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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-30000-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: 32
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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:
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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-30000-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: 37.41
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- name: ROUGE-L (Question Answering)
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type: rouge_l_question_answering
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value: 75.9
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- name: METEOR (Question Answering)
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type: meteor_question_answering
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value: 54.68
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- name: BERTScore (Question Answering)
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type: bertscore_question_answering
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value: 97.07
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- name: MoverScore (Question Answering)
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type: moverscore_question_answering
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value: 91.88
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- name: AnswerF1Score (Question Answering)
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type: answer_f1_score__question_answering
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value: 80.37
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- name: AnswerExactMatch (Question Answering)
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type: answer_exact_match_question_answering
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value: 73.69
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---
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# Model Card of `vocabtrimmer/mt5-small-trimmed-ko-30000-koquad-qa`
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This model is fine-tuned version of [ckpts/mt5-small-trimmed-ko-30000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-30000) 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-30000](https://huggingface.co/ckpts/mt5-small-trimmed-ko-30000)
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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 | 73.69 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| AnswerF1Score | 80.37 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| BERTScore | 97.07 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_1 | 70.24 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_2 | 61.81 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_3 | 51.41 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| Bleu_4 | 37.41 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| METEOR | 54.68 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| MoverScore | 91.88 | default | [lmqg/qg_koquad](https://huggingface.co/datasets/lmqg/qg_koquad) |
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| ROUGE_L | 75.9 | 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-30000
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- max_length: 512
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- max_length_output: 32
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- epoch: 5
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- batch: 32
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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: 2
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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-30000-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":
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{"validation": {"Bleu_1": 0.6992903294141829, "Bleu_2": 0.6127222228962876, "Bleu_3": 0.5083515309130512, "Bleu_4": 0.3635940594823205, "METEOR": 0.5462332421668936, "ROUGE_L": 0.7541467112803363, "BERTScore": 0.9710218722812839, "MoverScore": 0.9181111629442422, "AnswerF1Score": 79.72134499367644, "AnswerExactMatch": 73.06625043357613}, "test": {"Bleu_1": 0.7024203558129186, "Bleu_2": 0.6180556794660192, "Bleu_3": 0.5140638613931836, "Bleu_4": 0.37413409334289116, "METEOR": 0.5468391612861541, "ROUGE_L": 0.7589999956094918, "BERTScore": 0.970729525474637, "MoverScore": 0.9188335398437054, "AnswerF1Score": 80.36954056464982, "AnswerExactMatch": 73.69060006937218}}
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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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