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--- |
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license: cc-by-4.0 |
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metrics: |
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- bleu4 |
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- meteor |
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- rouge-l |
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- bertscore |
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- moverscore |
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language: ko |
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datasets: |
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- lmqg/qg_koquad |
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pipeline_tag: text2text-generation |
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tags: |
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- question generation |
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widget: |
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- text: "1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다." |
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example_title: "Question Generation Example 1" |
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- text: "백신이 없기때문에 예방책은 <hl> 살충제 <hl> 를 사용하면서 서식 장소(찻찬 받침, 배수로, 고인 물의 열린 저장소, 버려진 타이어 등)의 수를 줄임으로써 매개체를 통제할 수 있다." |
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example_title: "Question Generation Example 2" |
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- text: "<hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다." |
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example_title: "Question Generation Example 3" |
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model-index: |
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- name: lmqg/mt5-small-koquad-qg |
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results: |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_koquad |
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type: default |
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args: default |
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metrics: |
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- name: BLEU4 (Question Generation) |
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type: bleu4_question_generation |
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value: 10.57 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 25.64 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 27.52 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 82.89 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 82.49 |
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- name: QAAlignedF1Score-BERTScore (Gold Answer) |
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type: qa_aligned_f1_score_bertscore_gold_answer |
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value: 87.52 |
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- name: QAAlignedRecall-BERTScore (Gold Answer) |
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type: qa_aligned_recall_bertscore_gold_answer |
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value: 87.49 |
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- name: QAAlignedPrecision-BERTScore (Gold Answer) |
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type: qa_aligned_precision_bertscore_gold_answer |
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value: 87.57 |
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- name: QAAlignedF1Score-MoverScore (Gold Answer) |
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type: qa_aligned_f1_score_moverscore_gold_answer |
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value: 85.15 |
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- name: QAAlignedRecall-MoverScore (Gold Answer) |
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type: qa_aligned_recall_moverscore_gold_answer |
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value: 85.09 |
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- name: QAAlignedPrecision-MoverScore (Gold Answer) |
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type: qa_aligned_precision_moverscore_gold_answer |
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value: 85.23 |
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--- |
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# Model Card of `lmqg/mt5-small-koquad-qg` |
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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). |
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### Overview |
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- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) |
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- **Language:** ko |
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- **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (default) |
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/) |
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) |
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- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) |
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### Usage |
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- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) |
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```python |
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from lmqg import TransformersQG |
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# initialize model |
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model = TransformersQG(language="ko", model="lmqg/mt5-small-koquad-qg") |
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# model prediction |
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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") |
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``` |
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- With `transformers` |
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```python |
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from transformers import pipeline |
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pipe = pipeline("text2text-generation", "lmqg/mt5-small-koquad-qg") |
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output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.") |
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``` |
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## Evaluation |
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- ***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) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 82.89 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| Bleu_1 | 25.31 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| Bleu_2 | 18.59 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| Bleu_3 | 13.98 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| Bleu_4 | 10.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| METEOR | 27.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| MoverScore | 82.49 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| ROUGE_L | 25.64 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an 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) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 87.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| QAAlignedF1Score (MoverScore) | 85.15 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| QAAlignedPrecision (BERTScore) | 87.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| QAAlignedPrecision (MoverScore) | 85.23 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| QAAlignedRecall (BERTScore) | 87.49 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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| QAAlignedRecall (MoverScore) | 85.09 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) | |
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## Training hyperparameters |
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The following hyperparameters were used during fine-tuning: |
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- dataset_path: lmqg/qg_koquad |
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- dataset_name: default |
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- input_types: ['paragraph_answer'] |
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- output_types: ['question'] |
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- prefix_types: None |
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- model: google/mt5-small |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 7 |
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- batch: 16 |
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- lr: 0.001 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 4 |
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- label_smoothing: 0.15 |
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-koquad-qg/raw/main/trainer_config.json). |
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## Citation |
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``` |
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@inproceedings{ushio-etal-2022-generative, |
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", |
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author = "Ushio, Asahi and |
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Alva-Manchego, Fernando and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
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``` |
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