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# DistilBERT

## Overview

The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, a
distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a
small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
*bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
understanding benchmark.

The abstract from the paper is the following:

*As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
operating these large models in on-the-edge and/or under constrained computational training or inference budgets
remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
study.*

Tips:

- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
  separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
  necessary though, just let us know if you need this option.

This model was contributed by [victorsanh](https://huggingface.co/victorsanh). This model jax version was
contributed by [kamalkraj](https://huggingface.co/kamalkraj). The original code can be found [here](https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation).


## DistilBertConfig

[[autodoc]] DistilBertConfig

## DistilBertTokenizer

[[autodoc]] DistilBertTokenizer

## DistilBertTokenizerFast

[[autodoc]] DistilBertTokenizerFast

## DistilBertModel

[[autodoc]] DistilBertModel
    - forward

## DistilBertForMaskedLM

[[autodoc]] DistilBertForMaskedLM
    - forward

## DistilBertForSequenceClassification

[[autodoc]] DistilBertForSequenceClassification
    - forward

## DistilBertForMultipleChoice

[[autodoc]] DistilBertForMultipleChoice
    - forward

## DistilBertForTokenClassification

[[autodoc]] DistilBertForTokenClassification
    - forward

## DistilBertForQuestionAnswering

[[autodoc]] DistilBertForQuestionAnswering
    - forward

## TFDistilBertModel

[[autodoc]] TFDistilBertModel
    - call

## TFDistilBertForMaskedLM

[[autodoc]] TFDistilBertForMaskedLM
    - call

## TFDistilBertForSequenceClassification

[[autodoc]] TFDistilBertForSequenceClassification
    - call

## TFDistilBertForMultipleChoice

[[autodoc]] TFDistilBertForMultipleChoice
    - call

## TFDistilBertForTokenClassification

[[autodoc]] TFDistilBertForTokenClassification
    - call

## TFDistilBertForQuestionAnswering

[[autodoc]] TFDistilBertForQuestionAnswering
    - call

## FlaxDistilBertModel

[[autodoc]] FlaxDistilBertModel
    - __call__

## FlaxDistilBertForMaskedLM

[[autodoc]] FlaxDistilBertForMaskedLM
    - __call__

## FlaxDistilBertForSequenceClassification

[[autodoc]] FlaxDistilBertForSequenceClassification
    - __call__

## FlaxDistilBertForMultipleChoice

[[autodoc]] FlaxDistilBertForMultipleChoice
    - __call__

## FlaxDistilBertForTokenClassification

[[autodoc]] FlaxDistilBertForTokenClassification
    - __call__

## FlaxDistilBertForQuestionAnswering

[[autodoc]] FlaxDistilBertForQuestionAnswering
    - __call__
