--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: ru datasets: - lmqg/qag_ruquad pipeline_tag: text2text-generation tags: - questions and answers generation widget: - 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." example_title: "Questions & Answers Generation Example 1" model-index: - name: lmqg/mbart-large-cc25-ruquad-qag results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qag_ruquad type: default args: default metrics: - name: BLEU4 (Question & Answer Generation) type: bleu4_question_answer_generation value: 4.33 - name: ROUGE-L (Question & Answer Generation) type: rouge_l_question_answer_generation value: 18.59 - name: METEOR (Question & Answer Generation) type: meteor_question_answer_generation value: 23.52 - name: BERTScore (Question & Answer Generation) type: bertscore_question_answer_generation value: 69.58 - name: MoverScore (Question & Answer Generation) type: moverscore_question_answer_generation value: 52.29 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) type: qa_aligned_f1_score_bertscore_question_answer_generation value: 77.36 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) type: qa_aligned_recall_bertscore_question_answer_generation value: 80.05 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) type: qa_aligned_precision_bertscore_question_answer_generation value: 74.97 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) type: qa_aligned_f1_score_moverscore_question_answer_generation value: 56.1 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) type: qa_aligned_recall_moverscore_question_answer_generation value: 58.11 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) type: qa_aligned_precision_moverscore_question_answer_generation value: 54.4 --- # Model Card of `lmqg/mbart-large-cc25-ruquad-qag` This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) 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). ### Overview - **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) - **Language:** ru - **Training data:** [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) (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="ru", model="lmqg/mbart-large-cc25-ruquad-qag") # model prediction 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.") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad-qag") 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.") ``` ## Evaluation - ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qag/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_ruquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-------------------------------------------------------------------| | BERTScore | 69.58 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | Bleu_1 | 13.13 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | Bleu_2 | 8.29 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | Bleu_3 | 5.85 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | Bleu_4 | 4.33 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | METEOR | 23.52 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | MoverScore | 52.29 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | QAAlignedF1Score (BERTScore) | 77.36 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | QAAlignedF1Score (MoverScore) | 56.1 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | QAAlignedPrecision (BERTScore) | 74.97 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | QAAlignedPrecision (MoverScore) | 54.4 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | QAAlignedRecall (BERTScore) | 80.05 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | QAAlignedRecall (MoverScore) | 58.11 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | | ROUGE_L | 18.59 | default | [lmqg/qag_ruquad](https://huggingface.co/datasets/lmqg/qag_ruquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qag_ruquad - dataset_name: default - input_types: ['paragraph'] - output_types: ['questions_answers'] - prefix_types: None - model: facebook/mbart-large-cc25 - max_length: 512 - max_length_output: 256 - epoch: 6 - batch: 2 - lr: 0.0001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 32 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-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", } ```