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* First pass on utility classes and python tokenizers * finishing cleanup pass * style and quality * Fix tests * Updating following @mfuntowicz comment * style and quality * Fix Roberta * fix batch_size/seq_length inBatchEncoding * add alignement methods + tests * Fix OpenAI and Transfo-XL tokenizers * adding trim_offsets=True default for GPT2 et RoBERTa * style and quality * fix tests * add_prefix_space in roberta * bump up tokenizers to rc7 * style * unfortunately tensorfow does like these - removing shape/seq_len for now * Update src/transformers/tokenization_utils.py Co-Authored-By: Stefan Schweter <stefan@schweter.it> * Adding doc and docstrings * making flake8 happy Co-authored-by: Stefan Schweter <stefan@schweter.it>
107 lines
3.8 KiB
ReStructuredText
107 lines
3.8 KiB
ReStructuredText
DistilBERT
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----------------------------------------------------
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The DistilBERT model was proposed in the blog post
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`Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT <https://medium.com/huggingface/distilbert-8cf3380435b5>`__,
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and the paper `DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter <https://arxiv.org/abs/1910.01108>`__.
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DistilBERT is a small, fast, cheap and light Transformer model trained by distilling Bert base. It has 40% less
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parameters than `bert-base-uncased`, runs 60% faster while preserving over 95% of Bert's performances as measured on
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the GLUE language 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
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leverage knowledge distillation during the pre-training phase and show that it is possible to reduce the size of a
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BERT model by 40%, while retaining 97% of its language understanding capabilities and being 60% faster. To leverage
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the inductive biases learned by larger models during pre-training, we introduce a triple loss combining language
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modeling, distillation and cosine-distance losses. Our smaller, faster and lighter model is cheaper to pre-train
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and we demonstrate its capabilities for on-device computations in a proof-of-concept experiment and a comparative
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on-device 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 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 necessary though, just let's us know if you need this option.
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The original code can be found `here <https://github.com/huggingface/transformers/tree/master/examples/distillation>`_.
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DistilBertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertConfig
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:members:
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DistilBertTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertTokenizer
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:members:
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DistilBertTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertTokenizerFast
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:members:
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DistilBertModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertModel
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:members:
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DistilBertForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertForMaskedLM
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:members:
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DistilBertForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertForSequenceClassification
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:members:
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DistilBertForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.DistilBertForQuestionAnswering
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:members:
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TFDistilBertModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDistilBertModel
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:members:
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TFDistilBertForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDistilBertForMaskedLM
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:members:
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TFDistilBertForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDistilBertForSequenceClassification
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:members:
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TFDistilBertForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.TFDistilBertForQuestionAnswering
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:members:
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