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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/qg_ruquad |
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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: "Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов." |
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example_title: "Question Generation Example 1" |
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- text: "Однако, франкоязычный <hl> Квебек <hl> практически никогда не включается в состав Латинской Америки." |
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example_title: "Question Generation Example 2" |
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- text: "Классическим примером международного синдиката XX века была группа компаний <hl> Де Бирс <hl> , которая в 1980-е годы контролировала до 90 % мировой торговли алмазами." |
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example_title: "Question Generation Example 3" |
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model-index: |
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- name: lmqg/mbart-large-cc25-ruquad-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_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 Generation) |
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type: bleu4_question_generation |
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value: 18.8 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 34.18 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 29.3 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 87.18 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 65.88 |
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 92.08 |
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 92.08 |
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 92.09 |
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 71.45 |
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 71.45 |
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] |
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type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer |
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value: 71.46 |
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer |
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value: 79.14 |
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer |
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value: 82.85 |
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer |
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value: 75.88 |
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer |
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value: 56.25 |
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer |
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value: 58.93 |
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] |
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type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer |
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value: 54.01 |
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--- |
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# Model Card of `lmqg/mbart-large-cc25-ruquad-qg` |
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This model is fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) 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). |
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### Overview |
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- **Language model:** [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) |
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- **Language:** ru |
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- **Training data:** [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_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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### 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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# initialize model |
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model = TransformersQG(language="ru", model="lmqg/mbart-large-cc25-ruquad-qg") |
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# model prediction |
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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") |
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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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pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-ruquad-qg") |
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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, <hl> в мае 1860 года <hl> провёл серию опытов.") |
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``` |
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## Evaluation |
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_ruquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 87.18 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| Bleu_1 | 35.25 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| Bleu_2 | 28.1 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| Bleu_3 | 22.87 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| Bleu_4 | 18.8 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| METEOR | 29.3 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| MoverScore | 65.88 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| ROUGE_L | 34.18 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 92.08 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedF1Score (MoverScore) | 71.45 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedPrecision (BERTScore) | 92.09 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedPrecision (MoverScore) | 71.46 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedRecall (BERTScore) | 92.08 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedRecall (MoverScore) | 71.45 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mbart-large-cc25-ruquad-ae`](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-ae). [raw metric file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_ruquad.default.lmqg_mbart-large-cc25-ruquad-ae.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 79.14 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedF1Score (MoverScore) | 56.25 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedPrecision (BERTScore) | 75.88 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedPrecision (MoverScore) | 54.01 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedRecall (BERTScore) | 82.85 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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| QAAlignedRecall (MoverScore) | 58.93 | default | [lmqg/qg_ruquad](https://huggingface.co/datasets/lmqg/qg_ruquad) | |
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## Training hyperparameters |
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The following hyperparameters were used during fine-tuning: |
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- dataset_path: lmqg/qg_ruquad |
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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: facebook/mbart-large-cc25 |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 17 |
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- batch: 4 |
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- lr: 0.0001 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 16 |
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- label_smoothing: 0.15 |
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/mbart-large-cc25-ruquad-qg/raw/main/trainer_config.json). |
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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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