from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/bart-base-subjqa-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", "lmqg/bart-base-subjqa-electronics-qg")
output = pipe("<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	93.76	electronics	lmqg/qg_subjqa
Bleu_1	27.92	electronics	lmqg/qg_subjqa
Bleu_2	20	electronics	lmqg/qg_subjqa
Bleu_3	8.41	electronics	lmqg/qg_subjqa
Bleu_4	3.83	electronics	lmqg/qg_subjqa
METEOR	25.08	electronics	lmqg/qg_subjqa
MoverScore	66	electronics	lmqg/qg_subjqa
ROUGE_L	29.41	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: None
model: lmqg/bart-base-squad
max_length: 512
max_length_output: 32
epoch: 3
batch: 32
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 2
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",
}

research-backup/bart-base-subjqa-electronics-qg

Model Card of lmqg/bart-base-subjqa-electronics-qg

Overview

Usage

Evaluation

Training hyperparameters

Citation

research-backup/
bart-base-subjqa-electronics-qg

Model Card of `lmqg/bart-base-subjqa-electronics-qg`