Model Card of lmqg/mt5-base-frquad-qg
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg_frquad (dataset_name: default) via lmqg
.
Overview
- Language model: google/mt5-base
- 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-base-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-base-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 | 77.81 | default | lmqg/qg_frquad |
Bleu_1 | 25.06 | default | lmqg/qg_frquad |
Bleu_2 | 13.73 | default | lmqg/qg_frquad |
Bleu_3 | 8.93 | default | lmqg/qg_frquad |
Bleu_4 | 6.14 | default | lmqg/qg_frquad |
METEOR | 15.55 | default | lmqg/qg_frquad |
MoverScore | 54.58 | default | lmqg/qg_frquad |
ROUGE_L | 25.88 | 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) | 86.41 | default | lmqg/qg_frquad |
QAAlignedF1Score (MoverScore) | 60.19 | default | lmqg/qg_frquad |
QAAlignedPrecision (BERTScore) | 86.42 | default | lmqg/qg_frquad |
QAAlignedPrecision (MoverScore) | 60.19 | default | lmqg/qg_frquad |
QAAlignedRecall (BERTScore) | 86.4 | default | lmqg/qg_frquad |
QAAlignedRecall (MoverScore) | 60.18 | default | lmqg/qg_frquad |
- Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by
lmqg/mt5-base-frquad-ae
. raw metric file
Score | Type | Dataset | |
---|---|---|---|
QAAlignedF1Score (BERTScore) | 68.59 | default | lmqg/qg_frquad |
QAAlignedF1Score (MoverScore) | 47.87 | default | lmqg/qg_frquad |
QAAlignedPrecision (BERTScore) | 67.59 | default | lmqg/qg_frquad |
QAAlignedPrecision (MoverScore) | 47.42 | default | lmqg/qg_frquad |
QAAlignedRecall (BERTScore) | 69.69 | default | lmqg/qg_frquad |
QAAlignedRecall (MoverScore) | 48.36 | 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-base
- max_length: 512
- max_length_output: 32
- epoch: 24
- batch: 4
- lr: 0.0001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- 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",
}
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Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_frquadself-reported6.140
- ROUGE-L (Question Generation) on lmqg/qg_frquadself-reported25.880
- METEOR (Question Generation) on lmqg/qg_frquadself-reported15.550
- BERTScore (Question Generation) on lmqg/qg_frquadself-reported77.810
- MoverScore (Question Generation) on lmqg/qg_frquadself-reported54.580
- QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_frquadself-reported86.410
- QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_frquadself-reported86.400
- QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_frquadself-reported86.420
- QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_frquadself-reported60.190
- QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_frquadself-reported60.180