This model is fine-tuned version of facebook/mbart-large-cc25 for question generation and answer extraction jointly on the lmqg/qg_esquad (dataset_name: default) via lmqg.

Overview

Language model: facebook/mbart-large-cc25
Language: es
Training data: lmqg/qg_esquad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: https://arxiv.org/abs/2210.03992

Usage

With lmqg

python

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="es", model="lmqg/mbart-large-cc25-esquad-qg-ae")

# model prediction
question_answer_pairs = model.generate_qa("a noviembre , que es también la estación lluviosa.")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-esquad-qg-ae")

# answer extraction
answer = pipe("generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")

# question generation
question = pipe("extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos de recaudación de fondos se llevan a cabo cada año en ciudades como Birmingham, Londres, Toronto y Minneapolis, donde los académicos y profesionales somalíes dan conferencias y responden preguntas de la audiencia. <hl> El propósito de estos eventos es recaudar dinero para nuevas escuelas o universidades en Somalia, para ayudar a los somalíes que han sufrido como consecuencia de inundaciones y / o sequías, o para reunir fondos para la creación de nuevas mezquitas como.")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	79.36	default	lmqg/qg_esquad
Bleu_1	22.05	default	lmqg/qg_esquad
Bleu_2	14.55	default	lmqg/qg_esquad
Bleu_3	10.34	default	lmqg/qg_esquad
Bleu_4	7.61	default	lmqg/qg_esquad
METEOR	19.58	default	lmqg/qg_esquad
MoverScore	56.05	default	lmqg/qg_esquad
ROUGE_L	20.95	default	lmqg/qg_esquad

Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	81.13	default	lmqg/qg_esquad
QAAlignedF1Score (MoverScore)	54.86	default	lmqg/qg_esquad
QAAlignedPrecision (BERTScore)	77.75	default	lmqg/qg_esquad
QAAlignedPrecision (MoverScore)	52.82	default	lmqg/qg_esquad
QAAlignedRecall (BERTScore)	84.91	default	lmqg/qg_esquad
QAAlignedRecall (MoverScore)	57.16	default	lmqg/qg_esquad

Metric (Answer Extraction): raw metric file

	Score	Type	Dataset
AnswerExactMatch	52.81	default	lmqg/qg_esquad
AnswerF1Score	70.95	default	lmqg/qg_esquad
BERTScore	86.7	default	lmqg/qg_esquad
Bleu_1	32.77	default	lmqg/qg_esquad
Bleu_2	28.12	default	lmqg/qg_esquad
Bleu_3	24.52	default	lmqg/qg_esquad
Bleu_4	21.5	default	lmqg/qg_esquad
METEOR	40.42	default	lmqg/qg_esquad
MoverScore	77.96	default	lmqg/qg_esquad
ROUGE_L	46.66	default	lmqg/qg_esquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qg_esquad
dataset_name: default
input_types: ['paragraph_answer', 'paragraph_sentence']
output_types: ['question', 'answer']
prefix_types: ['qg', 'ae']
model: facebook/mbart-large-cc25
max_length: 512
max_length_output: 32
epoch: 5
batch: 2
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 32
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-esquad-qg-ae

Model Card of lmqg/mbart-large-cc25-esquad-qg-ae

Overview

Usage

Evaluation

Training hyperparameters

Citation

research-backup/
mbart-large-cc25-esquad-qg-ae

Model Card of `lmqg/mbart-large-cc25-esquad-qg-ae`