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