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lmqg
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mt5-small-frquad-qg

Text2Text Generation
Transformers
PyTorch
French
mt5
question generation
Eval Results
Inference Endpoints
Model card Files Community
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@@ -33,62 +33,43 @@ model-index:
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  metrics:
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  - name: BLEU4
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  type: bleu4
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- value: 0.0855433375613263
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  - name: ROUGE-L
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  type: rouge-l
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- value: 0.28563221971096636
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  - name: METEOR
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  type: meteor
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- value: 0.17511468784257161
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  - name: BERTScore
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  type: bertscore
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- value: 0.8070819788573244
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  - name: MoverScore
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  type: moverscore
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- value: 0.5650286067741268
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- - name: QAAlignedF1Score (BERTScore)
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- type: qa_aligned_f1_score_bertscore
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- value: 0.8852103609584148
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- - name: QAAlignedRecall (BERTScore)
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- type: qa_aligned_recall_bertscore
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- value: 0.8850916407941601
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- - name: QAAlignedPrecision (BERTScore)
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- type: qa_aligned_precision_bertscore
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- value: 0.8853370789590423
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- - name: QAAlignedF1Score (MoverScore)
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- type: qa_aligned_f1_score_moverscore
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- value: 0.6245594753629199
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- - name: QAAlignedRecall (MoverScore)
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- type: qa_aligned_recall_moverscore
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- value: 0.6245060109800799
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- - name: QAAlignedPrecision (MoverScore)
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- type: qa_aligned_precision_moverscore
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- value: 0.6246199947286513
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  ---
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  # Model Card of `lmqg/mt5-small-frquad`
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- This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the
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- [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
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- Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)).
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-
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- ```
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-
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- @inproceedings{ushio-etal-2022-generative,
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- title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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- author = "Ushio, Asahi and
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- Alva-Manchego, Fernando and
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- Camacho-Collados, Jose",
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- booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
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- month = dec,
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- year = "2022",
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- address = "Abu Dhabi, U.A.E.",
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- publisher = "Association for Computational Linguistics",
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- }
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-
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- ```
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-
92
  ### Overview
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  - **Language model:** [google/mt5-small](https://huggingface.co/google/mt5-small)
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  - **Language:** fr
@@ -100,42 +81,52 @@ Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](h
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  ### Usage
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  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
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  ```python
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-
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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')
 
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  # model prediction
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- question = model.generate_q(list_context=["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."], list_answer=["le Suprême Berger"])
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  ```
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  - With `transformers`
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  ```python
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-
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  from transformers import pipeline
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- # initialize model
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- pipe = pipeline("text2text-generation", 'lmqg/mt5-small-frquad')
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- # question generation
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- question = pipe('Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (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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  ```
122
 
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- ## Evaluation Metrics
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- ### Metrics
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- | Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
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- |:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
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- | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | default | 0.086 | 0.286 | 0.175 | 0.807 | 0.565 | [link](https://huggingface.co/lmqg/mt5-small-frquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) |
 
 
 
 
 
 
 
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- ### Metrics (QAG)
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- | Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
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- |:--------|:-----|--------------------------------:|---------------------------------:|-----:|
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- | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) | default | 0.885 | 0.625 | [link](https://huggingface.co/lmqg/mt5-small-frquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json) |
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-
 
 
 
 
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@@ -162,7 +153,6 @@ The full configuration can be found at [fine-tuning config file](https://hugging
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  ## Citation
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  ```
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-
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  @inproceedings{ushio-etal-2022-generative,
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  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
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  author = "Ushio, Asahi and
 
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  metrics:
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  - name: BLEU4
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  type: bleu4
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+ value: 8.55
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  - name: ROUGE-L
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  type: rouge-l
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+ value: 28.56
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  - name: METEOR
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  type: meteor
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+ value: 17.51
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  - name: BERTScore
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  type: bertscore
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+ value: 80.71
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  - name: MoverScore
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  type: moverscore
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+ value: 56.5
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+ - name: QAAlignedF1Score (BERTScore) [Gold Answer]
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+ type: qa_aligned_f1_score_bertscore_gold_answer
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+ value: 88.52
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+ - name: QAAlignedRecall (BERTScore) [Gold Answer]
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+ type: qa_aligned_recall_bertscore_gold_answer
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+ value: 88.51
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+ - name: QAAlignedPrecision (BERTScore) [Gold Answer]
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+ type: qa_aligned_precision_bertscore_gold_answer
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+ value: 88.53
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+ - name: QAAlignedF1Score (MoverScore) [Gold Answer]
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+ type: qa_aligned_f1_score_moverscore_gold_answer
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+ value: 62.46
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+ - name: QAAlignedRecall (MoverScore) [Gold Answer]
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+ type: qa_aligned_recall_moverscore_gold_answer
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+ value: 62.45
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+ - name: QAAlignedPrecision (MoverScore) [Gold Answer]
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+ type: qa_aligned_precision_moverscore_gold_answer
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+ value: 62.46
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  ---
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69
  # Model Card of `lmqg/mt5-small-frquad`
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+ This model is fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) for question generation task on the [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
 
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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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  ### Usage
82
  - With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
83
  ```python
 
84
  from lmqg import TransformersQG
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+
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  # initialize model
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+ model = TransformersQG(language="fr", model="lmqg/mt5-small-frquad")
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+
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  # model prediction
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+ questions = model.generate_q(list_context="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.", list_answer="le Suprême Berger")
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92
  ```
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  - With `transformers`
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  ```python
 
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  from transformers import pipeline
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+
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+ pipe = pipeline("text2text-generation", "lmqg/mt5-small-frquad")
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+ output = pipe("Créateur » (Maker), lui aussi au singulier, « <hl> le Suprême Berger <hl> » (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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101
  ```
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+ ## Evaluation
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+ - ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/lmqg/mt5-small-frquad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json)
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+ | | Score | Type | Dataset |
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+ |:-----------|--------:|:--------|:-----------------------------------------------------------------|
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+ | BERTScore | 80.71 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | Bleu_1 | 29.26 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | Bleu_2 | 17.56 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | Bleu_3 | 12.03 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | Bleu_4 | 8.55 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | METEOR | 17.51 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | MoverScore | 56.5 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | ROUGE_L | 28.56 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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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-frquad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_frquad.default.json)
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+ | | Score | Type | Dataset |
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+ |:--------------------------------|--------:|:--------|:-----------------------------------------------------------------|
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+ | QAAlignedF1Score (BERTScore) | 88.52 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | QAAlignedF1Score (MoverScore) | 62.46 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | QAAlignedPrecision (BERTScore) | 88.53 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | QAAlignedPrecision (MoverScore) | 62.46 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | QAAlignedRecall (BERTScore) | 88.51 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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+ | QAAlignedRecall (MoverScore) | 62.45 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
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  ## Citation
155
  ```
 
156
  @inproceedings{ushio-etal-2022-generative,
157
  title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
158
  author = "Ushio, Asahi and