question generation

Model Card of lmqg/bart-large-subjqa-electronics-qg

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/bart-large-subjqa-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")

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/bart-large-subjqa-electronics-qg")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Score Type Dataset
BERTScore 93.51 electronics lmqg/qg_subjqa
Bleu_1 28.11 electronics lmqg/qg_subjqa
Bleu_2 19.75 electronics lmqg/qg_subjqa
Bleu_3 9.66 electronics lmqg/qg_subjqa
Bleu_4 5.18 electronics lmqg/qg_subjqa
METEOR 25.17 electronics lmqg/qg_subjqa
MoverScore 65.68 electronics lmqg/qg_subjqa
ROUGE_L 28.87 electronics lmqg/qg_subjqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

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",
}