from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="en", model="lmqg/mbart-large-cc25-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/mbart-large-cc25-squad-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	90.36	default	lmqg/qg_squad
Bleu_1	56	default	lmqg/qg_squad
Bleu_2	39.41	default	lmqg/qg_squad
Bleu_3	29.76	default	lmqg/qg_squad
Bleu_4	23.03	default	lmqg/qg_squad
METEOR	25.1	default	lmqg/qg_squad
MoverScore	63.63	default	lmqg/qg_squad
ROUGE_L	50.58	default	lmqg/qg_squad

Metrics (Question Generation, Out-of-Domain)

Dataset	Type	BERTScore	Bleu_4	METEOR	MoverScore	ROUGE_L	Link
lmqg/qg_dequad	default	11.05	0.0	1.05	44.94	3.4	link
lmqg/qg_esquad	default	60.73	0.57	5.27	48.76	18.99	link
lmqg/qg_frquad	default	16.47	0.02	1.55	45.35	5.13	link
lmqg/qg_itquad	default	41.46	0.48	3.84	47.28	13.25	link
lmqg/qg_jaquad	default	19.89	0.06	1.74	45.51	6.11	link
lmqg/qg_koquad	default	31.67	0.38	3.06	46.59	10.34	link
lmqg/qg_ruquad	default	26.19	0.18	2.65	46.09	8.34	link

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: None
model: facebook/mbart-large-cc25
max_length: 512
max_length_output: 32
epoch: 6
batch: 32
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 2
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/mbart-large-cc25-squad-qg

Model Card of lmqg/mbart-large-cc25-squad-qg

Overview

Usage

Evaluation

Training hyperparameters

Citation

research-backup/
mbart-large-cc25-squad-qg

Model Card of `lmqg/mbart-large-cc25-squad-qg`