Model Card of vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg
This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-es-5000 for question generation task on the lmqg/qg_esquad (dataset_name: default) via lmqg
.
Overview
- Language model: vocabtrimmer/mt5-small-trimmed-es-5000
- Language: es
- Training data: lmqg/qg_esquad (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="es", model="vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg")
# model prediction
questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg")
output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")
Evaluation
- Metric (Question Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
BERTScore | 84.07 | default | lmqg/qg_esquad |
Bleu_1 | 25.67 | default | lmqg/qg_esquad |
Bleu_2 | 17.4 | default | lmqg/qg_esquad |
Bleu_3 | 12.59 | default | lmqg/qg_esquad |
Bleu_4 | 9.41 | default | lmqg/qg_esquad |
METEOR | 21.88 | default | lmqg/qg_esquad |
MoverScore | 58.84 | default | lmqg/qg_esquad |
ROUGE_L | 23.51 | default | lmqg/qg_esquad |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_esquad
- dataset_name: default
- input_types: paragraph_answer
- output_types: question
- prefix_types: None
- model: vocabtrimmer/mt5-small-trimmed-es-5000
- max_length: 512
- max_length_output: 32
- epoch: 12
- 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.
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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Dataset used to train vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_esquadself-reported9.410
- ROUGE-L (Question Generation) on lmqg/qg_esquadself-reported23.510
- METEOR (Question Generation) on lmqg/qg_esquadself-reported21.880
- BERTScore (Question Generation) on lmqg/qg_esquadself-reported84.070
- MoverScore (Question Generation) on lmqg/qg_esquadself-reported58.840