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language: en |
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# Electra small ⚡ + SQuAD v1 ❓ |
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[Electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) fine-tuned on [SQUAD v1.1 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) for **Q&A** downstream task. |
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## Details of the downstream task (Q&A) - Model 🧠 |
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**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset. |
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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 transformers/examples/question-answering/run_squad.py \ |
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--model_type electra \ |
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--model_name_or_path 'google/electra-small-discriminator' \ |
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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 10 \ |
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--max_seq_length 384 \ |
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--doc_stride 128 \ |
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--output_dir '/content/output' \ |
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--overwrite_output_dir \ |
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--save_steps 1000 |
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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** | **77.70** | |
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| **F1** | **85.74** | |
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| **Size**| **50 MB** | |
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Very good metrics for such a "small" model! |
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```json |
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{ |
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'exact': 77.70104068117313, |
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'f1': 85.73991234187997, |
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'total': 10570, |
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'HasAns_exact': 77.70104068117313, |
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'HasAns_f1': 85.73991234187997, |
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'HasAns_total': 10570, |
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'best_exact': 77.70104068117313, |
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'best_exact_thresh': 0.0, |
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'best_f1': 85.73991234187997, |
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'best_f1_thresh': 0.0 |
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} |
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``` |
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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/electra-small-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': 'What has been discovered by scientists from China ?' |
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}) |
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# Output: |
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{'answer': 'A new strain of flu', 'end': 19, 'score': 0.7950334108113424, 'start': 0} |
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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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