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lmqg
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mt5-base-ruquad-qag

Text2Text Generation
Transformers
PyTorch
Russian
mt5
questions and answers generation
Eval Results
Inference Endpoints
Model card Files Community
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asahi417 commited on
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model update

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  1. README.md +158 -0
  2. config.json +1 -1
  3. eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_ruquad.default.json +1 -0
  4. eval/samples.test.hyp.paragraph.questions_answers.lmqg_qag_ruquad.default.txt +0 -0
  5. eval/samples.validation.hyp.paragraph.questions_answers.lmqg_qag_ruquad.default.txt +0 -0
  6. pytorch_model.bin +2 -2
  7. tokenizer_config.json +1 -1
  8. trainer_config.json +1 -0
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: ru
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+ datasets:
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+ - lmqg/qag_ruquad
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+ pipeline_tag: text2text-generation
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+ tags:
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+ - questions and answers generation
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+ widget:
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+ - text: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов."
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+ example_title: "Questions & Answers Generation Example 1"
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+ model-index:
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+ - name: lmqg/mt5-base-ruquad-qag
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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/qag_ruquad
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+ type: default
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+ args: default
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+ metrics:
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+ - name: BLEU4 (Question & Answer Generation)
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+ type: bleu4_question_answer_generation
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+ value: 2.12
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+ - name: ROUGE-L (Question & Answer Generation)
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+ type: rouge_l_question_answer_generation
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+ value: 13.12
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+ - name: METEOR (Question & Answer Generation)
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+ type: meteor_question_answer_generation
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+ value: 16.85
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+ - name: BERTScore (Question & Answer Generation)
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+ type: bertscore_question_answer_generation
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+ value: 62.3
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+ - name: MoverScore (Question & Answer Generation)
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+ type: moverscore_question_answer_generation
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+ value: 50.58
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+ - name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
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+ type: qa_aligned_f1_score_bertscore_question_answer_generation
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+ value: 74.63
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+ - name: QAAlignedRecall-BERTScore (Question & Answer Generation)
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+ type: qa_aligned_recall_bertscore_question_answer_generation
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+ value: 75.38
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+ - name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
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+ type: qa_aligned_precision_bertscore_question_answer_generation
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+ value: 73.97
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+ - name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
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+ type: qa_aligned_f1_score_moverscore_question_answer_generation
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+ value: 54.24
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+ - name: QAAlignedRecall-MoverScore (Question & Answer Generation)
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+ type: qa_aligned_recall_moverscore_question_answer_generation
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+ value: 54.65
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+ - name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
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+ type: qa_aligned_precision_moverscore_question_answer_generation
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+ value: 53.91
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+ ---
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+
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+ # Model Card of `lmqg/mt5-base-ruquad-qag`
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+ This model is fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) for question & answer pair generation task on the [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (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:** [google/mt5-base](https://huggingface.co/google/mt5-base)
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+ - **Language:** ru
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+ - **Training data:** [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (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="ru", model="lmqg/mt5-base-ruquad-qag")
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+
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+ # model prediction
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+ question_answer_pairs = model.generate_qa("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
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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", "lmqg/mt5-base-ruquad-qag")
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+ output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.")
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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 & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-base-ruquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_ruquad.default.json)
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+
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+ | | Score | Type | Dataset |
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+ |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
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+ | BERTScore | 62.3 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | Bleu_1 | 7.51 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | Bleu_2 | 4.33 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | Bleu_3 | 2.92 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | Bleu_4 | 2.12 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | METEOR | 16.85 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | MoverScore | 50.58 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | QAAlignedF1Score (BERTScore) | 74.63 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | QAAlignedF1Score (MoverScore) | 54.24 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | QAAlignedPrecision (BERTScore) | 73.97 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | QAAlignedPrecision (MoverScore) | 53.91 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | QAAlignedRecall (BERTScore) | 75.38 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | QAAlignedRecall (MoverScore) | 54.65 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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+ | ROUGE_L | 13.12 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) |
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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/qag_ruquad
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+ - dataset_name: default
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+ - input_types: ['paragraph']
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+ - output_types: ['questions_answers']
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+ - prefix_types: None
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+ - model: google/mt5-base
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+ - max_length: 512
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+ - max_length_output: 256
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+ - epoch: 12
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+ - batch: 2
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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: 32
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+ - label_smoothing: 0.0
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+
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+ The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-base-ruquad-qag/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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+ ```
config.json CHANGED
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eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_ruquad.default.json ADDED
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+ {"validation": {"Bleu_1": 0.0727623029781301, "Bleu_2": 0.04124685733407366, "Bleu_3": 0.027560417651719207, "Bleu_4": 0.019835619588151256, "METEOR": 0.16482107780717625, "ROUGE_L": 0.1276036256543389, "BERTScore": 0.620910641857151, "MoverScore": 0.5054021740615375, "QAAlignedF1Score (BERTScore)": 0.7425083894545008, "QAAlignedRecall (BERTScore)": 0.7496717520922123, "QAAlignedPrecision (BERTScore)": 0.7362779845211975, "QAAlignedF1Score (MoverScore)": 0.5408001938000293, "QAAlignedRecall (MoverScore)": 0.5447353036490986, "QAAlignedPrecision (MoverScore)": 0.5375149386697426}, "test": {"Bleu_1": 0.0751331450834612, "Bleu_2": 0.043327470712133796, "Bleu_3": 0.029211298089606604, "Bleu_4": 0.02122018857138504, "METEOR": 0.16845591256816533, "ROUGE_L": 0.1312041896872967, "BERTScore": 0.6230435508206201, "MoverScore": 0.5058324046578218, "QAAlignedF1Score (BERTScore)": 0.7462666300251022, "QAAlignedRecall (BERTScore)": 0.7537636939764181, "QAAlignedPrecision (BERTScore)": 0.7397488655371148, "QAAlignedF1Score (MoverScore)": 0.5424477310159134, "QAAlignedRecall (MoverScore)": 0.5464948672511275, "QAAlignedPrecision (MoverScore)": 0.5391184382186318}}
eval/samples.test.hyp.paragraph.questions_answers.lmqg_qag_ruquad.default.txt ADDED
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eval/samples.validation.hyp.paragraph.questions_answers.lmqg_qag_ruquad.default.txt ADDED
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