model update
Browse files- README.md +26 -26
- config.json +1 -1
- eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_frquad.default.json +1 -1
- eval/samples.test.hyp.paragraph.questions_answers.lmqg_qag_frquad.default.txt +0 -0
- eval/samples.validation.hyp.paragraph.questions_answers.lmqg_qag_frquad.default.txt +0 -0
- pytorch_model.bin +2 -2
- tokenizer_config.json +1 -1
README.md
CHANGED
@@ -7,14 +7,14 @@ metrics:
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- rouge-l
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- bertscore
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- moverscore
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language:
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datasets:
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- lmqg/qag_frquad
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pipeline_tag: text2text-generation
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tags:
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- questions and answers generation
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widget:
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- text: "
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example_title: "Questions & Answers Generation Example 1"
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model-index:
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- name: lmqg/mt5-small-frquad-qag
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value: 15.33
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- name: METEOR (Question & Answer Generation)
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type: meteor_question_answer_generation
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value: 16.
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- name: BERTScore (Question & Answer Generation)
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type: bertscore_question_answer_generation
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value: 81
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- name: MoverScore (Question & Answer Generation)
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type: moverscore_question_answer_generation
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value:
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
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type: qa_aligned_f1_score_bertscore_question_answer_generation
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value:
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
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type: qa_aligned_recall_bertscore_question_answer_generation
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value:
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
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type: qa_aligned_precision_bertscore_question_answer_generation
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value:
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
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type: qa_aligned_f1_score_moverscore_question_answer_generation
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value:
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
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type: qa_aligned_recall_moverscore_question_answer_generation
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value:
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
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type: qa_aligned_precision_moverscore_question_answer_generation
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value:
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---
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# Model Card of `lmqg/mt5-small-frquad-qag`
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@@ -68,7 +68,7 @@ This model is fine-tuned version of [google/mt5-small](https://huggingface.co/go
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### Overview
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- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
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- **Language:**
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- **Training data:** [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) (default)
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="
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# model prediction
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question_answer_pairs = model.generate_qa("
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```
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@@ -92,7 +92,7 @@ question_answer_pairs = model.generate_qa("William Turner was an English painter
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/mt5-small-frquad-qag")
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output = pipe("
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```
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| | Score | Type | Dataset |
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|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
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| BERTScore | 81
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| Bleu_1 | 9.
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| Bleu_2 | 4.
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| Bleu_3 | 2.64 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| Bleu_4 | 1.54 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| METEOR | 16.
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| MoverScore |
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| QAAlignedF1Score (BERTScore) |
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| QAAlignedF1Score (MoverScore) |
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| QAAlignedPrecision (BERTScore) |
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| QAAlignedPrecision (MoverScore) |
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| QAAlignedRecall (BERTScore) |
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| QAAlignedRecall (MoverScore) |
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| ROUGE_L | 15.33 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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- rouge-l
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- bertscore
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- moverscore
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language: fr
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datasets:
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- lmqg/qag_frquad
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pipeline_tag: text2text-generation
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tags:
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- questions and answers generation
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widget:
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- text: "Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc."
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example_title: "Questions & Answers Generation Example 1"
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model-index:
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- name: lmqg/mt5-small-frquad-qag
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value: 15.33
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- name: METEOR (Question & Answer Generation)
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type: meteor_question_answer_generation
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value: 16.12
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- name: BERTScore (Question & Answer Generation)
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type: bertscore_question_answer_generation
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value: 64.81
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- name: MoverScore (Question & Answer Generation)
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type: moverscore_question_answer_generation
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value: 50.01
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- name: QAAlignedF1Score-BERTScore (Question & Answer Generation)
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type: qa_aligned_f1_score_bertscore_question_answer_generation
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value: 77.23
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- name: QAAlignedRecall-BERTScore (Question & Answer Generation)
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type: qa_aligned_recall_bertscore_question_answer_generation
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value: 77.74
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- name: QAAlignedPrecision-BERTScore (Question & Answer Generation)
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type: qa_aligned_precision_bertscore_question_answer_generation
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value: 76.76
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- name: QAAlignedF1Score-MoverScore (Question & Answer Generation)
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type: qa_aligned_f1_score_moverscore_question_answer_generation
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value: 52.36
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- name: QAAlignedRecall-MoverScore (Question & Answer Generation)
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type: qa_aligned_recall_moverscore_question_answer_generation
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value: 52.54
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- name: QAAlignedPrecision-MoverScore (Question & Answer Generation)
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type: qa_aligned_precision_moverscore_question_answer_generation
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value: 52.19
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---
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# Model Card of `lmqg/mt5-small-frquad-qag`
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### Overview
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- **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
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- **Language:** fr
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- **Training data:** [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) (default)
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
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from lmqg import TransformersQG
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# initialize model
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model = TransformersQG(language="fr", model="lmqg/mt5-small-frquad-qag")
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# model prediction
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question_answer_pairs = model.generate_qa("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
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```
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from transformers import pipeline
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pipe = pipeline("text2text-generation", "lmqg/mt5-small-frquad-qag")
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output = pipe("Créateur » (Maker), lui aussi au singulier, « le Suprême Berger » (The Great Shepherd) ; de l'autre, des réminiscences de la théologie de l'Antiquité : le tonnerre, voix de Jupiter, « Et souvent ta voix gronde en un tonnerre terrifiant », etc.")
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```
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| | Score | Type | Dataset |
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|:--------------------------------|--------:|:--------|:-------------------------------------------------------------------|
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| BERTScore | 64.81 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| Bleu_1 | 9.63 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| Bleu_2 | 4.73 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| Bleu_3 | 2.64 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| Bleu_4 | 1.54 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| METEOR | 16.12 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| MoverScore | 50.01 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| QAAlignedF1Score (BERTScore) | 77.23 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| QAAlignedF1Score (MoverScore) | 52.36 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| QAAlignedPrecision (BERTScore) | 76.76 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| QAAlignedPrecision (MoverScore) | 52.19 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| QAAlignedRecall (BERTScore) | 77.74 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| QAAlignedRecall (MoverScore) | 52.54 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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| ROUGE_L | 15.33 | default | [lmqg/qag_frquad](https://huggingface.co/datasets/lmqg/qag_frquad) |
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config.json
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{
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"_name_or_path": "lmqg_output/mt5-small-frquad-qag/
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"add_prefix": false,
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"architectures": [
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"MT5ForConditionalGeneration"
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{
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"_name_or_path": "lmqg_output/mt5-small-frquad-qag/model_mzgdpa/epoch_5",
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"add_prefix": false,
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"architectures": [
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"MT5ForConditionalGeneration"
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eval/metric.first.answer.paragraph.questions_answers.lmqg_qag_frquad.default.json
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{"validation": {"Bleu_1": 0.
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{"validation": {"Bleu_1": 0.2564384685675875, "Bleu_2": 0.13902990103351057, "Bleu_3": 0.07604461713510292, "Bleu_4": 0.048106284604614094, "METEOR": 0.17653494749823867, "ROUGE_L": 0.2232438933303529, "BERTScore": 0.7172536270615334, "MoverScore": 0.5143960911054434, "QAAlignedF1Score (BERTScore)": 0.7857067185106722, "QAAlignedRecall (BERTScore)": 0.7718289041633918, "QAAlignedPrecision (BERTScore)": 0.8009370848859868, "QAAlignedF1Score (MoverScore)": 0.537190892301648, "QAAlignedRecall (MoverScore)": 0.5271966829181792, "QAAlignedPrecision (MoverScore)": 0.5480096039350669}, "test": {"Bleu_1": 0.09633246448107434, "Bleu_2": 0.04727655391960404, "Bleu_3": 0.026361268946164768, "Bleu_4": 0.015435301672827135, "METEOR": 0.16121154834362886, "ROUGE_L": 0.15333781495958926, "BERTScore": 0.6480726455069834, "MoverScore": 0.5000641898713922, "QAAlignedF1Score (BERTScore)": 0.7722652721437624, "QAAlignedRecall (BERTScore)": 0.7774018039561464, "QAAlignedPrecision (BERTScore)": 0.7676207746892701, "QAAlignedF1Score (MoverScore)": 0.5235572755796504, "QAAlignedRecall (MoverScore)": 0.5254281358359643, "QAAlignedPrecision (MoverScore)": 0.5218568654099053}}
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eval/samples.test.hyp.paragraph.questions_answers.lmqg_qag_frquad.default.txt
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eval/samples.validation.hyp.paragraph.questions_answers.lmqg_qag_frquad.default.txt
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pytorch_model.bin
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tokenizer_config.json
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"additional_special_tokens": null,
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"eos_token": "</s>",
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"extra_ids": 0,
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"name_or_path": "lmqg_output/mt5-small-frquad-qag/
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"pad_token": "<pad>",
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"special_tokens_map_file": "/home/c.c2042013/.cache/huggingface/transformers/685ac0ca8568ec593a48b61b0a3c272beee9bc194a3c7241d15dcadb5f875e53.f76030f3ec1b96a8199b2593390c610e76ca8028ef3d24680000619ffb646276",
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"name_or_path": "lmqg_output/mt5-small-frquad-qag/model_mzgdpa/epoch_5",
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