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README.md ADDED
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+
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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: ko
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+ datasets:
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+ - lmqg/qg_koquad
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+ pipeline_tag: text2text-generation
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+ tags:
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+ - question generation
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+ widget:
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+ - 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."
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+ example_title: "Question Generation Example 1"
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+ - 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."
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+ example_title: "Question Generation Example 2"
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+ - text: "<hl> 원테이크 촬영 <hl> 이기 때문에 한 사람이 실수를 하면 처음부터 다시 찍어야 하는 상황이 발생한다."
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+ example_title: "Question Generation Example 3"
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+ model-index:
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+ - name: vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qg
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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_koquad
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+ type: default
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+ args: default
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+ metrics:
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+ - name: BLEU4 (Question Generation)
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+ type: bleu4_question_generation
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+ value: 0.0
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+ - name: ROUGE-L (Question Generation)
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+ type: rouge_l_question_generation
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+ value: 0.05
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+ - name: METEOR (Question Generation)
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+ type: meteor_question_generation
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+ value: 1.52
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+ - name: BERTScore (Question Generation)
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+ type: bertscore_question_generation
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+ value: 53.33
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+ - name: MoverScore (Question Generation)
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+ type: moverscore_question_generation
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+ value: 48.19
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+ ---
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+
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+ # Model Card of `vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qg`
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+ This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-ko-15000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-15000) 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).
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+
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+
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+ ### Overview
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+ - **Language model:** [vocabtrimmer/mt5-small-trimmed-ko-15000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-15000)
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+ - **Language:** ko
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+ - **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (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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+
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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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+
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+ # initialize model
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+ model = TransformersQG(language="ko", model="vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qg")
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+
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+ # model prediction
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+ 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")
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+
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+ ```
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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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+
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+ pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qg")
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+ 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.")
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+
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+ ```
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+
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+ ## Evaluation
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+
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+
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+ - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json)
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+
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+ | | Score | Type | Dataset |
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+ |:-----------|--------:|:--------|:-----------------------------------------------------------------|
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+ | BERTScore | 53.33 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | Bleu_1 | 0.01 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | Bleu_2 | 0 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | Bleu_3 | 0 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | Bleu_4 | 0 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | METEOR | 1.52 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | MoverScore | 48.19 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+ | ROUGE_L | 0.05 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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+
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+
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+
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+ ## Training hyperparameters
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+
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+ The following hyperparameters were used during fine-tuning:
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+ - dataset_path: lmqg/qg_koquad
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+ - dataset_name: default
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+ - input_types: paragraph_answer
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+ - output_types: question
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+ - prefix_types: None
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+ - model: vocabtrimmer/mt5-small-trimmed-ko-15000
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+ - max_length: 512
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+ - max_length_output: 32
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+ - epoch: 5
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+ - batch: 16
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+ - lr: 0.0005
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+ - fp16: False
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+ - random_seed: 1
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+ - gradient_accumulation_steps: 4
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+ - label_smoothing: 0.15
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+
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+ The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qg/raw/main/trainer_config.json).
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+
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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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+ ```
eval/metric.first.answer.paragraph_answer.question.lmqg_qg_koquad.default.json ADDED
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+ {"validation": {"Bleu_1": 0.00025834548172205476, "Bleu_2": 5.0175636956104234e-05, "Bleu_3": 2.962297835379666e-10, "Bleu_4": 7.309011298875784e-13}, "test": {"Bleu_1": 7.411112963889285e-05, "Bleu_2": 8.516453261694912e-13, "Bleu_3": 1.95941221532042e-15, "Bleu_4": 9.544535591096194e-17}}
eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_koquad.default.json ADDED
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+ {"validation": {"Bleu_1": 0.00032984753713838534, "Bleu_2": 6.523498336271802e-05, "Bleu_3": 3.87458489407366e-10, "Bleu_4": 9.588408525859448e-13, "METEOR": 0.01634679868665742, "ROUGE_L": 0.0008804828189812416, "BERTScore": 0.5326547119599363, "MoverScore": 0.4813786173822095}, "test": {"Bleu_1": 7.596908058420152e-05, "Bleu_2": 8.729478120255356e-13, "Bleu_3": 2.0083041936305836e-15, "Bleu_4": 9.78206351113566e-17, "METEOR": 0.01520266564946064, "ROUGE_L": 0.0005290375067912147, "BERTScore": 0.5332766564813083, "MoverScore": 0.4819022589874832}}
eval/samples.test.hyp.paragraph_answer.question.lmqg_qg_koquad.default.txt ADDED
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eval/samples.validation.hyp.paragraph_answer.question.lmqg_qg_koquad.default.txt ADDED
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