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language: it |
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# UmBERTo Wikipedia Uncased + italian SQuAD v1 📚 🧐 ❓ |
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[UmBERTo-Wikipedia-Uncased](https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1) fine-tuned on [Italian SQUAD v1 dataset](https://github.com/crux82/squad-it) for **Q&A** downstream task. |
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## Details of the downstream task (Q&A) - Model 🧠 |
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[UmBERTo](https://github.com/musixmatchresearch/umberto) is a Roberta-based Language Model trained on large Italian Corpora and uses two innovative approaches: SentencePiece and Whole Word Masking. |
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UmBERTo-Wikipedia-Uncased Training is trained on a relative small corpus (~7GB) extracted from Wikipedia-ITA. |
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## Details of the downstream task (Q&A) - Dataset 📚 |
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[SQuAD](https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/) [Rajpurkar et al. 2016] is a large scale dataset for training of question answering systems on factoid questions. It contains more than 100,000 question-answer pairs about passages from 536 articles chosen from various domains of Wikipedia. |
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**SQuAD-it** is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. |
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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 bert \ |
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--model_name_or_path 'Musixmatch/umberto-wikipedia-uncased-v1' \ |
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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/SQuAD_it-train.json' \ |
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--predict_file '/content/dataset/SQuAD_it-test.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/drive/My\ Drive/umberto-uncased-finetuned-squadv1-it \ |
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--overwrite_output_dir \ |
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--save_steps 1000 |
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``` |
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With 10 epochs the model overfits the train dataset so I evaluated the different checkpoints created during training (every 1000 steps) and chose the best (In this case the one created at 17000 steps). |
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## Test set Results 🧾 |
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| Metric | # Value | |
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| **EM** | **60.50** | |
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| **F1** | **72.41** | |
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```json |
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{ |
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'exact': 60.50729399395453, |
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'f1': 72.4141113348361, |
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'total': 7609, |
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'HasAns_exact': 60.50729399395453, |
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'HasAns_f1': 72.4141113348361, |
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'HasAns_total': 7609, |
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'best_exact': 60.50729399395453, |
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'best_exact_thresh': 0.0, |
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'best_f1': 72.4141113348361, |
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'best_f1_thresh': 0.0 |
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} |
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``` |
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## Comparison ⚖️ |
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| Model | EM | F1 score | |
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| -------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- | |
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| [DrQA-it trained on SQuAD-it ](https://github.com/crux82/squad-it/blob/master/README.md#evaluating-a-neural-model-over-squad-it) | 56.1 | 65.9 | |
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| This one |60.50 |72.41 | |
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| [bert-italian-finedtuned-squadv1-it-alfa](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa) |**62.51** |**74.16** | | **62.51** | **74.16** | |
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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/umberto-wikipedia-uncased-v1-finetuned-squadv1-it') |
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QnA_pipeline({ |
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'context': 'Marco Aurelio era un imperatore romano che praticava lo stoicismo come filosofia di vita .', |
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'question': 'Quale filosofia seguì Marco Aurelio ?' |
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}) |
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# Output: |
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{'answer': 'stoicismo', 'end': 65, 'score': 0.9477770241566028, 'start': 56} |
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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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