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100 lines
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SqueezeBERT
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The SqueezeBERT model was proposed in `SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
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<https://arxiv.org/abs/2006.11316>`__ by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, Kurt W. Keutzer. It's a
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bidirectional transformer similar to the BERT model. The key difference between the BERT architecture and the
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SqueezeBERT architecture is that SqueezeBERT uses `grouped convolutions <https://blog.yani.io/filter-group-tutorial>`__
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instead of fully-connected layers for the Q, K, V and FFN layers.
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The abstract from the paper is the following:
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*Humans read and write hundreds of billions of messages every day. Further, due to the availability of large datasets,
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large computing systems, and better neural network models, natural language processing (NLP) technology has made
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significant strides in understanding, proofreading, and organizing these messages. Thus, there is a significant
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opportunity to deploy NLP in myriad applications to help web users, social networks, and businesses. In particular, we
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consider smartphones and other mobile devices as crucial platforms for deploying NLP models at scale. However, today's
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highly-accurate NLP neural network models such as BERT and RoBERTa are extremely computationally expensive, with
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BERT-base taking 1.7 seconds to classify a text snippet on a Pixel 3 smartphone. In this work, we observe that methods
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such as grouped convolutions have yielded significant speedups for computer vision networks, but many of these
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techniques have not been adopted by NLP neural network designers. We demonstrate how to replace several operations in
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self-attention layers with grouped convolutions, and we use this technique in a novel network architecture called
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SqueezeBERT, which runs 4.3x faster than BERT-base on the Pixel 3 while achieving competitive accuracy on the GLUE test
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set. The SqueezeBERT code will be released.*
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Tips:
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- SqueezeBERT is a model with absolute position embeddings so it's usually advised to pad the inputs on the right
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rather than the left.
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- SqueezeBERT is similar to BERT and therefore relies on the masked language modeling (MLM) objective. It is therefore
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efficient at predicting masked tokens and at NLU in general, but is not optimal for text generation. Models trained
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with a causal language modeling (CLM) objective are better in that regard.
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- For best results when finetuning on sequence classification tasks, it is recommended to start with the
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`squeezebert/squeezebert-mnli-headless` checkpoint.
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SqueezeBertConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertConfig
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:members:
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SqueezeBertTokenizer
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertTokenizer
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:members: build_inputs_with_special_tokens, get_special_tokens_mask,
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create_token_type_ids_from_sequences, save_vocabulary
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SqueezeBertTokenizerFast
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertTokenizerFast
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:members:
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SqueezeBertModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertModel
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:members:
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SqueezeBertForMaskedLM
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertForMaskedLM
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:members:
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SqueezeBertForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertForSequenceClassification
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:members:
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SqueezeBertForMultipleChoice
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertForMultipleChoice
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:members:
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SqueezeBertForTokenClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertForTokenClassification
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:members:
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SqueezeBertForQuestionAnswering
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.SqueezeBertForQuestionAnswering
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:members:
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