from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="research-backup/t5-small-subjqa-vanilla-electronics-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", "research-backup/t5-small-subjqa-vanilla-electronics-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	52.85	electronics	lmqg/qg_subjqa
Bleu_1	2.89	electronics	lmqg/qg_subjqa
Bleu_2	0.7	electronics	lmqg/qg_subjqa
Bleu_3	0	electronics	lmqg/qg_subjqa
Bleu_4	0	electronics	lmqg/qg_subjqa
METEOR	2.64	electronics	lmqg/qg_subjqa
MoverScore	49.57	electronics	lmqg/qg_subjqa
ROUGE_L	2.38	electronics	lmqg/qg_subjqa

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_subjqa
dataset_name: electronics
input_types: ['paragraph_answer']
output_types: ['question']
prefix_types: ['qg']
model: t5-small
max_length: 512
max_length_output: 32
epoch: 1
batch: 32
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",
}

research-backup/t5-small-subjqa-vanilla-electronics-qg

Model Card of research-backup/t5-small-subjqa-vanilla-electronics-qg

Overview

Usage

Evaluation

Training hyperparameters

Citation

research-backup/
t5-small-subjqa-vanilla-electronics-qg

Model Card of `research-backup/t5-small-subjqa-vanilla-electronics-qg`