commit files to HF hub

README.md

@@ -0,0 +1,135 @@
+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: zh
+datasets:
+- lmqg/qag_zhquad
+pipeline_tag: text2text-generation
+tags:
+- questions and answers generation
+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。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。"
+  example_title: "Questions & Answers Generation Example 1" 
+model-index:
+- name: lmqg/mt5-small-zhquad-qag
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qag_zhquad
+      type: default
+      args: default
+    metrics:
+    - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_bertscore_question_answer_generation
+      value: 75.47
+    - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
+      type: qa_aligned_recall_bertscore_question_answer_generation
+      value: 75.41
+    - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
+      type: qa_aligned_precision_bertscore_question_answer_generation
+      value: 75.56
+    - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
+      type: qa_aligned_f1_score_moverscore_question_answer_generation
+      value: 52.42
+    - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
+      type: qa_aligned_recall_moverscore_question_answer_generation
+      value: 52.33
+    - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
+      type: qa_aligned_precision_moverscore_question_answer_generation
+      value: 52.53
+---
+
+# Model Card of `lmqg/mt5-small-zhquad-qag`
+This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question & answer pair generation task on the [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)   
+- **Language:** zh  
+- **Training data:** [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_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-small-zhquad-qag")
+
+# 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-small-zhquad-qag")
+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。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。")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-zhquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_zhquad.default.json) 
+
+|                                 |   Score | Type    | Dataset                                                            |
+|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
+| QAAlignedF1Score (BERTScore)    |   75.47 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
+| QAAlignedF1Score (MoverScore)   |   52.42 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
+| QAAlignedPrecision (BERTScore)  |   75.56 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
+| QAAlignedPrecision (MoverScore) |   52.53 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
+| QAAlignedRecall (BERTScore)     |   75.41 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
+| QAAlignedRecall (MoverScore)    |   52.33 | default | [lmqg/qag_zhquad](https://huggingface.co/datasets/lmqg/qag_zhquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qag_zhquad
+ - dataset_name: default
+ - input_types: ['paragraph']
+ - output_types: ['questions_answers']
+ - prefix_types: None
+ - model: google/mt5-small
+ - max_length: 512
+ - max_length_output: 256
+ - epoch: 12
+ - batch: 8
+ - lr: 0.001
+ - 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-small-zhquad-qag/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_qag_zhquad.default.json

@@ -0,0 +1 @@
+{"validation": {"Bleu_1": 0.14134835343020602, "Bleu_2": 0.10534816521967788, "Bleu_3": 0.0766084173202246, "Bleu_4": 0.04687224075080508, "METEOR": 0.14697248412164318, "ROUGE_L": 0.21885139820728308, "BERTScore": 0.6995925002748316, "MoverScore": 0.5008344918609549, "QAAlignedF1Score (BERTScore)": 0.7592320980145917, "QAAlignedRecall (BERTScore)": 0.7419003721531561, "QAAlignedPrecision (BERTScore)": 0.7784040149783445, "QAAlignedF1Score (MoverScore)": 0.5269689064434208, "QAAlignedRecall (MoverScore)": 0.5157330420279342, "QAAlignedPrecision (MoverScore)": 0.5393656213706578}, "test": {"Bleu_1": 0.05186334464606799, "Bleu_2": 0.03793786971902631, "Bleu_3": 0.026232797866259774, "Bleu_4": 0.01568589004862248, "METEOR": 0.09359306444063609, "ROUGE_L": 0.10737244587096752, "BERTScore": 0.6015494508939344, "MoverScore": 0.4916226730806146, "QAAlignedF1Score (BERTScore)": 0.7547118486424483, "QAAlignedRecall (BERTScore)": 0.7540613106188125, "QAAlignedPrecision (BERTScore)": 0.7556347203918363, "QAAlignedF1Score (MoverScore)": 0.5242262076682067, "QAAlignedRecall (MoverScore)": 0.5232569024808614, "QAAlignedPrecision (MoverScore)": 0.5253399484071091}}

trainer_config.json

@@ -0,0 +1 @@
+{"dataset_path": "lmqg/qag_zhquad", "dataset_name": "default", "input_types": ["paragraph"], "output_types": ["questions_answers"], "prefix_types": null, "model": "google/mt5-small", "max_length": 512, "max_length_output": 256, "epoch": 12, "batch": 8, "lr": 0.001, "fp16": false, "random_seed": 1, "gradient_accumulation_steps": 8, "label_smoothing": 0.15}