from lmqg import TransformersQG

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

# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/flan-t5-small-squad-qg")
output = pipe("generate question: <hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	90.23	default	lmqg/qg_squad
Bleu_1	56.66	default	lmqg/qg_squad
Bleu_2	40.4	default	lmqg/qg_squad
Bleu_3	30.95	default	lmqg/qg_squad
Bleu_4	24.34	default	lmqg/qg_squad
METEOR	25.58	default	lmqg/qg_squad
MoverScore	63.77	default	lmqg/qg_squad
ROUGE_L	51.26	default	lmqg/qg_squad

Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by lmqg/flan-t5-small-squad-ae. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	92.34	default	lmqg/qg_squad
QAAlignedF1Score (MoverScore)	63.8	default	lmqg/qg_squad
QAAlignedPrecision (BERTScore)	92.13	default	lmqg/qg_squad
QAAlignedPrecision (MoverScore)	63.89	default	lmqg/qg_squad
QAAlignedRecall (BERTScore)	92.58	default	lmqg/qg_squad
QAAlignedRecall (MoverScore)	63.8	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
output_types: question
prefix_types: ['qg']
model: google/flan-t5-small
max_length: 512
max_length_output: 32
epoch: 7
batch: 64
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
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/flan-t5-small-squad-qg

Model Card of lmqg/flan-t5-small-squad-qg

Overview

Usage

Evaluation

Training hyperparameters

Citation

lmqg/
flan-t5-small-squad-qg

Model Card of `lmqg/flan-t5-small-squad-qg`