Model Card of research-backup/t5-large-subjqa-vanilla-electronics-qg

This model is fine-tuned version of t5-large for question generation task on the lmqg/qg_subjqa (dataset_name: electronics) via lmqg.

Overview

• Language model: t5-large
• Language: en
• Training data: lmqg/qg_subjqa (electronics)
• 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="research-backup/t5-large-subjqa-vanilla-electronics-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", "research-backup/t5-large-subjqa-vanilla-electronics-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	81.61	electronics	lmqg/qg_subjqa
Bleu_1	5.2	electronics	lmqg/qg_subjqa
Bleu_2	1.22	electronics	lmqg/qg_subjqa
Bleu_3	0.32	electronics	lmqg/qg_subjqa
Bleu_4	0	electronics	lmqg/qg_subjqa
METEOR	6.49	electronics	lmqg/qg_subjqa
MoverScore	50.46	electronics	lmqg/qg_subjqa
ROUGE_L	8.01	electronics	lmqg/qg_subjqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

• dataset_path: lmqg/qg_subjqa
• dataset_name: electronics
• input_types: ['paragraph_answer']
• output_types: ['question']
• prefix_types: ['qg']
• model: t5-large
• max_length: 512
• max_length_output: 32
• epoch: 1
• batch: 16
• lr: 1e-05
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 8
• 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",
}