--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: es datasets: - lmqg/qg_esquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "del Ministerio de Desarrollo Urbano , Gobierno de la India." example_title: "Question Generation Example 1" - text: "a noviembre , que es también la estación lluviosa." example_title: "Question Generation Example 2" - text: "como el gobierno de Abbott que asumió el cargo el 18 de septiembre de 2013." example_title: "Question Generation Example 3" model-index: - name: lmqg/mt5-small-esquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_esquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 9.61 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 24.62 - name: METEOR (Question Generation) type: meteor_question_generation value: 22.71 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 84.07 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 59.06 - name: BLEU4 (Question & Answer Generation (with Gold Answer)) type: bleu4_question_answer_generation_with_gold_answer value: 12.77 - name: ROUGE-L (Question & Answer Generation (with Gold Answer)) type: rouge_l_question_answer_generation_with_gold_answer value: 42.77 - name: METEOR (Question & Answer Generation (with Gold Answer)) type: meteor_question_answer_generation_with_gold_answer value: 37.58 - name: BERTScore (Question & Answer Generation (with Gold Answer)) type: bertscore_question_answer_generation_with_gold_answer value: 89.41 - name: MoverScore (Question & Answer Generation (with Gold Answer)) type: moverscore_question_answer_generation_with_gold_answer value: 63.56 - 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: 89.43 - 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: 89.41 - 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: 89.44 - 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: 63.73 - 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: 63.72 - 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: 63.75 - name: BLEU4 (Question & Answer Generation) type: bleu4_question_answer_generation value: 1.83 - name: ROUGE-L (Question & Answer Generation) type: rouge_l_question_answer_generation value: 15.46 - name: METEOR (Question & Answer Generation) type: meteor_question_answer_generation value: 22.22 - name: BERTScore (Question & Answer Generation) type: bertscore_question_answer_generation value: 69.78 - name: MoverScore (Question & Answer Generation) type: moverscore_question_answer_generation value: 51.8 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 79.89 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 82.56 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 77.46 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 54.82 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 56.52 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 53.31 --- # Model Card of `lmqg/mt5-small-esquad-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_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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:** es - **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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="es", model="lmqg/mt5-small-esquad-qg") # model prediction questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad-qg") output = pipe("del Ministerio de Desarrollo Urbano , Gobierno de la India.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 84.07 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 26.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 17.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 12.88 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 9.61 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 22.71 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 59.06 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 24.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 89.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 39.84 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 26.59 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 18.28 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 12.77 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 37.58 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 63.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (BERTScore) | 89.43 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (MoverScore) | 63.73 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (BERTScore) | 89.44 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (MoverScore) | 63.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (BERTScore) | 89.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (MoverScore) | 63.72 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 42.77 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | - ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-esquad-ae`](https://huggingface.co/lmqg/mt5-small-esquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.lmqg_mt5-small-esquad-ae.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 69.78 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_1 | 11.1 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_2 | 5.5 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_3 | 3 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | Bleu_4 | 1.83 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | METEOR | 22.22 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | MoverScore | 51.8 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (BERTScore) | 79.89 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedF1Score (MoverScore) | 54.82 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (BERTScore) | 77.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedPrecision (MoverScore) | 53.31 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (BERTScore) | 82.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | QAAlignedRecall (MoverScore) | 56.52 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | | ROUGE_L | 15.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_esquad - 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: 16 - batch: 64 - lr: 0.0005 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-esquad-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", } ```