--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: ko datasets: - lmqg/qg_koquad pipeline_tag: text2text-generation tags: - question generation widget: - text: "1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다." example_title: "Question Generation Example 1" - text: "백신이 없기때문에 예방책은 살충제 를 사용하면서 서식 장소(찻찬 받침, 배수로, 고인 물의 열린 저장소, 버려진 타이어 등)의 수를 줄임으로써 매개체를 통제할 수 있다." example_title: "Question Generation Example 2" - text: " 원테이크 촬영 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다." example_title: "Question Generation Example 3" model-index: - name: lmqg/mt5-small-koquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_koquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 10.57 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 25.64 - name: METEOR (Question Generation) type: meteor_question_generation value: 27.52 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 82.89 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 82.49 - name: BLEU4 (Question & Answer Generation (with Gold Answer)) type: bleu4_question_answer_generation_with_gold_answer value: 10.86 - name: ROUGE-L (Question & Answer Generation (with Gold Answer)) type: rouge_l_question_answer_generation_with_gold_answer value: 46.66 - name: METEOR (Question & Answer Generation (with Gold Answer)) type: meteor_question_answer_generation_with_gold_answer value: 36.66 - name: BERTScore (Question & Answer Generation (with Gold Answer)) type: bertscore_question_answer_generation_with_gold_answer value: 87.21 - name: MoverScore (Question & Answer Generation (with Gold Answer)) type: moverscore_question_answer_generation_with_gold_answer value: 84.46 - 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: 87.52 - 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: 87.49 - 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: 87.57 - 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: 85.15 - 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: 85.09 - 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: 85.23 --- # Model Card of `lmqg/mt5-small-koquad-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_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) (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:** ko - **Training data:** [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) (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="ko", model="lmqg/mt5-small-koquad-qg") # model prediction questions = model.generate_q(list_context="1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.", list_answer="남부군") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qg") output = pipe("1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.") ``` ## Evaluation - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co./lmqg/mt5-small-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 82.89 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_1 | 25.31 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_2 | 18.59 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_3 | 13.98 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_4 | 10.57 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | METEOR | 27.52 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | MoverScore | 82.49 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | ROUGE_L | 25.64 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | - ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co./lmqg/mt5-small-koquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_koquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 87.21 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_1 | 41.86 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_2 | 30.67 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_3 | 18.85 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | Bleu_4 | 10.86 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | METEOR | 36.66 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | MoverScore | 84.46 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | QAAlignedF1Score (BERTScore) | 87.52 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | QAAlignedF1Score (MoverScore) | 85.15 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | QAAlignedPrecision (BERTScore) | 87.57 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | QAAlignedPrecision (MoverScore) | 85.23 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | QAAlignedRecall (BERTScore) | 87.49 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | QAAlignedRecall (MoverScore) | 85.09 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | | ROUGE_L | 46.66 | default | [lmqg/qg_koquad](https://huggingface.co./datasets/lmqg/qg_koquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_koquad - 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: 7 - batch: 16 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 4 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co./lmqg/mt5-small-koquad-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", } ```