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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: es |
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datasets: |
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- lmqg/qg_esquad |
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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: "del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India." |
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example_title: "Question Generation Example 1" |
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- text: "a <hl> noviembre <hl> , que es también la estación lluviosa." |
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example_title: "Question Generation Example 2" |
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- text: "como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de septiembre de 2013." |
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example_title: "Question Generation Example 3" |
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model-index: |
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- name: lmqg/mt5-small-esquad-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_esquad |
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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: 9.61 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 24.62 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 22.71 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 84.07 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 59.06 |
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- name: BLEU4 (Question & Answer Generation (with Gold Answer)) |
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type: bleu4_question_answer_generation_with_gold_answer |
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value: 12.77 |
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- name: ROUGE-L (Question & Answer Generation (with Gold Answer)) |
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type: rouge_l_question_answer_generation_with_gold_answer |
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value: 42.77 |
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- name: METEOR (Question & Answer Generation (with Gold Answer)) |
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type: meteor_question_answer_generation_with_gold_answer |
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value: 37.58 |
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- name: BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: bertscore_question_answer_generation_with_gold_answer |
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value: 89.41 |
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- name: MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: moverscore_question_answer_generation_with_gold_answer |
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value: 63.56 |
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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: 89.43 |
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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: 89.41 |
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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: 89.44 |
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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: 63.73 |
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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: 63.72 |
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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: 63.75 |
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- name: BLEU4 (Question & Answer Generation) |
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type: bleu4_question_answer_generation |
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value: 1.83 |
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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: 15.46 |
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- name: METEOR (Question & Answer Generation) |
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type: meteor_question_answer_generation |
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value: 22.22 |
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- name: BERTScore (Question & Answer Generation) |
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type: bertscore_question_answer_generation |
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value: 69.78 |
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- name: MoverScore (Question & Answer Generation) |
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type: moverscore_question_answer_generation |
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value: 51.8 |
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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: 79.89 |
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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: 82.56 |
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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: 77.46 |
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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: 54.82 |
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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: 56.52 |
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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: 53.31 |
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--- |
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# Model Card of `lmqg/mt5-small-esquad-qg` |
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This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). |
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### Overview |
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- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small) |
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- **Language:** es |
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- **Training data:** [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) (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="es", model="lmqg/mt5-small-esquad-qg") |
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# model prediction |
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questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre") |
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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/mt5-small-esquad-qg") |
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output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.") |
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``` |
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## Evaluation |
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- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_esquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 84.07 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_1 | 26.03 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_2 | 17.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_3 | 12.88 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_4 | 9.61 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| METEOR | 22.71 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| MoverScore | 59.06 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| ROUGE_L | 24.62 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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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/mt5-small-esquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 89.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_1 | 39.84 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_2 | 26.59 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_3 | 18.28 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_4 | 12.77 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| METEOR | 37.58 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| MoverScore | 63.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedF1Score (BERTScore) | 89.43 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedF1Score (MoverScore) | 63.73 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedPrecision (BERTScore) | 89.44 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedPrecision (MoverScore) | 63.75 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedRecall (BERTScore) | 89.41 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedRecall (MoverScore) | 63.72 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| ROUGE_L | 42.77 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/mt5-small-esquad-ae`](https://huggingface.co/lmqg/mt5-small-esquad-ae). [raw metric file](https://huggingface.co/lmqg/mt5-small-esquad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_esquad.default.lmqg_mt5-small-esquad-ae.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 69.78 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_1 | 11.1 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_2 | 5.5 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_3 | 3 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| Bleu_4 | 1.83 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| METEOR | 22.22 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| MoverScore | 51.8 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedF1Score (BERTScore) | 79.89 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedF1Score (MoverScore) | 54.82 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedPrecision (BERTScore) | 77.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedPrecision (MoverScore) | 53.31 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedRecall (BERTScore) | 82.56 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| QAAlignedRecall (MoverScore) | 56.52 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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| ROUGE_L | 15.46 | default | [lmqg/qg_esquad](https://huggingface.co/datasets/lmqg/qg_esquad) | |
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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_esquad |
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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: google/mt5-small |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 16 |
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- batch: 64 |
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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: 1 |
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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/mt5-small-esquad-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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