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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: fr |
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datasets: |
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- lmqg/qg_frquad |
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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: "Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc." |
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example_title: "Question Generation Example 1" |
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- text: "Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la <hl> Grande Guerre <hl> de 14-18, ou son rejet par l'électorat en juillet 1945." |
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example_title: "Question Generation Example 2" |
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- text: "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938." |
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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-frquad-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_frquad |
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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: 0.72 |
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- name: ROUGE-L (Question Generation) |
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type: rouge_l_question_generation |
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value: 16.4 |
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- name: METEOR (Question Generation) |
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type: meteor_question_generation |
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value: 7.78 |
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- name: BERTScore (Question Generation) |
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type: bertscore_question_generation |
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value: 71.48 |
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- name: MoverScore (Question Generation) |
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type: moverscore_question_generation |
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value: 50.35 |
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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: 9.7 |
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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: 33.61 |
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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: 26.31 |
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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: 80.27 |
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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: 55.65 |
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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: 81.27 |
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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: 81.25 |
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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: 81.29 |
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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: 55.61 |
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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: 55.6 |
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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: 55.61 |
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--- |
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# Model Card of `lmqg/mbart-large-cc25-frquad-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_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (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:** fr |
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- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (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="fr", model="lmqg/mbart-large-cc25-frquad-qg") |
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# model prediction |
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questions = model.generate_q(list_context="Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.", list_answer="le Suprême Berger") |
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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-frquad-qg") |
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output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.") |
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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-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) |
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| | Score | Type | Dataset | |
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|:-----------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 71.48 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_1 | 14.36 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_2 | 3.58 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_3 | 1.45 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_4 | 0.72 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| METEOR | 7.78 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| MoverScore | 50.35 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| ROUGE_L | 16.4 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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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-frquad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json) |
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| | Score | Type | Dataset | |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------| |
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| BERTScore | 80.27 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_1 | 29.47 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_2 | 19.07 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_3 | 13.39 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| Bleu_4 | 9.7 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| METEOR | 26.31 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| MoverScore | 55.65 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| QAAlignedF1Score (BERTScore) | 81.27 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| QAAlignedF1Score (MoverScore) | 55.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| QAAlignedPrecision (BERTScore) | 81.29 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| QAAlignedPrecision (MoverScore) | 55.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| QAAlignedRecall (BERTScore) | 81.25 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| QAAlignedRecall (MoverScore) | 55.6 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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| ROUGE_L | 33.61 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | |
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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_frquad |
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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: 8 |
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- batch: 4 |
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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: 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-frquad-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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