model update
Browse files
README.md
CHANGED
@@ -46,38 +46,38 @@ model-index:
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- name: MoverScore (Question Generation)
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type: moverscore_question_generation
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value: 54.64
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- name: BLEU4 (Question & Answer Generation
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type:
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value: 0.08
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- name: ROUGE-L (Question & Answer Generation
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type:
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value: 16.17
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- name: METEOR (Question & Answer Generation
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type:
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value: 18.96
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- name: BERTScore (Question & Answer Generation
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type:
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value: 74.29
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- name: MoverScore (Question & Answer Generation
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type:
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value: 52.45
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation
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type:
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value: 90.55
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation
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type:
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value: 90.51
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation
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type:
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value: 90.59
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation
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type:
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value: 64.33
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation
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type:
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value: 64.29
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation
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type:
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value: 64.37
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---
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@@ -132,7 +132,7 @@ output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereins
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| ROUGE_L | 10.08 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) |
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- ***Metric (Question & Answer Generation
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| | Score | Type | Dataset |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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- name: MoverScore (Question Generation)
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type: moverscore_question_generation
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value: 54.64
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- name: BLEU4 (Question & Answer Generation)
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type: bleu4_question_answer_generation
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value: 0.08
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- name: ROUGE-L (Question & Answer Generation)
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type: rouge_l_question_answer_generation
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value: 16.17
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- name: METEOR (Question & Answer Generation)
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type: meteor_question_answer_generation
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value: 18.96
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- name: BERTScore (Question & Answer Generation)
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type: bertscore_question_answer_generation
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value: 74.29
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- name: MoverScore (Question & Answer Generation)
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type: moverscore_question_answer_generation
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value: 52.45
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
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type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
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value: 90.55
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
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type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
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value: 90.51
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
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type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
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value: 90.59
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
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type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
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value: 64.33
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
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type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
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value: 64.29
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
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type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
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value: 64.37
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---
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| ROUGE_L | 10.08 | default | [lmqg/qg_dequad](https://huggingface.co/datasets/lmqg/qg_dequad) |
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- ***Metric (Question & Answer Generation)***: QAG metrics are computed with *the gold answer* and generated question on it for this model, as the model cannot provide an answer. [raw metric file](https://huggingface.co/lmqg/mt5-small-dequad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_dequad.default.json)
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| | Score | Type | Dataset |
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|:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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