commit files to HF hub

README.md

@@ -0,0 +1,211 @@
+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: zh
+datasets:
+- lmqg/qg_zhquad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+- answer extraction
+widget:
+- 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。"
+  example_title: "Question Generation Example 1" 
+- 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"
+  example_title: "Question Generation Example 2" 
+- 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。"
+  example_title: "Answer Extraction Example 1" 
+model-index:
+- name: lmqg/mt5-base-zhquad-qg-ae
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_zhquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question Generation)
+      type: bleu4_question_generation
+      value: 14.63
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 34.07
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 23.69
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 76.82
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 57.24
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer
+      value: 78.4
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer
+      value: 81.92
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer
+      value: 75.27
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer
+      value: 53.55
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer
+      value: 55.82
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer))
+      type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer
+      value: 51.56
+    - name: BLEU4 (Answer Extraction)
+      type: bleu4_answer_extraction
+      value: 82.63
+    - name: ROUGE-L (Answer Extraction)
+      type: rouge_l_answer_extraction
+      value: 95.72
+    - name: METEOR (Answer Extraction)
+      type: meteor_answer_extraction
+      value: 71.18
+    - name: BERTScore (Answer Extraction)
+      type: bertscore_answer_extraction
+      value: 99.76
+    - name: MoverScore (Answer Extraction)
+      type: moverscore_answer_extraction
+      value: 98.8
+    - name: AnswerF1Score (Answer Extraction)
+      type: answer_f1_score__answer_extraction
+      value: 95.15
+    - name: AnswerExactMatch (Answer Extraction)
+      type: answer_exact_match_answer_extraction
+      value: 95.07
+---
+
+# Model Card of `lmqg/mt5-base-zhquad-qg-ae`
+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).
+
+
+### 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-qg-ae")
+
+# model prediction
+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。")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "lmqg/mt5-base-zhquad-qg-ae")
+
+# answer extraction
+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。")
+
+# question generation
+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。")
+
+```
+
+## Evaluation
+
+
+- ***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) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   76.82 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_1     |   36.9  | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_2     |   25.74 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_3     |   19.13 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_4     |   14.63 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| METEOR     |   23.69 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| MoverScore |   57.24 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| ROUGE_L    |   34.07 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+
+
+- ***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)
+
+|                                 |   Score | Type    | Dataset                                                          |
+|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   78.4  | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| QAAlignedF1Score (MoverScore)   |   53.55 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| QAAlignedPrecision (BERTScore)  |   75.27 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| QAAlignedPrecision (MoverScore) |   51.56 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| QAAlignedRecall (BERTScore)     |   81.92 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| QAAlignedRecall (MoverScore)    |   55.82 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+
+
+- ***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)
+
+|                  |   Score | Type    | Dataset                                                          |
+|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
+| AnswerExactMatch |   95.07 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| AnswerF1Score    |   95.15 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| BERTScore        |   99.76 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_1           |   92.37 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_2           |   89.37 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_3           |   86.14 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| Bleu_4           |   82.63 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| METEOR           |   71.18 | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| MoverScore       |   98.8  | default | [lmqg/qg_zhquad](https://huggingface.co/datasets/lmqg/qg_zhquad) |
+| ROUGE_L          |   95.72 | 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_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
+
+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).
+
+## 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.questions_answers.lmqg_qg_zhquad.default.json

@@ -0,0 +1 @@
+{"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

@@ -0,0 +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

@@ -0,0 +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

@@ -0,0 +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}}

trainer_config.json

@@ -0,0 +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}