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150 lines
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150 lines
4.8 KiB
Plaintext
<!--Copyright 2020 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# DistilBERT
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## Overview
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The DistilBERT model was proposed in the blog post [Smaller, faster, cheaper, lighter: Introducing DistilBERT, a
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distilled version of BERT](https://medium.com/huggingface/distilbert-8cf3380435b5), and the paper [DistilBERT, a
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distilled version of BERT: smaller, faster, cheaper and lighter](https://arxiv.org/abs/1910.01108). DistilBERT is a
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small, fast, cheap and light Transformer model trained by distilling BERT base. It has 40% less parameters than
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*bert-base-uncased*, runs 60% faster while preserving over 95% of BERT's performances as measured on the GLUE language
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understanding benchmark.
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The abstract from the paper is the following:
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*As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP),
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operating these large models in on-the-edge and/or under constrained computational training or inference budgets
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remains challenging. In this work, we propose a method to pre-train a smaller general-purpose language representation
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model, called DistilBERT, which can then be fine-tuned with good performances on a wide range of tasks like its larger
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counterparts. While most prior work investigated the use of distillation for building task-specific models, we leverage
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knowledge distillation during the pretraining phase and show that it is possible to reduce the size of a BERT model by
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40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage the inductive
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biases learned by larger models during pretraining, we introduce a triple loss combining language modeling,
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distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train and we
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demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative on-device
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study.*
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Tips:
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- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
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separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
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- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
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necessary though, just let us know if you need this option.
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This model was contributed by [victorsanh](https://huggingface.co/victorsanh). This model jax version was
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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).
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## DistilBertConfig
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[[autodoc]] DistilBertConfig
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## DistilBertTokenizer
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[[autodoc]] DistilBertTokenizer
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## DistilBertTokenizerFast
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[[autodoc]] DistilBertTokenizerFast
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## DistilBertModel
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[[autodoc]] DistilBertModel
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- forward
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## DistilBertForMaskedLM
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[[autodoc]] DistilBertForMaskedLM
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- forward
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## DistilBertForSequenceClassification
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[[autodoc]] DistilBertForSequenceClassification
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- forward
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## DistilBertForMultipleChoice
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[[autodoc]] DistilBertForMultipleChoice
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- forward
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## DistilBertForTokenClassification
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[[autodoc]] DistilBertForTokenClassification
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- forward
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## DistilBertForQuestionAnswering
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[[autodoc]] DistilBertForQuestionAnswering
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- forward
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## TFDistilBertModel
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[[autodoc]] TFDistilBertModel
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- call
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## TFDistilBertForMaskedLM
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[[autodoc]] TFDistilBertForMaskedLM
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- call
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## TFDistilBertForSequenceClassification
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[[autodoc]] TFDistilBertForSequenceClassification
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- call
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## TFDistilBertForMultipleChoice
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[[autodoc]] TFDistilBertForMultipleChoice
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- call
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## TFDistilBertForTokenClassification
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[[autodoc]] TFDistilBertForTokenClassification
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- call
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## TFDistilBertForQuestionAnswering
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[[autodoc]] TFDistilBertForQuestionAnswering
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- call
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## FlaxDistilBertModel
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[[autodoc]] FlaxDistilBertModel
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- __call__
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## FlaxDistilBertForMaskedLM
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[[autodoc]] FlaxDistilBertForMaskedLM
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- __call__
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## FlaxDistilBertForSequenceClassification
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[[autodoc]] FlaxDistilBertForSequenceClassification
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- __call__
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## FlaxDistilBertForMultipleChoice
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[[autodoc]] FlaxDistilBertForMultipleChoice
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- __call__
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## FlaxDistilBertForTokenClassification
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[[autodoc]] FlaxDistilBertForTokenClassification
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- __call__
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## FlaxDistilBertForQuestionAnswering
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[[autodoc]] FlaxDistilBertForQuestionAnswering
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- __call__
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