This model is fine-tuned version of t5-large for question generation and answer extraction jointly on the lmqg/qg_squad (dataset_name: default) via lmqg.

Overview

Language model: t5-large
Language: en
Training data: lmqg/qg_squad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: https://arxiv.org/abs/2210.03992

Usage

With lmqg

python

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/t5-large-squad-qg-ae")

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

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/t5-large-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

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.69	default	lmqg/qg_squad
Bleu_1	59.93	default	lmqg/qg_squad
Bleu_2	43.98	default	lmqg/qg_squad
Bleu_3	34.19	default	lmqg/qg_squad
Bleu_4	27.2	default	lmqg/qg_squad
METEOR	27.81	default	lmqg/qg_squad
MoverScore	65.29	default	lmqg/qg_squad
ROUGE_L	54.23	default	lmqg/qg_squad

Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	92.87	default	lmqg/qg_squad
QAAlignedF1Score (MoverScore)	64.67	default	lmqg/qg_squad
QAAlignedPrecision (BERTScore)	92.72	default	lmqg/qg_squad
QAAlignedPrecision (MoverScore)	64.82	default	lmqg/qg_squad
QAAlignedRecall (BERTScore)	93.04	default	lmqg/qg_squad
QAAlignedRecall (MoverScore)	64.63	default	lmqg/qg_squad

Metric (Answer Extraction): raw metric file

	Score	Type	Dataset
AnswerExactMatch	59.26	default	lmqg/qg_squad
AnswerF1Score	70.3	default	lmqg/qg_squad
BERTScore	91.63	default	lmqg/qg_squad
Bleu_1	60.87	default	lmqg/qg_squad
Bleu_2	56.96	default	lmqg/qg_squad
Bleu_3	53.12	default	lmqg/qg_squad
Bleu_4	49.73	default	lmqg/qg_squad
METEOR	44.46	default	lmqg/qg_squad
MoverScore	82.48	default	lmqg/qg_squad
ROUGE_L	69.82	default	lmqg/qg_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_squad
dataset_name: default
input_types: ['paragraph_answer', 'paragraph_sentence']
output_types: ['question', 'answer']
prefix_types: ['qg', 'ae']
model: t5-large
max_length: 512
max_length_output: 32
epoch: 3
batch: 16
lr: 0.0001
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",
}

lmqg/t5-large-squad-qg-ae

Model Card of lmqg/t5-large-squad-qg-ae

Overview

Usage

Evaluation

Training hyperparameters

Citation

lmqg/
t5-large-squad-qg-ae

Model Card of `lmqg/t5-large-squad-qg-ae`