PyTorch DistilBERT
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DistilBERT
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DistilBERT is a small, fast, cheap and light Transformer model
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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
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Bert's performances as measured on the GLUE language understanding benchmark.
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Here are the differences between the interface of Bert and DistilBert:
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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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For more information on DistilBERT, please refer to our
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`detailed blog post`_
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.. _`detailed blog post`:
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https://medium.com/huggingface/distilbert-8cf3380435b5
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``DistilBertConfig``
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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