From bf497376ee0ff2ab242962a7942518f39871d554 Mon Sep 17 00:00:00 2001 From: Manuel Romero Date: Fri, 10 Jul 2020 17:42:49 +0200 Subject: [PATCH] Create README.md (#5572) --- .../README.md | 96 +++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 model_cards/mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it/README.md diff --git a/model_cards/mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it/README.md b/model_cards/mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it/README.md new file mode 100644 index 0000000000..6ba7ced127 --- /dev/null +++ b/model_cards/mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it/README.md @@ -0,0 +1,96 @@ +--- +language: italian +--- + +# UmBERTo Wikipedia Uncased + italian SQuAD v1 ๐Ÿ“š ๐Ÿง โ“ + +[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. + +## Details of the downstream task (Q&A) - Model ๐Ÿง  + +[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. +UmBERTo-Wikipedia-Uncased Training is trained on a relative small corpus (~7GB) extracted from Wikipedia-ITA. + + +## Details of the downstream task (Q&A) - Dataset ๐Ÿ“š + +[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. + +**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. + +## Model training ๐Ÿ‹๏ธโ€ + +The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command: + +```bash +python transformers/examples/question-answering/run_squad.py \ + --model_type bert \ + --model_name_or_path 'Musixmatch/umberto-wikipedia-uncased-v1' \ + --do_eval \ + --do_train \ + --do_lower_case \ + --train_file '/content/dataset/SQuAD_it-train.json' \ + --predict_file '/content/dataset/SQuAD_it-test.json' \ + --per_gpu_train_batch_size 16 \ + --learning_rate 3e-5 \ + --num_train_epochs 10 \ + --max_seq_length 384 \ + --doc_stride 128 \ + --output_dir /content/drive/My\ Drive/umberto-uncased-finetuned-squadv1-it \ + --overwrite_output_dir \ + --save_steps 1000 +``` +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). + +## Test set Results ๐Ÿงพ + +| Metric | # Value | +| ------ | --------- | +| **EM** | **60.50** | +| **F1** | **72.41** | + + + +```json +{ +'exact': 60.50729399395453, +'f1': 72.4141113348361, +'total': 7609, +'HasAns_exact': 60.50729399395453, +'HasAns_f1': 72.4141113348361, +'HasAns_total': 7609, +'best_exact': 60.50729399395453, +'best_exact_thresh': 0.0, +'best_f1': 72.4141113348361, +'best_f1_thresh': 0.0 +} +``` + +## Comparison โš–๏ธ + +| Model | EM | F1 score | +| -------------------------------------------------------------------------------------------------------------------------------- | --------- | --------- | +| [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 | +| This one |60.50 |72.41 | +| [bert-italian-finedtuned-squadv1-it-alfa](https://huggingface.co/mrm8488/bert-italian-finedtuned-squadv1-it-alfa) |**62.51** |**74.16** | | **62.51** | **74.16** | + + +### Model in action ๐Ÿš€ + +Fast usage with **pipelines**: + +```python +from transformers import pipeline + +QnA_pipeline = pipeline('question-answering', model='mrm8488/umberto-wikipedia-uncased-v1-finetuned-squadv1-it') + +QnA_pipeline({ + 'context': 'Marco Aurelio era un imperatore romano che praticava lo stoicismo come filosofia di vita .', + 'question': 'Quale filosofia seguรฌ Marco Aurelio ?' +}) +# Output: +{'answer': 'stoicismo', 'end': 65, 'score': 0.9477770241566028, 'start': 56} +``` + +> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/) +> Made with ♥ in Spain