Model Card of lmqg/t5-small-subjqa-grocery-qg
This model is fine-tuned version of lmqg/t5-small-squad for question generation task on the lmqg/qg_subjqa (dataset_name: grocery) via lmqg
.
Overview
- Language model: lmqg/t5-small-squad
- Language: en
- Training data: lmqg/qg_subjqa (grocery)
- 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="en", model="lmqg/t5-small-subjqa-grocery-qg")
# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/t5-small-subjqa-grocery-qg")
output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
Evaluation
- Metric (Question Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
BERTScore | 93.24 | grocery | lmqg/qg_subjqa |
Bleu_1 | 20.56 | grocery | lmqg/qg_subjqa |
Bleu_2 | 13.02 | grocery | lmqg/qg_subjqa |
Bleu_3 | 5.11 | grocery | lmqg/qg_subjqa |
Bleu_4 | 0 | grocery | lmqg/qg_subjqa |
METEOR | 23.31 | grocery | lmqg/qg_subjqa |
MoverScore | 65.64 | grocery | lmqg/qg_subjqa |
ROUGE_L | 22.17 | grocery | lmqg/qg_subjqa |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_subjqa
- dataset_name: grocery
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: ['qg']
- model: lmqg/t5-small-squad
- max_length: 512
- max_length_output: 32
- epoch: 3
- batch: 32
- lr: 5e-05
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- 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 research-backup/t5-small-subjqa-grocery-qg
Evaluation results
- BLEU4 (Question Generation) on lmqg/qg_subjqaself-reported0.000
- ROUGE-L (Question Generation) on lmqg/qg_subjqaself-reported22.170
- METEOR (Question Generation) on lmqg/qg_subjqaself-reported23.310
- BERTScore (Question Generation) on lmqg/qg_subjqaself-reported93.240
- MoverScore (Question Generation) on lmqg/qg_subjqaself-reported65.640