--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: it datasets: - lmqg/qg_itquad pipeline_tag: text2text-generation tags: - question generation widget: - text: " Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento." example_title: "Question Generation Example 1" - text: "L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere da cinque a dieci anni prima di una produzione significativa." example_title: "Question Generation Example 2" - text: "il Giappone è stato il paese più dipendente dal petrolio arabo." example_title: "Question Generation Example 3" model-index: - name: lmqg/mt5-small-itquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_itquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 7.37 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 21.93 - name: METEOR (Question Generation) type: meteor_question_generation value: 17.57 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 80.8 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 56.79 - name: BLEU4 (Question & Answer Generation (with Gold Answer)) type: bleu4_question_answer_generation_with_gold_answer value: 15.44 - name: ROUGE-L (Question & Answer Generation (with Gold Answer)) type: rouge_l_question_answer_generation_with_gold_answer value: 40.08 - name: METEOR (Question & Answer Generation (with Gold Answer)) type: meteor_question_answer_generation_with_gold_answer value: 34.31 - name: BERTScore (Question & Answer Generation (with Gold Answer)) type: bertscore_question_answer_generation_with_gold_answer value: 86.62 - name: MoverScore (Question & Answer Generation (with Gold Answer)) type: moverscore_question_answer_generation_with_gold_answer value: 60.68 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 87.66 - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 87.57 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer value: 87.76 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 61.6 - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 61.48 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer value: 61.73 - name: BLEU4 (Question & Answer Generation) type: bleu4_question_answer_generation value: 3.48 - name: ROUGE-L (Question & Answer Generation) type: rouge_l_question_answer_generation value: 23.31 - name: METEOR (Question & Answer Generation) type: meteor_question_answer_generation value: 23.95 - name: BERTScore (Question & Answer Generation) type: bertscore_question_answer_generation value: 77.23 - name: MoverScore (Question & Answer Generation) type: moverscore_question_answer_generation value: 54.66 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 81.63 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 82.28 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 81.04 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 55.85 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 56.14 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 55.6 --- # Model Card of `lmqg/mt5-small-itquad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). ### Overview - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) - **Language:** it - **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (default) - **Online Demo:** [https://autoqg.net/](https://autoqg.net/) - **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) - **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) ### Usage - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) ```python from lmqg import TransformersQG # initialize model model = TransformersQG(language="it", model="lmqg/mt5-small-itquad-qg") # model prediction questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-itquad-qg") output = pipe(" Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 80.8 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_1 | 22.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_2 | 14.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_3 | 10.34 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_4 | 7.37 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | METEOR | 17.57 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | MoverScore | 56.79 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ROUGE_L | 21.93 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 86.62 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_1 | 40.5 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_2 | 28.64 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_3 | 20.78 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_4 | 15.44 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | METEOR | 34.31 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | MoverScore | 60.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (BERTScore) | 87.66 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (MoverScore) | 61.6 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (BERTScore) | 87.76 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (MoverScore) | 61.73 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (BERTScore) | 87.57 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (MoverScore) | 61.48 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ROUGE_L | 40.08 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-itquad-ae`](https://huggingface.co/lmqg/mt5-small-itquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.lmqg_mt5-small-itquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 77.23 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_1 | 23.77 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_2 | 13.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_3 | 6.48 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | Bleu_4 | 3.48 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | METEOR | 23.95 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | MoverScore | 54.66 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (BERTScore) | 81.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedF1Score (MoverScore) | 55.85 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (BERTScore) | 81.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedPrecision (MoverScore) | 55.6 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (BERTScore) | 82.28 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | QAAlignedRecall (MoverScore) | 56.14 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | | ROUGE_L | 23.31 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_itquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 15 - batch: 16 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.0 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-itquad-qg/raw/main/trainer_config.json). ## Citation ``` @inproceedings{ushio-etal-2022-generative, title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", author = "Ushio, Asahi and Alva-Manchego, Fernando and Camacho-Collados, Jose", booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2022", address = "Abu Dhabi, U.A.E.", publisher = "Association for Computational Linguistics", } ```