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README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: ko
+datasets:
+- lmqg/qg_koquad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+widget:
+- text: "1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다."
+  example_title: "Question Generation Example 1" 
+- text: "백신이 없기때문에 예방책은 <hl> 살충제 <hl> 를 사용하면서 서식 장소(찻찬 받침, 배수로, 고인 물의 열린 저장소, 버려진 타이어 등)의 수를 줄임으로써 매개체를 통제할 수 있다."
+  example_title: "Question Generation Example 2" 
+- text: "<hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다."
+  example_title: "Question Generation Example 3" 
+model-index:
+- name: vocabtrimmer/mt5-small-trimmed-ko-5000-koquad-qg
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_koquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question Generation)
+      type: bleu4_question_generation
+      value: 10.86
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 26.55
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 28.37
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 83.39
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 82.62
+---
+
+# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-5000-koquad-qg`
+This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-5000) for question generation task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
+
+
+### Overview
+- **Language model:** [vocabtrimmer/mt5-small-trimmed-ko-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-5000)   
+- **Language:** ko  
+- **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (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="ko", model="vocabtrimmer/mt5-small-trimmed-ko-5000-koquad-qg")
+
+# model prediction
+questions = model.generate_q(list_context="1990년 영화 《 남부군 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.", list_answer="남부군")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ko-5000-koquad-qg")
+output = pipe("1990년 영화 《 <hl> 남부군 <hl> 》에서 단역으로 영화배우 첫 데뷔에 이어 같은 해 KBS 드라마 《지구인》에서 단역으로 출연하였고 이듬해 MBC 《여명의 눈동자》를 통해 단역으로 출연하였다.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-5000-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   83.39 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_1     |   26.35 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_2     |   19.29 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_3     |   14.43 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| Bleu_4     |   10.86 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| METEOR     |   28.37 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| MoverScore |   82.62 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+| ROUGE_L    |   26.55 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_koquad
+ - dataset_name: default
+ - input_types: paragraph_answer
+ - output_types: question
+ - prefix_types: None
+ - model: vocabtrimmer/mt5-small-trimmed-ko-5000
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 10
+ - batch: 16
+ - lr: 0.001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 4
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-5000-koquad-qg/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_answer.question.lmqg_qg_koquad.default.json

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+{"validation": {"Bleu_1": 0.2496108044722054, "Bleu_2": 0.1812224395694888, "Bleu_3": 0.13473807391604525, "Bleu_4": 0.10150792917722413}, "test": {"Bleu_1": 0.26062386980107966, "Bleu_2": 0.190559322451657, "Bleu_3": 0.14246924655889417, "Bleu_4": 0.10722926989771604}}

eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json

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+{"validation": {"Bleu_1": 0.28087665374848914, "Bleu_2": 0.20774398313455106, "Bleu_3": 0.15646206662215253, "Bleu_4": 0.11878709149545796, "METEOR": 0.2907674930795983, "ROUGE_L": 0.2752458685282714, "BERTScore": 0.827671270658977, "MoverScore": 0.8287219544605307}, "test": {"Bleu_1": 0.26350652912262384, "Bleu_2": 0.19293059005178845, "Bleu_3": 0.14434690847145198, "Bleu_4": 0.10864995187150303, "METEOR": 0.2837066425732749, "ROUGE_L": 0.2654599546413742, "BERTScore": 0.8338704939877218, "MoverScore": 0.8261534980247792}}