commit files to HF hub
Browse files- README.md +211 -0
- eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_zhquad.default.json +1 -0
- eval/metric.first.answer.paragraph_answer.question.lmqg_qg_zhquad.default.json +1 -0
- eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_zhquad.default.json +1 -0
- eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_zhquad.default.json +1 -0
- eval/samples.test.hyp.paragraph.questions_answers.lmqg_qg_zhquad.default.txt +0 -0
- eval/samples.test.hyp.paragraph_answer.question.lmqg_qg_zhquad.default.txt +0 -0
- eval/samples.test.hyp.paragraph_sentence.answer.lmqg_qg_zhquad.default.txt +0 -0
- eval/samples.validation.hyp.paragraph.questions_answers.lmqg_qg_zhquad.default.txt +0 -0
- eval/samples.validation.hyp.paragraph_answer.question.lmqg_qg_zhquad.default.txt +0 -0
- eval/samples.validation.hyp.paragraph_sentence.answer.lmqg_qg_zhquad.default.txt +0 -0
- trainer_config.json +1 -0
README.md
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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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188 |
+
- batch: 32
|
189 |
+
- lr: 0.0005
|
190 |
+
- fp16: False
|
191 |
+
- random_seed: 1
|
192 |
+
- gradient_accumulation_steps: 2
|
193 |
+
- label_smoothing: 0.15
|
194 |
+
|
195 |
+
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).
|
196 |
+
|
197 |
+
## Citation
|
198 |
+
```
|
199 |
+
@inproceedings{ushio-etal-2022-generative,
|
200 |
+
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
|
201 |
+
author = "Ushio, Asahi and
|
202 |
+
Alva-Manchego, Fernando and
|
203 |
+
Camacho-Collados, Jose",
|
204 |
+
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
|
205 |
+
month = dec,
|
206 |
+
year = "2022",
|
207 |
+
address = "Abu Dhabi, U.A.E.",
|
208 |
+
publisher = "Association for Computational Linguistics",
|
209 |
+
}
|
210 |
+
|
211 |
+
```
|
eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_zhquad.default.json
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|
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|
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+
{"test": {"QAAlignedF1Score (BERTScore)": 0.7840325667005541, "QAAlignedRecall (BERTScore)": 0.8192277935277489, "QAAlignedPrecision (BERTScore)": 0.7526823553477937, "QAAlignedF1Score (MoverScore)": 0.5354834983683481, "QAAlignedRecall (MoverScore)": 0.5582311913531273, "QAAlignedPrecision (MoverScore)": 0.5156456203732518, "Bleu_1": 0.004120169646593162, "Bleu_2": 0.0002690462586043532, "Bleu_3": 2.309690680114931e-09, "Bleu_4": 6.809078457750052e-12, "METEOR": 0.18410054149133784, "ROUGE_L": 0.008395744475536369, "BERTScore": 0.6401465189512394, "MoverScore": 0.5144297590022558}, "validation": {"QAAlignedF1Score (BERTScore)": 0.7775678025934519, "QAAlignedRecall (BERTScore)": 0.792034959739694, "QAAlignedPrecision (BERTScore)": 0.7645720974173859, "QAAlignedF1Score (MoverScore)": 0.5296673149872821, "QAAlignedRecall (MoverScore)": 0.5377457296740437, "QAAlignedPrecision (MoverScore)": 0.522520577815569, "Bleu_1": 0.020248651555948103, "Bleu_2": 0.002488586967260189, "Bleu_3": 1.8529426509033387e-08, "Bleu_4": 5.1003887614414895e-11, "METEOR": 0.22259731778597108, "ROUGE_L": 0.03550224398447071, "BERTScore": 0.7134572867191199, "MoverScore": 0.5324894696915295}}
|
eval/metric.first.answer.paragraph_answer.question.lmqg_qg_zhquad.default.json
ADDED
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|
|
|
|
|
1 |
+
{"validation": {"Bleu_1": 0.331403644188708, "Bleu_2": 0.21741208697691133, "Bleu_3": 0.15291393571778292, "Bleu_4": 0.1114773553115748}, "test": {"Bleu_1": 0.36647700419945134, "Bleu_2": 0.25574123119104364, "Bleu_3": 0.19014527826918273, "Bleu_4": 0.14539788962863745}}
|
eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_zhquad.default.json
ADDED
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|
|
|
|
|
1 |
+
{"validation": {"Bleu_1": 0.9095607235141746, "Bleu_2": 0.8764123107984839, "Bleu_3": 0.8421956410982502, "Bleu_4": 0.8064084363691053, "METEOR": 0.7048075020925044, "ROUGE_L": 0.95025934294886, "BERTScore": 0.9962562629975643, "MoverScore": 0.983760362101422, "AnswerF1Score": 94.32633155253362, "AnswerExactMatch": 94.17192812044682}, "test": {"Bleu_1": 0.9237335154282393, "Bleu_2": 0.8936666994570295, "Bleu_3": 0.8613613683073535, "Bleu_4": 0.8262964699391061, "METEOR": 0.7118335238554365, "ROUGE_L": 0.9572456613078124, "BERTScore": 0.9976168804489449, "MoverScore": 0.9879965808467877, "AnswerF1Score": 95.15483706838734, "AnswerExactMatch": 95.07042253521126}}
|
eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_zhquad.default.json
ADDED
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|
|
|
|
|
1 |
+
{"validation": {"Bleu_1": 0.3488776348206905, "Bleu_2": 0.23076767559810957, "Bleu_3": 0.16334827219050144, "Bleu_4": 0.11977698407723236, "METEOR": 0.22255230035396023, "ROUGE_L": 0.3153618768839511, "BERTScore": 0.7507793895323698, "MoverScore": 0.5626607296579037}, "test": {"Bleu_1": 0.3689802851596385, "Bleu_2": 0.2574117790800594, "Bleu_3": 0.19134174600366827, "Bleu_4": 0.14633645379328508, "METEOR": 0.23692156158635738, "ROUGE_L": 0.34068667498869315, "BERTScore": 0.7682263112027325, "MoverScore": 0.5724303111254394}}
|
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eval/samples.test.hyp.paragraph_sentence.answer.lmqg_qg_zhquad.default.txt
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eval/samples.validation.hyp.paragraph.questions_answers.lmqg_qg_zhquad.default.txt
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eval/samples.validation.hyp.paragraph_answer.question.lmqg_qg_zhquad.default.txt
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eval/samples.validation.hyp.paragraph_sentence.answer.lmqg_qg_zhquad.default.txt
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trainer_config.json
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|
|
|
|
|
1 |
+
{"dataset_path": "lmqg/qg_zhquad", "dataset_name": "default", "input_types": ["paragraph_answer", "paragraph_sentence"], "output_types": ["question", "answer"], "prefix_types": ["qg", "ae"], "model": "google/mt5-base", "max_length": 512, "max_length_output": 32, "epoch": 5, "batch": 32, "lr": 0.0005, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 2, "label_smoothing": 0.15}
|