--- license: cc-by-4.0 metrics: - bleu4 - meteor - rouge-l - bertscore - moverscore language: ru datasets: - lmqg/qg_ruquad pipeline_tag: text2text-generation tags: - question 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: "Question Generation Example 1" - text: "Однако, франкоязычный Квебек практически никогда не включается в состав Латинской Америки." example_title: "Question Generation Example 2" - text: "Классическим примером международного синдиката XX века была группа компаний Де Бирс , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами." example_title: "Question Generation Example 3" model-index: - name: lmqg/mt5-small-ruquad-qg results: - task: name: Text2text Generation type: text2text-generation dataset: name: lmqg/qg_ruquad type: default args: default metrics: - name: BLEU4 (Question Generation) type: bleu4_question_generation value: 16.31 - name: ROUGE-L (Question Generation) type: rouge_l_question_generation value: 31.39 - name: METEOR (Question Generation) type: meteor_question_generation value: 26.39 - name: BERTScore (Question Generation) type: bertscore_question_generation value: 84.27 - name: MoverScore (Question Generation) type: moverscore_question_generation value: 62.49 - name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer value: 90.17 - name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer value: 90.16 - name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer value: 90.17 - name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer value: 68.22 - name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer value: 68.21 - name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer value: 68.23 --- # Model Card of `lmqg/mt5-small-ruquad-qg` This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) (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:** ru - **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_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/mt5-small-ruquad-qg") # model prediction questions = model.generate_q(list_context="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.", list_answer="в мае 1860 года") ``` - With `transformers` ```python from transformers import pipeline pipe = pipeline("text2text-generation", "lmqg/mt5-small-ruquad-qg") 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 Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:-----------|--------:|:--------|:-----------------------------------------------------------------| | BERTScore | 84.27 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_1 | 31.03 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_2 | 24.58 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_3 | 19.92 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | Bleu_4 | 16.31 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | METEOR | 26.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | MoverScore | 62.49 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | ROUGE_L | 31.39 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | - ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mt5-small-ruquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json) | | Score | Type | Dataset | |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| | QAAlignedF1Score (BERTScore) | 90.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedF1Score (MoverScore) | 68.22 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedPrecision (BERTScore) | 90.17 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedPrecision (MoverScore) | 68.23 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedRecall (BERTScore) | 90.16 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | | QAAlignedRecall (MoverScore) | 68.21 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | ## Training hyperparameters The following hyperparameters were used during fine-tuning: - dataset_path: lmqg/qg_ruquad - dataset_name: default - input_types: ['paragraph_answer'] - output_types: ['question'] - prefix_types: None - model: google/mt5-small - max_length: 512 - max_length_output: 32 - epoch: 5 - batch: 64 - lr: 0.001 - fp16: False - random_seed: 1 - gradient_accumulation_steps: 1 - label_smoothing: 0.15 The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mt5-small-ruquad-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", } ```