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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-15000-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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@@ -114,15 +114,15 @@ 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:
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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:
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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/vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qa/raw/main/trainer_config.json).
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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: 35.71
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value: 78.59
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value: 56.74
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value: 97.44
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value: 92.81
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value: 82.66
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value: 76.41
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qa`
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This model is fine-tuned version of [ckpts/mt5-small-trimmed-ko-15000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-15000) 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-15000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-15000)
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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 | 76.41 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| AnswerF1Score | 82.66 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| BERTScore | 97.44 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_1 | 71.78 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_2 | 62.64 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_3 | 50.82 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_4 | 35.71 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| METEOR | 56.74 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| MoverScore | 92.81 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| ROUGE_L | 78.59 | 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-15000
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- max_length: 512
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- max_length_output: 32
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- epoch: 10
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- batch: 64
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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: 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/vocabtrimmer/mt5-small-trimmed-ko-15000-koquad-qa/raw/main/trainer_config.json).
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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": 0.7100208768266482, "Bleu_2": 0.6200193491759389, "Bleu_3": 0.5080818087942911, "Bleu_4": 0.3632132399466476, "METEOR": 0.562890596693216, "ROUGE_L": 0.7740274367814087, "BERTScore": 0.9726204494717963, "MoverScore": 0.9231726569882075, "AnswerF1Score": 81.02993414647949, "AnswerExactMatch": 74.54040929587235}, "test": {"Bleu_1": 0.7178126912874191, "Bleu_2": 0.6263725853703858, "Bleu_3": 0.5082091784867124, "Bleu_4": 0.3570804063484529, "METEOR": 0.5673725371314443, "ROUGE_L": 0.7859017937674925, "BERTScore": 0.9743670976042045, "MoverScore": 0.9280690877126812, "AnswerF1Score": 82.66070721585822, "AnswerExactMatch": 76.41345820326049}}
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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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