100 lines
4.4 KiB
Markdown
100 lines
4.4 KiB
Markdown
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# SqueezeBERT
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## Overview
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The SqueezeBERT model was proposed in [SqueezeBERT: What can computer vision teach NLP about efficient neural networks?](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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This model was contributed by [forresti](https://huggingface.co/forresti).
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## Usage 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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## Resources
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- [Text classification task guide](../tasks/sequence_classification)
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- [Token classification task guide](../tasks/token_classification)
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- [Question answering task guide](../tasks/question_answering)
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- [Masked language modeling task guide](../tasks/masked_language_modeling)
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- [Multiple choice task guide](../tasks/multiple_choice)
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## SqueezeBertConfig
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[[autodoc]] SqueezeBertConfig
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## SqueezeBertTokenizer
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[[autodoc]] SqueezeBertTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## SqueezeBertTokenizerFast
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[[autodoc]] SqueezeBertTokenizerFast
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## SqueezeBertModel
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[[autodoc]] SqueezeBertModel
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## SqueezeBertForMaskedLM
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[[autodoc]] SqueezeBertForMaskedLM
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## SqueezeBertForSequenceClassification
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[[autodoc]] SqueezeBertForSequenceClassification
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## SqueezeBertForMultipleChoice
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[[autodoc]] SqueezeBertForMultipleChoice
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## SqueezeBertForTokenClassification
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[[autodoc]] SqueezeBertForTokenClassification
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## SqueezeBertForQuestionAnswering
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[[autodoc]] SqueezeBertForQuestionAnswering
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