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README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: zh
+datasets:
+- lmqg/qg_zhquad
+pipeline_tag: text2text-generation
+tags:
+- answer extraction
+widget:
+- 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。"
+  example_title: "Answering Extraction Example 1" 
+model-index:
+- name: lmqg/mt5-base-zhquad-ae
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_zhquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 79.86
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 94.53
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 68.41
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 99.48
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 97.97
+    - name: AnswerF1Score (Answer Extraction)
+      type: answer_f1_score__answer_extraction
+      value: 92.68
+    - name: AnswerExactMatch (Answer Extraction)
+      type: answer_exact_match_answer_extraction
+      value: 92.62
+---
+
+# Model Card of `lmqg/mt5-base-zhquad-ae`
+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).
+
+
+### Overview
+- **Language model:** [google/mt5-base](https://huggingface.co/google/mt5-base)   
+- **Language:** zh  
+- **Training data:** [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) (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="zh", model="lmqg/mt5-base-zhquad-ae")
+
+# model prediction
+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。")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-zhquad-ae")
+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。")
+
+```
+
+## Evaluation
+
+
+- ***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) 
+
+|                  |   Score | Type    | Dataset                                                          |
+|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
+| AnswerExactMatch |   92.62 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| AnswerF1Score    |   92.68 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| BERTScore        |   99.48 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_1           |   90.95 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_2           |   87.44 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_3           |   83.75 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_4           |   79.86 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| METEOR           |   68.41 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| MoverScore       |   97.97 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| ROUGE_L          |   94.53 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_zhquad
+ - dataset_name: default
+ - input_types: ['paragraph_sentence']
+ - output_types: ['answer']
+ - prefix_types: None
+ - model: google/mt5-base
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 18
+ - batch: 8
+ - lr: 0.0001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 8
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-zhquad-ae/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",
+}
+
+```

eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_zhquad.default.json

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+{"validation": {"Bleu_1": 0.8821431499548708, "Bleu_2": 0.842715525856889, "Bleu_3": 0.8031818661660554, "Bleu_4": 0.7626687095604595, "METEOR": 0.663477475311334, "ROUGE_L": 0.926675722073625, "BERTScore": 0.9885125362710967, "MoverScore": 0.9655442642159453, "AnswerF1Score": 89.34422627720345, "AnswerExactMatch": 89.23020883924235}, "test": {"Bleu_1": 0.9094807255263107, "Bleu_2": 0.8743577806268511, "Bleu_3": 0.8374906862795883, "Bleu_4": 0.798584672727377, "METEOR": 0.6840866066914842, "ROUGE_L": 0.9453220529418422, "BERTScore": 0.9947916523505033, "MoverScore": 0.9796722865505375, "AnswerF1Score": 92.67735724692986, "AnswerExactMatch": 92.61777561923263}}

trainer_config.json

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+{"dataset_path": "lmqg/qg_zhquad", "dataset_name": "default", "input_types": ["paragraph_sentence"], "output_types": ["answer"], "prefix_types": null, "model": "google/mt5-base", "max_length": 512, "max_length_output": 32, "epoch": 18, "batch": 8, "lr": 0.0001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 8, "label_smoothing": 0.15}