from lmqg import TransformersQG

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

# 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-qag")
output = pipe("generate question and answer: Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	93.45	default	lmqg/qag_squad
QAAlignedF1Score (MoverScore)	66.05	default	lmqg/qag_squad
QAAlignedPrecision (BERTScore)	93.34	default	lmqg/qag_squad
QAAlignedPrecision (MoverScore)	66.34	default	lmqg/qag_squad
QAAlignedRecall (BERTScore)	93.57	default	lmqg/qag_squad
QAAlignedRecall (MoverScore)	65.84	default	lmqg/qag_squad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qag_squad
dataset_name: default
input_types: ['paragraph']
output_types: ['questions_answers']
prefix_types: ['qag']
model: t5-large
max_length: 512
max_length_output: 256
epoch: 12
batch: 8
lr: 0.0001
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",
}

lmqg/t5-large-squad-qag

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

Overview

Usage

Evaluation

Training hyperparameters

Citation

lmqg/
t5-large-squad-qag

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