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README.md
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@@ -31,33 +31,33 @@ 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-ko-60000-koquad-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:** ko
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- **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (default)
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
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@@ -93,16 +93,16 @@ output = pipe("question: 매드 클라운이 참가해 큰 화제를 모았던
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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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@@ -114,12 +114,12 @@ 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.
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- fp16: False
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- random_seed: 1
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- gradient_accumulation_steps: 2
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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: 35.86
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value: 77.74
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value: 55.96
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value: 97.28
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value: 92.46
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value: 81.57
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value: 75.06
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-60000-koquad-qa`
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This model is fine-tuned version of [ckpts/mt5-small-trimmed-ko-60000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-60000) for question answering task on the [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (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-ko-60000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-60000)
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- **Language:** ko
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- **Training data:** [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) (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 | 75.06 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| AnswerF1Score | 81.57 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| BERTScore | 97.28 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_1 | 71.37 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_2 | 62.48 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_3 | 51 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_4 | 35.86 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| METEOR | 55.96 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| MoverScore | 92.46 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| ROUGE_L | 77.74 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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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-ko-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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- random_seed: 1
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- gradient_accumulation_steps: 2
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eval/metric.first.answer.paragraph_question.answer.lmqg_qg_koquad.default.json
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{"validation": {"Bleu_1": 0.
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{"validation": {"Bleu_1": 0.7118608328939682, "Bleu_2": 0.6261082641370145, "Bleu_3": 0.5241052545619721, "Bleu_4": 0.3834854328398384, "METEOR": 0.5577362960886979, "ROUGE_L": 0.767547458076137, "BERTScore": 0.9716366701786856, "MoverScore": 0.9203647551982136, "AnswerF1Score": 79.6981596851524, "AnswerExactMatch": 73.08359347901491}, "test": {"Bleu_1": 0.7137291089920318, "Bleu_2": 0.6248437350095181, "Bleu_3": 0.5099545735242434, "Bleu_4": 0.35863598370102473, "METEOR": 0.5595853856844631, "ROUGE_L": 0.7774229793701365, "BERTScore": 0.9728185705145407, "MoverScore": 0.924570053934317, "AnswerF1Score": 81.57176419572936, "AnswerExactMatch": 75.06070065903573}}
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eval/samples.test.hyp.paragraph_question.answer.lmqg_qg_koquad.default.txt
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eval/samples.validation.hyp.paragraph_question.answer.lmqg_qg_koquad.default.txt
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