metadata
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: lmqg/mt5-small-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: 8.55
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 28.56
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 17.51
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 80.71
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 56.5
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 88.52
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
Answer)) [Gold Answer]
type: >-
qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 88.51
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 88.53
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 62.46
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 62.45
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation (with
Gold Answer)) [Gold Answer]
type: >-
qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 62.46
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
value: 79.72
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
value: 82.06
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_precision_bertscore_question_answer_generation_gold_answer
value: 77.58
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
value: 53.94
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_recall_moverscore_question_answer_generation_gold_answer
value: 55.32
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_precision_moverscore_question_answer_generation_gold_answer
value: 52.7
Model Card of lmqg/mt5-small-frquad-qg
This model is fine-tuned version of google/mt5-small for question generation task on the lmqg/qg_frquad (dataset_name: default) via lmqg.
Overview
- Language model: google/mt5-small
- Language: fr
- Training data: lmqg/qg_frquad (default)
- Online Demo: https://autoqg.net/
- Repository: https://github.com/asahi417/lm-question-generation
- Paper: https://arxiv.org/abs/2210.03992
Usage
- With
lmqg
from lmqg import TransformersQG
# initialize model
model = TransformersQG(language="fr", model="lmqg/mt5-small-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
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/mt5-small-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
| Score | Type | Dataset | |
|---|---|---|---|
| BERTScore | 80.71 | default | lmqg/qg_frquad |
| Bleu_1 | 29.26 | default | lmqg/qg_frquad |
| Bleu_2 | 17.56 | default | lmqg/qg_frquad |
| Bleu_3 | 12.03 | default | lmqg/qg_frquad |
| Bleu_4 | 8.55 | default | lmqg/qg_frquad |
| METEOR | 17.51 | default | lmqg/qg_frquad |
| MoverScore | 56.5 | default | lmqg/qg_frquad |
| ROUGE_L | 28.56 | default | lmqg/qg_frquad |
- Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| QAAlignedF1Score (BERTScore) | 88.52 | default | lmqg/qg_frquad |
| QAAlignedF1Score (MoverScore) | 62.46 | default | lmqg/qg_frquad |
| QAAlignedPrecision (BERTScore) | 88.53 | default | lmqg/qg_frquad |
| QAAlignedPrecision (MoverScore) | 62.46 | default | lmqg/qg_frquad |
| QAAlignedRecall (BERTScore) | 88.51 | default | lmqg/qg_frquad |
| QAAlignedRecall (MoverScore) | 62.45 | default | lmqg/qg_frquad |
- Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by
lmqg/mt5-small-frquad-ae. raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| QAAlignedF1Score (BERTScore) | 79.72 | default | lmqg/qg_frquad |
| QAAlignedF1Score (MoverScore) | 53.94 | default | lmqg/qg_frquad |
| QAAlignedPrecision (BERTScore) | 77.58 | default | lmqg/qg_frquad |
| QAAlignedPrecision (MoverScore) | 52.7 | default | lmqg/qg_frquad |
| QAAlignedRecall (BERTScore) | 82.06 | default | lmqg/qg_frquad |
| QAAlignedRecall (MoverScore) | 55.32 | default | 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: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 14
- batch: 64
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 1
- label_smoothing: 0.15
The full configuration can be found at fine-tuning config file.
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",
}