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--- |
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language: en |
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datasets: |
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- squad |
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--- |
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# SqueezeBERT + SQuAD (v1.1) |
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[squeezebert-uncased](https://huggingface.co/squeezebert/squeezebert-uncased) fine-tuned on [SQUAD v1.1](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task. |
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## Details of SqueezeBERT |
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This model, `squeezebert-uncased`, is a pretrained model for the English language using a masked language modeling (MLM) and Sentence Order Prediction (SOP) objective. |
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SqueezeBERT was introduced in [this paper](https://arxiv.org/abs/2006.11316). This model is case-insensitive. The model architecture is similar to BERT-base, but with the pointwise fully-connected layers replaced with [grouped convolutions](https://blog.yani.io/filter-group-tutorial/). |
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The authors found that SqueezeBERT is 4.3x faster than `bert-base-uncased` on a Google Pixel 3 smartphone. |
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More about the model [here](https://arxiv.org/abs/2004.02984) |
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## Details of the downstream task (Q&A) - Dataset 📚 🧐 ❓ |
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**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. |
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SQuAD v1.1 contains **100,000+** question-answer pairs on **500+** articles. |
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## Model training 🏋️ |
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The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: |
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```bash |
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python /content/transformers/examples/question-answering/run_squad.py \ |
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--model_type bert \ |
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--model_name_or_path squeezebert/squeezebert-uncased \ |
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--do_eval \ |
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--do_train \ |
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--do_lower_case \ |
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--train_file /content/dataset/train-v1.1.json \ |
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--predict_file /content/dataset/dev-v1.1.json \ |
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--per_gpu_train_batch_size 16 \ |
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--learning_rate 3e-5 \ |
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--num_train_epochs 15 \ |
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--max_seq_length 384 \ |
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--doc_stride 128 \ |
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--output_dir /content/output_dir \ |
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--overwrite_output_dir \ |
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--save_steps 2000 |
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``` |
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## Test set Results 🧾 |
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| Metric | # Value | |
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| ------ | --------- | |
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| **EM** | **76.66** | |
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| **F1** | **85.83** | |
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Model Size: **195 MB** |
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### Model in action 🚀 |
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Fast usage with **pipelines**: |
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```python |
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from transformers import pipeline |
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QnA_pipeline = pipeline('question-answering', model='mrm8488/squeezebert-finetuned-squadv1') |
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QnA_pipeline({ |
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'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.', |
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'question': 'Who did identified it ?' |
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}) |
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# Output: {'answer': 'scientists.', 'end': 106, 'score': 0.6988425850868225, 'start': 96} |
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``` |
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> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) |
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> Made with <span style="color: #e25555;">♥</span> in Spain |
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