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README.md

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+
+---
+license: cc-by-4.0
+metrics:
+- bleu4
+- meteor
+- rouge-l
+- bertscore
+- moverscore
+language: fr
+datasets:
+- lmqg/qg_frquad
+pipeline_tag: text2text-generation
+tags:
+- question generation
+widget:
+- text: "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."
+  example_title: "Question Generation Example 1" 
+- text: "Ce black dog peut être lié à des évènements traumatisants issus du monde extérieur, tels que son renvoi de l'Amirauté après la catastrophe des Dardanelles, lors de la <hl> Grande Guerre <hl> de 14-18, ou son rejet par l'électorat en juillet 1945."
+  example_title: "Question Generation Example 2" 
+- text: "contre <hl> Normie Smith <hl> et 15 000 dollars le 28 novembre 1938."
+  example_title: "Question Generation Example 3" 
+model-index:
+- name: vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg
+  results:
+  - task:
+      name: Text2text Generation
+      type: text2text-generation
+    dataset:
+      name: lmqg/qg_frquad
+      type: default
+      args: default
+    metrics:
+    - name: BLEU4 (Question Generation)
+      type: bleu4_question_generation
+      value: 7.2
+    - name: ROUGE-L (Question Generation)
+      type: rouge_l_question_generation
+      value: 26.89
+    - name: METEOR (Question Generation)
+      type: meteor_question_generation
+      value: 16.13
+    - name: BERTScore (Question Generation)
+      type: bertscore_question_generation
+      value: 79.05
+    - name: MoverScore (Question Generation)
+      type: moverscore_question_generation
+      value: 55.28
+---
+
+# Model Card of `vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg`
+This model is fine-tuned version of [vocabtrimmer/mt5-small-trimmed-fr-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-5000) 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).
+
+
+### Overview
+- **Language model:** [vocabtrimmer/mt5-small-trimmed-fr-5000](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-5000)   
+- **Language:** fr  
+- **Training data:** [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) (default)
+- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
+- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
+- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
+
+### Usage
+- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
+```python
+from lmqg import TransformersQG
+
+# initialize model
+model = TransformersQG(language="fr", model="vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg")
+
+# model prediction
+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")
+
+```
+
+- With `transformers`
+```python
+from transformers import pipeline
+
+pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg")
+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.")
+
+```
+
+## Evaluation
+
+
+- ***Metric (Question Generation)***: [raw metric file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json) 
+
+|            |   Score | Type    | Dataset                                                          |
+|:-----------|--------:|:--------|:-----------------------------------------------------------------|
+| BERTScore  |   79.05 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_1     |   27.12 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_2     |   15.65 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_3     |   10.42 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| Bleu_4     |    7.2  | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| METEOR     |   16.13 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| MoverScore |   55.28 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+| ROUGE_L    |   26.89 | default | [lmqg/qg_frquad](https://huggingface.co/datasets/lmqg/qg_frquad) |
+
+
+
+## Training hyperparameters
+
+The following hyperparameters were used during fine-tuning:
+ - dataset_path: lmqg/qg_frquad
+ - dataset_name: default
+ - input_types: paragraph_answer
+ - output_types: question
+ - prefix_types: None
+ - model: vocabtrimmer/mt5-small-trimmed-fr-5000
+ - max_length: 512
+ - max_length_output: 32
+ - epoch: 15
+ - batch: 16
+ - lr: 0.001
+ - fp16: False
+ - random_seed: 1
+ - gradient_accumulation_steps: 4
+ - label_smoothing: 0.15
+
+The full configuration can be found at [fine-tuning config file](https://huggingface.co/vocabtrimmer/mt5-small-trimmed-fr-5000-frquad-qg/raw/main/trainer_config.json).
+
+## Citation
+```
+@inproceedings{ushio-etal-2022-generative,
+    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
+    author = "Ushio, Asahi  and
+        Alva-Manchego, Fernando  and
+        Camacho-Collados, Jose",
+    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
+    month = dec,
+    year = "2022",
+    address = "Abu Dhabi, U.A.E.",
+    publisher = "Association for Computational Linguistics",
+}
+
+```

eval/metric.first.answer.paragraph_answer.question.lmqg_qg_frquad.default.json

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+{"validation": {"Bleu_1": 0.27605071205278814, "Bleu_2": 0.1554736073409939, "Bleu_3": 0.10314671593336713, "Bleu_4": 0.0719757772784161}, "test": {"Bleu_1": 0.27008889150526083, "Bleu_2": 0.1556359539667937, "Bleu_3": 0.10341199891323995, "Bleu_4": 0.07147297765113227}}

eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_frquad.default.json

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+{"validation": {"Bleu_1": 0.27802050824787405, "Bleu_2": 0.15682524320053076, "Bleu_3": 0.10413373849849157, "Bleu_4": 0.07259841143576594, "METEOR": 0.15973316169804913, "ROUGE_L": 0.28727166610889227, "BERTScore": 0.7836307362871182, "MoverScore": 0.5507881595138353}, "test": {"Bleu_1": 0.2711581629557644, "Bleu_2": 0.15654240373107764, "Bleu_3": 0.1041536185897896, "Bleu_4": 0.07204421672840086, "METEOR": 0.1613051764768341, "ROUGE_L": 0.2689296170783525, "BERTScore": 0.7904696900674826, "MoverScore": 0.552838619115938}}