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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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- answer extraction |
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widget: |
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- text: "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: "Answering Extraction Example 1" |
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model-index: |
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- name: lmqg/mt5-base-zhquad-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 (Answer Extraction) |
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type: bleu4_answer_extraction |
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value: 79.86 |
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- name: ROUGE-L (Answer Extraction) |
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type: rouge_l_answer_extraction |
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value: 94.53 |
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- name: METEOR (Answer Extraction) |
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type: meteor_answer_extraction |
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value: 68.41 |
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- name: BERTScore (Answer Extraction) |
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type: bertscore_answer_extraction |
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value: 99.48 |
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- name: MoverScore (Answer Extraction) |
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type: moverscore_answer_extraction |
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value: 97.97 |
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- name: AnswerF1Score (Answer Extraction) |
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type: answer_f1_score__answer_extraction |
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value: 92.68 |
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- name: AnswerExactMatch (Answer Extraction) |
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type: answer_exact_match_answer_extraction |
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value: 92.62 |
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--- |
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# Model Card of `lmqg/mt5-base-zhquad-ae` |
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This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for answer extraction 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-ae") |
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# model prediction |
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answers = model.generate_a("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-ae") |
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output = pipe("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 (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-zhquad-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 | 92.62 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| AnswerF1Score | 92.68 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| BERTScore | 99.48 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_1 | 90.95 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_2 | 87.44 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_3 | 83.75 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| Bleu_4 | 79.86 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| METEOR | 68.41 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| MoverScore | 97.97 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) | |
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| ROUGE_L | 94.53 | 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_sentence'] |
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- output_types: ['answer'] |
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- prefix_types: None |
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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: 18 |
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- batch: 8 |
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- lr: 0.0001 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 8 |
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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-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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