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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: it |
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datasets: |
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- lmqg/qg_itquad |
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pipeline_tag: text2text-generation |
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tags: |
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- question generation |
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- answer extraction |
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widget: |
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- text: "generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento." |
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example_title: "Question Generation Example 1" |
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- text: "generate question: L' individuazione del petrolio e lo sviluppo di nuovi giacimenti richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una produzione significativa." |
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example_title: "Question Generation Example 2" |
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- text: "generate question: il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo." |
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example_title: "Question Generation Example 3" |
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- text: "extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento." |
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example_title: "Answer Extraction Example 1" |
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- text: "extract answers: <hl> Furono introdotti autocarri compatti, come la Toyota Hilux e il Datsun Truck, seguiti dal camion Mazda (venduto come il Ford Courier), e l' Isuzu costruito Chevrolet LUV. <hl> Mitsubishi rebranded il suo Forte come Dodge D-50 pochi anni dopo la crisi petrolifera. Mazda, Mitsubishi e Isuzu avevano partnership congiunte rispettivamente con Ford, Chrysler e GM. In seguito i produttori americani introdussero le loro sostituzioni nazionali (Ford Ranger, Dodge Dakota e la Chevrolet S10/GMC S-15), ponendo fine alla loro politica di importazione vincolata." |
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example_title: "Answer Extraction Example 2" |
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model-index: |
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- name: lmqg/mt5-small-itquad-qg-ae |
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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_itquad |
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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: 7.25 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 21.84 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 17.5 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 80.61 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 56.63 |
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer |
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value: 81.81 |
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer |
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value: 82.51 |
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer |
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value: 81.17 |
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer |
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value: 56.02 |
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer |
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value: 56.32 |
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) |
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type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer |
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value: 55.76 |
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- name: BLEU4 (Answer Extraction) |
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type: bleu4_answer_extraction |
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value: 26.01 |
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- name: ROUGE-L (Answer Extraction) |
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type: rouge_l_answer_extraction |
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value: 45.15 |
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- name: METEOR (Answer Extraction) |
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type: meteor_answer_extraction |
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value: 42.68 |
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- name: BERTScore (Answer Extraction) |
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type: bertscore_answer_extraction |
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value: 90.24 |
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- name: MoverScore (Answer Extraction) |
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type: moverscore_answer_extraction |
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value: 81.17 |
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- name: AnswerF1Score (Answer Extraction) |
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type: answer_f1_score__answer_extraction |
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value: 72.09 |
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- name: AnswerExactMatch (Answer Extraction) |
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type: answer_exact_match_answer_extraction |
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value: 57.85 |
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--- |
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# Model Card of `lmqg/mt5-small-itquad-qg-ae` |
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This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation and answer extraction jointly on the [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (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:** it |
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- **Training data:** [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) (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="it", model="lmqg/mt5-small-itquad-qg-ae") |
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# model prediction |
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question_answer_pairs = model.generate_qa("Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") |
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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-itquad-qg-ae") |
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# answer extraction |
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answer = pipe("generate question: <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.") |
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# question generation |
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question = pipe("extract answers: <hl> Il 6 ottobre 1973 , la Siria e l' Egitto, con il sostegno di altre nazioni arabe, lanciarono un attacco a sorpresa su Israele, su Yom Kippur. <hl> Questo rinnovo delle ostilità nel conflitto arabo-israeliano ha liberato la pressione economica sottostante sui prezzi del petrolio. All' epoca, l' Iran era il secondo esportatore mondiale di petrolio e un vicino alleato degli Stati Uniti. Settimane più tardi, lo scià d' Iran ha detto in un' intervista: Naturalmente[il prezzo del petrolio] sta andando a salire Certamente! E come! Avete[Paesi occidentali] aumentato il prezzo del grano che ci vendete del 300 per cento, e lo stesso per zucchero e cemento.") |
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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-itquad-qg-ae/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_itquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 80.61 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_1 | 22.53 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_2 | 14.75 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_3 | 10.19 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_4 | 7.25 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| METEOR | 17.5 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| MoverScore | 56.63 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| ROUGE_L | 21.84 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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- ***Metric (Question & Answer Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_itquad.default.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| QAAlignedF1Score (BERTScore) | 81.81 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| QAAlignedF1Score (MoverScore) | 56.02 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| QAAlignedPrecision (BERTScore) | 81.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| QAAlignedPrecision (MoverScore) | 55.76 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| QAAlignedRecall (BERTScore) | 82.51 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| QAAlignedRecall (MoverScore) | 56.32 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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- ***Metric (Answer Extraction)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-itquad-qg-ae/raw/main/eval/metric.first.answer.paragraph_sentence.answer.lmqg_qg_itquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------------|--------:|:--------|:-----------------------------------------------------------------| |
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| AnswerExactMatch | 57.85 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| AnswerF1Score | 72.09 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| BERTScore | 90.24 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_1 | 39.33 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_2 | 33.64 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_3 | 29.59 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| Bleu_4 | 26.01 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| METEOR | 42.68 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| MoverScore | 81.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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| ROUGE_L | 45.15 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) | |
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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_itquad |
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- dataset_name: default |
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- input_types: ['paragraph_answer', 'paragraph_sentence'] |
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- output_types: ['question', 'answer'] |
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- prefix_types: ['qg', 'ae'] |
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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: 13 |
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- batch: 16 |
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- lr: 0.001 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 4 |
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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-itquad-qg-ae/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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