from lmqg import TransformersQG

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

# model prediction
answers = model.generate_a("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-ae")
output = 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 (Answer Extraction): raw metric file

	Score	Type	Dataset
AnswerExactMatch	59.77	default	lmqg/qg_squad
AnswerF1Score	70.41	default	lmqg/qg_squad
BERTScore	91.91	default	lmqg/qg_squad
Bleu_1	65.48	default	lmqg/qg_squad
Bleu_2	62.11	default	lmqg/qg_squad
Bleu_3	58.71	default	lmqg/qg_squad
Bleu_4	55.66	default	lmqg/qg_squad
METEOR	43.06	default	lmqg/qg_squad
MoverScore	82.82	default	lmqg/qg_squad
ROUGE_L	69.67	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_sentence']
output_types: ['answer']
prefix_types: ['ae']
model: t5-large
max_length: 512
max_length_output: 32
epoch: 9
batch: 4
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 32
label_smoothing: 0.0

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-ae

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

Overview

Usage

Evaluation

Training hyperparameters

Citation

lmqg/
t5-large-squad-ae

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