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README.md
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@@ -29,33 +29,33 @@ model-index:
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metrics:
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- name: BLEU4 (Question Answering)
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type: bleu4_question_answering
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value:
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value:
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value:
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value:
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value:
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value:
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value:
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qa`
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This model is fine-tuned version of [
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### Overview
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- **Language model:** [
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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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| | Score | Type | Dataset |
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|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
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| AnswerExactMatch |
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| AnswerF1Score |
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| BERTScore |
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| Bleu_1 |
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| Bleu_2 |
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| Bleu_3 |
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| Bleu_4 |
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| METEOR |
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| MoverScore |
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| ROUGE_L |
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@@ -112,10 +112,10 @@ The following hyperparameters were used during fine-tuning:
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- input_types: ['paragraph_question']
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- output_types: ['answer']
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- prefix_types: None
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- model:
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- max_length: 512
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- max_length_output: 32
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- epoch:
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- batch: 32
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- lr: 0.0005
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- fp16: False
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metrics:
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- name: BLEU4 (Question Answering)
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type: bleu4_question_answering
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value: 9.49
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value: 34.13
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value: 29.49
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value: 90.7
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value: 76.17
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value: 56.49
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value: 40.52
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-it-60000-itquad-qa`
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This model is fine-tuned version of [ckpts/mt5-small-trimmed-it-60000](https://huggingface.co/ckpts/mt5-small-trimmed-it-60000) for question answering task 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:** [ckpts/mt5-small-trimmed-it-60000](https://huggingface.co/ckpts/mt5-small-trimmed-it-60000)
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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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| | Score | Type | Dataset |
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|:-----------------|--------:|:--------|:-----------------------------------------------------------------|
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| AnswerExactMatch | 40.52 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| AnswerF1Score | 56.49 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| BERTScore | 90.7 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| Bleu_1 | 20.1 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| Bleu_2 | 15.3 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| Bleu_3 | 12.04 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| Bleu_4 | 9.49 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| METEOR | 29.49 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| MoverScore | 76.17 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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| ROUGE_L | 34.13 | default | [lmqg/qg_itquad](https://huggingface.co/datasets/lmqg/qg_itquad) |
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- input_types: ['paragraph_question']
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- output_types: ['answer']
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- prefix_types: None
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- model: ckpts/mt5-small-trimmed-it-60000
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- max_length: 512
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- max_length_output: 32
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- epoch: 15
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- batch: 32
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- lr: 0.0005
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- fp16: False
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eval/metric.first.answer.paragraph_question.answer.lmqg_qg_itquad.default.json
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{"validation": {"Bleu_1": 0.
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{"validation": {"Bleu_1": 0.2168200141551461, "Bleu_2": 0.16457544807858454, "Bleu_3": 0.13015003475283593, "Bleu_4": 0.10269429902258806, "METEOR": 0.3176781214512819, "ROUGE_L": 0.3446343748642225, "BERTScore": 0.9211360962824245, "MoverScore": 0.7948918389120013, "AnswerF1Score": 62.031363955784265, "AnswerExactMatch": 48.258641082928115}, "test": {"Bleu_1": 0.20095433209719146, "Bleu_2": 0.15300907487721307, "Bleu_3": 0.12044830411721509, "Bleu_4": 0.09492166933146612, "METEOR": 0.2948779214599961, "ROUGE_L": 0.34130328206695704, "BERTScore": 0.9069560774915211, "MoverScore": 0.7617222323179461, "AnswerF1Score": 56.491622198303375, "AnswerExactMatch": 40.51780785911421}}
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eval/samples.test.hyp.paragraph_question.answer.lmqg_qg_itquad.default.txt
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eval/samples.validation.hyp.paragraph_question.answer.lmqg_qg_itquad.default.txt
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