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*This model was released on 2020-10-24 and added to Hugging Face Transformers on 2021-07-24.*

# RemBERT

[RemBERT](https://huggingface.co/papers/2010.12821) re-evaluates the practice of sharing weights between input and output embeddings in pre-trained language models. It demonstrates that decoupling these embeddings enhances modeling flexibility and optimizes parameter allocation in multilingual models. By reallocating input embedding parameters within Transformer layers, the model achieves superior performance on natural language understanding tasks without increasing parameters during fine-tuning. Additionally, allocating extra capacity to the output embedding, which is discarded post-pre-training, improves the model's generalization and transferability to various tasks and languages. These insights enable strong performance on the XTREME benchmark without expanding the parameter count at the fine-tuning stage.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="fill-mask", model="google/rembert", dtype="auto")
pipeline("Plants create <mask> through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForMaskedLM, AutoTokenizer

model = AutoModelForMaskedLM.from_pretrained("google/rembert", dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("google/rembert")

inputs = tokenizer("Plants create <mask> through a process known as photosynthesis.", return_tensors="pt")
outputs = model(**inputs)
mask_token_id = tokenizer.mask_token_id
mask_position = (inputs.input_ids == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
predicted_word = tokenizer.decode(outputs.logits[0, mask_position].argmax(dim=-1))
print(f"Predicted word: {predicted_word}")
```

</hfoption>
</hfoptions>

## Usage tips

- For fine-tuning, RemBERT is like a bigger version of mBERT with ALBERT-like embedding factorization.
- Embeddings aren't tied during pre-training (unlike BERT). This enables smaller input embeddings (preserved during fine-tuning) and bigger output embeddings (discarded at fine-tuning).
- The tokenizer is similar to ALBERT's tokenizer rather than BERT's.

## RemBertConfig

[[autodoc]] RemBertConfig

## RemBertTokenizer

[[autodoc]] RemBertTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## RemBertTokenizerFast

[[autodoc]] RemBertTokenizerFast
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## RemBertModel

[[autodoc]] RemBertModel
    - forward

## RemBertForCausalLM

[[autodoc]] RemBertForCausalLM
    - forward

## RemBertForMaskedLM

[[autodoc]] RemBertForMaskedLM
    - forward

## RemBertForSequenceClassification

[[autodoc]] RemBertForSequenceClassification
    - forward

## RemBertForMultipleChoice

[[autodoc]] RemBertForMultipleChoice
    - forward

## RemBertForTokenClassification

[[autodoc]] RemBertForTokenClassification
    - forward

## RemBertForQuestionAnswering

[[autodoc]] RemBertForQuestionAnswering
    - forward

