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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: zh |
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datasets: |
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- lmqg/qg_zhquad |
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pipeline_tag: text2text-generation |
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tags: |
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- question generation |
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- answer extraction |
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widget: |
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- text: "generate question: 南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近<hl> 南安普敦中央 <hl>火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。" |
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example_title: "Question Generation Example 1" |
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- text: "generate question: 芝加哥大学的<hl> 1960—61 <hl>集团理论年汇集了Daniel Gorenstein、John G. Thompson和Walter Feit等团体理论家，奠定了一个合作的基础，借助于其他众多数学家的输入，1982中对所有有限的简单群进行了分类。这个项目的规模超过了以往的数学研究，无论是证明的长度还是研究人员的数量。目前正在进行研究，以简化这一分类的证明。如今，群论仍然是一个非常活跃的数学分支，影响着许多其他领域" |
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example_title: "Question Generation Example 2" |
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- text: "extract answers: 南安普敦的警察服务由汉普郡警察提供。 南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。 <hl> 该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。 <hl> 此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。 在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。" |
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example_title: "Answer Extraction Example 1" |
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model-index: |
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- name: lmqg/mt5-base-zhquad-qg-ae |
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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_zhquad |
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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: 14.63 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 34.07 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 23.69 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 76.82 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 57.24 |
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer |
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value: 78.4 |
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer |
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value: 81.92 |
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer |
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value: 75.27 |
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer |
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value: 53.55 |
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer |
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value: 55.82 |
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer |
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value: 51.56 |
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- name: BLEU4 (Answer Extraction) |
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type: bleu4_answer_extraction |
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value: 82.63 |
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- name: ROUGE-L (Answer Extraction) |
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type: rouge_l_answer_extraction |
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value: 95.72 |
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- name: METEOR (Answer Extraction) |
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type: meteor_answer_extraction |
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value: 71.18 |
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- name: BERTScore (Answer Extraction) |
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type: bertscore_answer_extraction |
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value: 99.76 |
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- name: MoverScore (Answer Extraction) |
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type: moverscore_answer_extraction |
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value: 98.8 |
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- name: AnswerF1Score (Answer Extraction) |
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type: answer_f1_score__answer_extraction |
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value: 95.15 |
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- name: AnswerExactMatch (Answer Extraction) |
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type: answer_exact_match_answer_extraction |
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value: 95.07 |
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--- |
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# Model Card of `lmqg/mt5-base-zhquad-qg-ae` |
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This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question generation and answer extraction jointly on the [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (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-base](https://huggingface.co/google/mt5-base) |
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- **Language:** zh |
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- **Training data:** [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (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="zh", model="lmqg/mt5-base-zhquad-qg-ae") |
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# model prediction |
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question_answer_pairs = model.generate_qa("南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近南安普敦中央火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。") |
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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-base-zhquad-qg-ae") |
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# answer extraction |
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answer = pipe("generate question: 南安普敦的警察服务由汉普郡警察提供。南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。该建筑位于南路，2011年启用，靠近<hl> 南安普敦中央 <hl>火车站。此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。") |
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# question generation |
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question = pipe("extract answers: 南安普敦的警察服务由汉普郡警察提供。 南安普敦行动的主要基地是一座新的八层专用建筑，造价3000万英镑。 <hl> 该建筑位于南路，2011年启用，靠近 南安普敦中央 火车站。 <hl> 此前，南安普顿市中心的行动位于市民中心西翼，但由于设施老化，加上计划在旧警察局和地方法院建造一座新博物馆，因此必须搬迁。 在Portswood、Banister Park、Hille和Shirley还有其他警察局，在南安普顿中央火车站还有一个英国交通警察局。") |
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``` |
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## Evaluation |
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_zhquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 76.82 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_1 | 36.9 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_2 | 25.74 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_3 | 19.13 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_4 | 14.63 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| METEOR | 23.69 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| MoverScore | 57.24 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| ROUGE_L | 34.07 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_zhquad.default.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 78.4 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| QAAlignedF1Score (MoverScore) | 53.55 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| QAAlignedPrecision (BERTScore) | 75.27 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| QAAlignedPrecision (MoverScore) | 51.56 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| QAAlignedRecall (BERTScore) | 81.92 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| QAAlignedRecall (MoverScore) | 55.82 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_zhquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------------|--------:|:--------|:-----------------------------------------------------------------| |
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| AnswerExactMatch | 95.07 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| AnswerF1Score | 95.15 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| BERTScore | 99.76 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_1 | 92.37 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_2 | 89.37 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_3 | 86.14 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_4 | 82.63 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| METEOR | 71.18 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| MoverScore | 98.8 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| ROUGE_L | 95.72 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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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_zhquad |
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- dataset_name: default |
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- input_types: ['paragraph_answer', 'paragraph_sentence'] |
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- output_types: ['question', 'answer'] |
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- prefix_types: ['qg', 'ae'] |
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- model: google/mt5-base |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 5 |
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- batch: 32 |
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- lr: 0.0005 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 2 |
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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-base-zhquad-qg-ae/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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