question generation answer extraction

Model Card of lmqg/bart-base-squad-qg-ae

This model is fine-tuned version of facebook/bart-base for question generation and answer extraction jointly on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/bart-base-squad-qg-ae")

# model prediction
question_answer_pairs = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/bart-base-squad-qg-ae")

# answer extraction
answer = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

# question generation
question = pipe("extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.")

Evaluation

Score Type Dataset
BERTScore 90.65 default lmqg/qg_squad
Bleu_1 56.53 default lmqg/qg_squad
Bleu_2 40.97 default lmqg/qg_squad
Bleu_3 31.71 default lmqg/qg_squad
Bleu_4 25.07 default lmqg/qg_squad
METEOR 25.87 default lmqg/qg_squad
MoverScore 64.49 default lmqg/qg_squad
ROUGE_L 52.79 default lmqg/qg_squad
Score Type Dataset
QAAlignedF1Score (BERTScore) 93.45 default lmqg/qg_squad
QAAlignedF1Score (MoverScore) 64.47 default lmqg/qg_squad
QAAlignedPrecision (BERTScore) 92.78 default lmqg/qg_squad
QAAlignedPrecision (MoverScore) 63.55 default lmqg/qg_squad
QAAlignedRecall (BERTScore) 94.14 default lmqg/qg_squad
QAAlignedRecall (MoverScore) 65.49 default lmqg/qg_squad
Score Type Dataset
AnswerExactMatch 57.58 default lmqg/qg_squad
AnswerF1Score 69.14 default lmqg/qg_squad
BERTScore 91.86 default lmqg/qg_squad
Bleu_1 65.9 default lmqg/qg_squad
Bleu_2 63.06 default lmqg/qg_squad
Bleu_3 60.47 default lmqg/qg_squad
Bleu_4 58.31 default lmqg/qg_squad
METEOR 41.39 default lmqg/qg_squad
MoverScore 81.95 default lmqg/qg_squad
ROUGE_L 68.38 default lmqg/qg_squad

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",
}