local: internal/file_utils
+          title: General Utilities
+        - local: internal/time_series_utils
+          title: Utilities for Time Series
+      title: Internal Helpers
+  title: API

docs/source/ms/index.mdx

@@ -0,0 +1,456 @@
+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Dilesenkan di bawah Lesen Apache, Versi 2.0 ("Lesen"); anda tidak boleh menggunakan fail ini kecuali dengan mematuhi
+Lesen. Anda boleh mendapatkan salinan Lesen di
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Melainkan diperlukan oleh undang-undang yang terpakai atau dipersetujui secara bertulis, perisian yang diedarkan di bawah Lesen diedarkan pada
+ASAS ""SEBAGAIMANA ADANYA"", TANPA WARANTI ATAU SEBARANG JENIS SYARAT, sama ada nyata atau tersirat. Lihat Lesen untuk
+bahasa tertentu yang mengawal kebenaran dan pengehadan di bawah Lesen.
+-->
+
+# 🤗 Transformers
+
+Pembelajaran Mesin terkini untuk [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), dan [JAX](https://jax.readthedocs.io/en/latest/).
+
+🤗 Transformers menyediakan API dan alatan untuk memuat turun dan melatih model pra-latihan terkini dengan mudah. Menggunakan model terlatih boleh mengurangkan kos pengiraan anda, jejak karbon dan menjimatkan masa serta sumber yang diperlukan untuk melatih model dari awal. Model ini menyokong tugas biasa dalam modaliti yang berbeza, seperti:
+
+📝 **Natural Language Processing**: klasifikasi teks, pengecaman entiti bernama, menjawab soalan, pemodelan bahasa, ringkasan, terjemahan, pilihan berganda dan penjanaan teks.<br>
+🖼️ **Computer Vision**: pengelasan imej, pengesanan objek dan pembahagian.<br>
+🗣️ **Audio**: pengecaman pertuturan automatik dan klasifikasi audio.<br>
+🐙 **Multimodal**: jawapan soalan jadual, pengecaman aksara optik, pengekstrakan maklumat daripada dokumen yang diimbas, klasifikasi video dan jawapan soalan visual.
+
+🤗 Transformer menyokong kebolehoperasian rangka kerja antara PyTorch, TensorFlow, and JAX. Ini memberikan fleksibiliti untuk menggunakan rangka kerja yang berbeza pada setiap peringkat kehidupan model; latih model dalam tiga baris kod dalam satu rangka kerja, dan muatkannya untuk inferens dalam rangka kerja yang lain. Model juga boleh dieksport ke format seperti ONNX.
+
+Sertai komuniti yang semakin berkembang di [Hub](https://huggingface.co/models), [forum](https://discuss.huggingface.co/), atau [Discord](https://discord.com/invite/JfAtkvEtRb) hari ini!
+
+## Jika anda sedang mencari sokongan tersuai daripada pasukan 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="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
+</a>
+
+## Kandungan
+
+Dokumentasi disusun kepada lima bahagian:
+
+- **MULAKAN** menyediakan lawatan pantas ke perpustakaan dan arahan pemasangan untuk bangun dan berjalan.
+- **TUTORIAL** ialah tempat yang bagus untuk bermula jika anda seorang pemula. Bahagian ini akan membantu anda memperoleh kemahiran asas yang anda perlukan untuk mula menggunakan perpustakaan.
+- **PANDUAN CARA-CARA** menunjukkan kepada anda cara untuk mencapai matlamat tertentu, seperti memperhalusi model terlatih untuk pemodelan bahasa atau cara menulis dan berkongsi model tersuai.
+- **PANDUAN KONSEP** menawarkan lebih banyak perbincangan dan penjelasan tentang konsep dan idea asas di sebalik model, tugasan dan falsafah reka bentuk 🤗 Transformers.
+- **API** menerangkan semua kelas dan fungsi:
+
+  - **KELAS UTAMA** memperincikan kelas yang paling penting seperti konfigurasi, model, tokenizer dan saluran paip.
+  - **MODEL** memperincikan kelas dan fungsi yang berkaitan dengan setiap model yang dilaksanakan dalam perpustakaan.
+  - **PEMBANTU DALAMAN** memperincikan kelas utiliti dan fungsi yang digunakan secara dalaman.
+
+### Model yang disokong
+
+<!--Senarai ini dikemas kini secara automatik daripada README dengan _make fix-copies_. Jangan kemas kini secara manual! -->
+
+1. **[ALBERT](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. **[ALIGN](model_doc/align)** (from Google Research) released with the paper [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/abs/2102.05918) by Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yunhsuan Sung, Zhen Li, Tom Duerig.
+1. **[AltCLIP](model_doc/altclip)** (from BAAI) released with the paper [AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679) by Chen, Zhongzhi and Liu, Guang and Zhang, Bo-Wen and Ye, Fulong and Yang, Qinghong and Wu, Ledell.
+1. **[Audio Spectrogram Transformer](model_doc/audio-spectrogram-transformer)** (from MIT) released with the paper [AST: Audio Spectrogram Transformer](https://arxiv.org/abs/2104.01778) by Yuan Gong, Yu-An Chung, James Glass.
+1. **[Autoformer](model_doc/autoformer)** (from Tsinghua University) released with the paper [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://arxiv.org/abs/2106.13008) by Haixu Wu, Jiehui Xu, Jianmin Wang, Mingsheng Long.
+1. **[BART](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](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](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](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](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](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](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](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](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. **[BioGpt](model_doc/biogpt)** (from Microsoft Research AI4Science) released with the paper [BioGPT: generative pre-trained transformer for biomedical text generation and mining](https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bbac409/6713511?guestAccessKey=a66d9b5d-4f83-4017-bb52-405815c907b9) by Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon and Tie-Yan Liu.
+1. **[BiT](model_doc/bit)** (from Google AI) released with the paper [Big Transfer (BiT): General Visual Representation Learning](https://arxiv.org/abs/1912.11370) by Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby.
+1. **[Blenderbot](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](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. **[BLIP](model_doc/blip)** (from Salesforce) released with the paper [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://arxiv.org/abs/2201.12086) by Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi.
+1. **[BLIP-2](model_doc/blip-2)** (from Salesforce) released with the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Junnan Li, Dongxu Li, Silvio Savarese, Steven Hoi.
+1. **[BLOOM](model_doc/bloom)** (from BigScience workshop) released by the [BigScience Workshop](https://bigscience.huggingface.co/).
+1. **[BORT](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. **[BridgeTower](model_doc/bridgetower)** (from Harbin Institute of Technology/Microsoft Research Asia/Intel Labs) released with the paper [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://arxiv.org/abs/2206.08657) by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan.
+1. **[ByT5](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](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](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. **[Chinese-CLIP](model_doc/chinese_clip)** (from OFA-Sys) released with the paper [Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://arxiv.org/abs/2211.01335) by An Yang, Junshu Pan, Junyang Lin, Rui Men, Yichang Zhang, Jingren Zhou, Chang Zhou.
+1. **[CLAP](model_doc/clap)** (from LAION-AI) released with the paper [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://arxiv.org/abs/2211.06687) by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov.
+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. **[CLIPSeg](model_doc/clipseg)** (from University of Göttingen) released with the paper [Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003) by Timo Lüddecke and Alexander Ecker.
+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. **[ConvNeXTV2](model_doc/convnextv2)** (from Facebook AI) released with the paper [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](https://arxiv.org/abs/2301.00808) by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, 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.
+1. **[CPM-Ant](model_doc/cpmant)** (from OpenBMB) released by the [OpenBMB](https://www.openbmb.org/).
+1. **[CTRL](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](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](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](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. **[DePlot](model_doc/deplot)** (from Google AI) released with the paper [DePlot: One-shot visual language reasoning by plot-to-table translation](https://arxiv.org/abs/2212.10505) by Fangyu Liu, Julian Martin Eisenschlos, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Wenhu Chen, Nigel Collier, Yasemin Altun.
+1. **[DETA](model_doc/deta)** (from The University of Texas at Austin) released with the paper [NMS Strikes Back](https://arxiv.org/abs/2212.06137) by Jeffrey Ouyang-Zhang, Jang Hyun Cho, Xingyi Zhou, Philipp Krähenbühl.
+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.
+1. **[DiNAT](model_doc/dinat)** (from SHI Labs) released with the paper [Dilated Neighborhood Attention Transformer](https://arxiv.org/abs/2209.15001) by Ali Hassani and Humphrey Shi.
+1. **[DistilBERT](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](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](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](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](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. **[EfficientFormer](model_doc/efficientformer)** (from Snap Research) released with the paper [EfficientFormer: Vision Transformers at MobileNetSpeed](https://arxiv.org/abs/2206.01191) by Yanyu Li, Geng Yuan, Yang Wen, Ju Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren.
+1. **[EfficientNet](model_doc/efficientnet)** (from Google Brain) released with the paper [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946) by Mingxing Tan, Quoc V. Le.
+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. **[ErnieM](model_doc/ernie_m)** (from Baidu) released with the paper [ERNIE-M: Enhanced Multilingual Representation by Aligning Cross-lingual Semantics with Monolingual Corpora](https://arxiv.org/abs/2012.15674) by Xuan Ouyang, Shuohuan Wang, Chao Pang, Yu Sun, Hao Tian, Hua Wu, Haifeng Wang.
+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 and ESMFold** were 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. **[FLAN-T5](model_doc/flan-t5)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-t5-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
+1. **[FLAN-UL2](model_doc/flan-ul2)** (from Google AI) released in the repository [google-research/t5x](https://github.com/google-research/t5x/blob/main/docs/models.md#flan-ul2-checkpoints) by Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei
+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.
+1. **[FocalNet](model_doc/focalnet)** (from Microsoft Research) released with the paper [Focal Modulation Networks](https://arxiv.org/abs/2203.11926) by Jianwei Yang, Chunyuan Li, Xiyang Dai, Lu Yuan, Jianfeng Gao.
+1. **[Funnel Transformer](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. **[GIT](model_doc/git)** (from Microsoft Research) released with the paper [GIT: A Generative Image-to-text Transformer for Vision and Language](https://arxiv.org/abs/2205.14100) by Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang.
+1. **[GLPN](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](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. **[GPT-Sw3](model_doc/gpt-sw3)** (from AI-Sweden) released with the paper [Lessons Learned from GPT-SW3: Building the First Large-Scale Generative Language Model for Swedish](http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.376.pdf) by Ariel Ekgren, Amaru Cuba Gyllensten, Evangelia Gogoulou, Alice Heiman, Severine Verlinden, Joey Öhman, Fredrik Carlsson, Magnus Sahlgren.
+1. **[GPTBigCode](model_doc/gpt_bigcode)** (from BigCode) released with the paper [SantaCoder: don't reach for the stars!](https://arxiv.org/abs/2301.03988) by Loubna Ben Allal, Raymond Li, Denis Kocetkov, Chenghao Mou, Christopher Akiki, Carlos Munoz Ferrandis, Niklas Muennighoff, Mayank Mishra, Alex Gu, Manan Dey, Logesh Kumar Umapathi, Carolyn Jane Anderson, Yangtian Zi, Joel Lamy Poirier, Hailey Schoelkopf, Sergey Troshin, Dmitry Abulkhanov, Manuel Romero, Michael Lappert, Francesco De Toni, Bernardo García del Río, Qian Liu, Shamik Bose, Urvashi Bhattacharyya, Terry Yue Zhuo, Ian Yu, Paulo Villegas, Marco Zocca, Sourab Mangrulkar, David Lansky, Huu Nguyen, Danish Contractor, Luis Villa, Jia Li, Dzmitry Bahdanau, Yacine Jernite, Sean Hughes, Daniel Fried, Arjun Guha, Harm de Vries, Leandro von Werra.
+1. **[GPTSAN-japanese](model_doc/gptsan-japanese)** released in the repository [tanreinama/GPTSAN](https://github.com/tanreinama/GPTSAN/blob/main/report/model.md) by Toshiyuki Sakamoto(tanreinama).
+1. **[Graphormer](model_doc/graphormer)** (from Microsoft) released with the paper [Do Transformers Really Perform Bad for Graph Representation?](https://arxiv.org/abs/2106.05234) by Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, Tie-Yan Liu.
+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.
+1. **[Hubert](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](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](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. **[Informer](model_doc/informer)** (from Beihang University, UC Berkeley, Rutgers University, SEDD Company) released with the paper [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://arxiv.org/abs/2012.07436) by Haoyi Zhou, Shanghang Zhang, Jieqi Peng, Shuai Zhang, Jianxin Li, Hui Xiong, and Wancai Zhang.
+1. **[Jukebox](model_doc/jukebox)** (from OpenAI) released with the paper [Jukebox: A Generative Model for Music](https://arxiv.org/pdf/2005.00341.pdf) by Prafulla Dhariwal, Heewoo Jun, Christine Payne, Jong Wook Kim, Alec Radford, Ilya Sutskever.
+1. **[LayoutLM](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](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](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](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. **[LLaMA](model_doc/llama)** (from The FAIR team of Meta AI) released with the paper [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971) by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample.
+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.
+1. **[LXMERT](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](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. **[Mask2Former](model_doc/mask2former)** (from FAIR and UIUC) released with the paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
+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. **[MatCha](model_doc/matcha)** (from Google AI) released with the paper [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://arxiv.org/abs/2212.09662) by Fangyu Liu, Francesco Piccinno, Syrine Krichene, Chenxi Pang, Kenton Lee, Mandar Joshi, Yasemin Altun, Nigel Collier, Julian Martin Eisenschlos.
+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.
+1. **[MEGA](model_doc/mega)** (from Meta/USC/CMU/SJTU) released with the paper [Mega: Moving Average Equipped Gated Attention](https://arxiv.org/abs/2209.10655) by Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham Neubig, Jonathan May, and Luke Zettlemoyer.
+1. **[Megatron-BERT](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](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. **[MGP-STR](model_doc/mgp-str)** (from Alibaba Research) released with the paper [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao.
+1. **[mLUKE](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](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. **[MobileNetV1](model_doc/mobilenet_v1)** (from Google Inc.) released with the paper [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861) by Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam.
+1. **[MobileNetV2](model_doc/mobilenet_v2)** (from Google Inc.) released with the paper [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381) by Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen.
+1. **[MobileViT](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](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](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](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. **[NAT](model_doc/nat)** (from SHI Labs) released with the paper [Neighborhood Attention Transformer](https://arxiv.org/abs/2204.07143) by Ali Hassani, Steven Walton, Jiachen Li, Shen Li, and Humphrey Shi.
+1. **[Nezha](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](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. **[NLLB-MOE](model_doc/nllb-moe)** (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](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. **[OneFormer](model_doc/oneformer)** (from SHI Labs) released with the paper [OneFormer: One Transformer to Rule Universal Image Segmentation](https://arxiv.org/abs/2211.06220) by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
+1. **[OpenLlama](model_doc/open-llama)** (from [s-JoL](https://huggingface.co/s-JoL)) released in [Open-Llama](https://github.com/s-JoL/Open-Llama). 
+1. **[OPT](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](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](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](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](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](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. **[Pix2Struct](model_doc/pix2struct)** (from Google) released with the paper [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu, Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova.
+1. **[PLBart](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](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](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](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](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](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](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](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](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](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](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. **[RoBERTa-PreLayerNorm](model_doc/roberta-prelayernorm)** (from Facebook) released with the paper [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://arxiv.org/abs/1904.01038) by Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, Michael Auli.
+1. **[RoCBert](model_doc/roc_bert)** (from WeChatAI) released with the paper [RoCBert: Robust Chinese Bert with Multimodal Contrastive Pretraining](https://aclanthology.org/2022.acl-long.65.pdf) by HuiSu, WeiweiShi, XiaoyuShen, XiaoZhou, TuoJi, JiaruiFang, JieZhou.
+1. **[RoFormer](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. **[RWKV](model_doc/rwkv)** (from Bo Peng), released on [this repo](https://github.com/BlinkDL/RWKV-LM) by Bo Peng.
+1. **[SegFormer](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. **[Segment Anything](model_doc/sam)** (from Meta AI) released with the paper [Segment Anything](https://arxiv.org/pdf/2304.02643v1.pdf) by Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alex Berg, Wan-Yen Lo, Piotr Dollar, Ross Girshick.
+1. **[SEW](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](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. **[SpeechT5](model_doc/speecht5)** (from Microsoft Research) released with the paper [SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing](https://arxiv.org/abs/2110.07205) by Junyi Ao, Rui Wang, Long Zhou, Chengyi Wang, Shuo Ren, Yu Wu, Shujie Liu, Tom Ko, Qing Li, Yu Zhang, Zhihua Wei, Yao Qian, Jinyu Li, Furu Wei.
+1. **[SpeechToTextTransformer](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](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](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](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. **[SwiftFormer](model_doc/swiftformer)** (from MBZUAI) released with the paper [SwiftFormer: Efficient Additive Attention for Transformer-based Real-time Mobile Vision Applications](https://arxiv.org/abs/2303.15446) by Abdelrahman Shaker, Muhammad Maaz, Hanoona Rasheed, Salman Khan, Ming-Hsuan Yang, Fahad Shahbaz Khan.
+1. **[Swin Transformer](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](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. **[Swin2SR](model_doc/swin2sr)** (from University of Würzburg) released with the paper [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://arxiv.org/abs/2209.11345) by Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte.
+1. **[SwitchTransformers](model_doc/switch_transformers)** (from Google) released with the paper [Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity](https://arxiv.org/abs/2101.03961) by William Fedus, Barret Zoph, Noam Shazeer.
+1. **[T5](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](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. **[Table Transformer](model_doc/table-transformer)** (from Microsoft Research) released with the paper [PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents](https://arxiv.org/abs/2110.00061) by Brandon Smock, Rohith Pesala, Robin Abraham.
+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. **[TimeSformer](model_doc/timesformer)** (from Facebook) released with the paper [Is Space-Time Attention All You Need for Video Understanding?](https://arxiv.org/abs/2102.05095) by Gedas Bertasius, Heng Wang, Lorenzo Torresani.
+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.
+1. **[TVLT](model_doc/tvlt)** (from UNC Chapel Hill) released with the paper [TVLT: Textless Vision-Language Transformer](https://arxiv.org/abs/2209.14156) by Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal.
+1. **[UL2](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](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](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. **[UPerNet](model_doc/upernet)** (from Peking University) released with the paper [Unified Perceptual Parsing for Scene Understanding](https://arxiv.org/abs/1807.10221) by Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun.
+1. **[VAN](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](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](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)](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. **[ViT Hybrid](model_doc/vit_hybrid)** (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. **[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. **[X-MOD](model_doc/xmod)** (from Meta AI) released with the paper [Lifting the Curse of Multilinguality by Pre-training Modular Transformers](http://dx.doi.org/10.18653/v1/2022.naacl-main.255) by Jonas Pfeiffer, Naman Goyal, Xi Lin, Xian Li, James Cross, Sebastian Riedel, Mikel Artetxe.
+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.
+1. **[XLM-ProphetNet](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](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](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. **[XLM-V](model_doc/xlm-v)** (from Meta AI) released with the paper [XLM-V: Overcoming the Vocabulary Bottleneck in Multilingual Masked Language Models](https://arxiv.org/abs/2301.10472) by Davis Liang, Hila Gonen, Yuning Mao, Rui Hou, Naman Goyal, Marjan Ghazvininejad, Luke Zettlemoyer, Madian Khabsa.
+1. **[XLNet](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](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](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](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](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.
+
+
+### Rangka kerja yang disokong
+
+Jadual di bawah mewakili sokongan semasa dalam perpustakaan untuk setiap model tersebut, sama ada model tersebut mempunyai Python
+tokenizer (dipanggil ""lambat""). Tokenizer ""pantas"" yang disokong oleh perpustakaan Tokenizers 🤗, sama ada mereka mempunyai sokongan dalam Jax (melalui
+Flax), PyTorch, dan/atau TensorFlow.
+
+<!--Jadual ini dikemas kini secara automatik daripada modul auto dengan _make fix-copies_. Jangan kemas kini secara manual!-->
+
+|             Model             | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
+|:-----------------------------:|:--------------:|:--------------:|:---------------:|:------------------:|:------------:|
+|            ALBERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             ALIGN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            AltCLIP            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+| Audio Spectrogram Transformer |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          Autoformer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             BART              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             BEiT              |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|             BERT              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        Bert Generation        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            BigBird            |       ✅       |       ✅       |       ✅        |         ❌         |      ✅      |
+|        BigBird-Pegasus        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            BioGpt             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              BiT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          Blenderbot           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        BlenderbotSmall        |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             BLIP              |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            BLIP-2             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             BLOOM             |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          BridgeTower          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           CamemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            CANINE             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         Chinese-CLIP          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             CLAP              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             CLIP              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|            CLIPSeg            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            CodeGen            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|       Conditional DETR        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           ConvBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|           ConvNeXT            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|          ConvNeXTV2           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            CPM-Ant            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             CTRL              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|              CvT              |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|         Data2VecAudio         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         Data2VecText          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        Data2VecVision         |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            DeBERTa            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          DeBERTa-v2           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|     Decision Transformer      |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        Deformable DETR        |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             DeiT              |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             DETA              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             DETR              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             DiNAT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          DistilBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           DonutSwin           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              DPR              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|              DPT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        EfficientFormer        |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|         EfficientNet          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            ELECTRA            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        Encoder decoder        |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|             ERNIE             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            ErnieM             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              ESM              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|  FairSeq Machine-Translation  |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           FlauBERT            |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             FLAVA             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             FNet              |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|           FocalNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|      Funnel Transformer       |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|              GIT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             GLPN              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            GPT Neo            |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|           GPT NeoX            |       ❌       |       ✅       |       ✅        |         ❌         |      ❌      |
+|       GPT NeoX Japanese       |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             GPT-J             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|            GPT-Sw3            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|          GPTBigCode           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        GPTSAN-japanese        |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          Graphormer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           GroupViT            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            Hubert             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            I-BERT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           ImageGPT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Informer            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            Jukebox            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           LayoutLM            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          LayoutLMv2           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          LayoutLMv3           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|              LED              |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|             LeViT             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             LiLT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             LLaMA             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          Longformer           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            LongT5             |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|             LUKE              |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            LXMERT             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            M-CTC-T            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            M2M100             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            Marian             |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
+|           MarkupLM            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          Mask2Former          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          MaskFormer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        MaskFormerSwin         |       ❌       |       ❌       |       ❌        |         ❌         |      ❌      |
+|             mBART             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             MEGA              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         Megatron-BERT         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            MGP-STR            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          MobileBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|          MobileNetV1          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          MobileNetV2          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           MobileViT           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             MPNet             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|              MT5              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|              MVP              |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|              NAT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             Nezha             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           NLLB-MOE            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         Nyströmformer         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           OneFormer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          OpenAI GPT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|         OpenAI GPT-2          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           OpenLlama           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              OPT              |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|            OWL-ViT            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            Pegasus            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|           PEGASUS-X           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Perceiver           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          Pix2Struct           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            PLBart             |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          PoolFormer           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          ProphetNet           |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            QDQBert            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              RAG              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             REALM             |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|           Reformer            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|            RegNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|            RemBERT            |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|            ResNet             |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|           RetriBERT           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|            RoBERTa            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|     RoBERTa-PreLayerNorm      |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|            RoCBert            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           RoFormer            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|             RWKV              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              SAM              |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|           SegFormer           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|              SEW              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             SEW-D             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|    Speech Encoder decoder     |       ❌       |       ❌       |       ✅        |         ❌         |      ✅      |
+|          Speech2Text          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|         Speech2Text2          |       ✅       |       ❌       |       ❌        |         ❌         |      ❌      |
+|           SpeechT5            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Splinter            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          SqueezeBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
+|          SwiftFormer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|       Swin Transformer        |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|      Swin Transformer V2      |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            Swin2SR            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|      SwitchTransformers       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              T5               |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|       Table Transformer       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             TAPAS             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|    Time Series Transformer    |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          TimeSformer          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|    Trajectory Transformer     |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|        Transformer-XL         |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|             TrOCR             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             TVLT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           UniSpeech           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|         UniSpeechSat          |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            UPerNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              VAN              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           VideoMAE            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             ViLT              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|    Vision Encoder decoder     |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|     VisionTextDualEncoder     |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|          VisualBERT           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|              ViT              |       ❌       |       ❌       |       ✅        |         ✅         |      ✅      |
+|          ViT Hybrid           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            ViTMAE             |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
+|            ViTMSN             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           Wav2Vec2            |       ✅       |       ❌       |       ✅        |         ✅         |      ✅      |
+|      Wav2Vec2-Conformer       |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             WavLM             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|            Whisper            |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|            X-CLIP             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             X-MOD             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             XGLM              |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|              XLM              |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
+|        XLM-ProphetNet         |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          XLM-RoBERTa          |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
+|        XLM-RoBERTa-XL         |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             XLNet             |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
+|             YOLOS             |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|             YOSO              |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+
+<!-- Tamat -->

docs/source/pt/index.mdx

@@ -34,7 +34,7 @@ Cada arquitetura 🤗 Transformers é definida em um módulo individual do Pytho
 ## Se você estiver procurando suporte do time da Hugging Face, acesse
 
 <a target="_blank" href="https://huggingface.co/support">
-    <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);"></img>
+    <img alt="HuggingFace Expert Acceleration Program" src="https://huggingface.co/front/thumbnails/support.png" style="width: 100%; max-width: 600px; border: 1px solid #eee; border-radius: 4px; box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05);">
 </a>
 
 ## Conteúdo

examples/flax/question-answering/run_qa.py

@@ -61,7 +61,7 @@ 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("4.30.0.dev0")
+check_min_version("4.31.0.dev0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

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("4.30.0.dev0")
+check_min_version("4.31.0.dev0")
 
 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("4.30.0.dev0")
+check_min_version("4.31.0.dev0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/flax/vision/requirements.txt

@@ -3,6 +3,6 @@ jaxlib>=0.1.59
 flax>=0.3.5
 optax>=0.0.8
 -f https://download.pytorch.org/whl/torch_stable.html
-torch==1.9.0+cpu 
+torch==1.11.0+cpu
 -f https://download.pytorch.org/whl/torch_stable.html
-torchvision==0.10.0+cpu
+torchvision==0.12.0+cpu

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("4.30.0.dev0")
+check_min_version("4.31.0.dev0")
 
 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("4.30.0.dev0")
+check_min_version("4.31.0.dev0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")