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research-backup/
mbart-large-cc25-esquad-qag

Name: mbart-large-cc25-esquad-qag
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questions and answers generation

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Model Card of `lmqg/mbart-large-cc25-esquad-qag`

This model is fine-tuned version of facebook/mbart-large-cc25 for question & answer pair generation task on the lmqg/qag_esquad (dataset_name: default) via lmqg.

Overview

Language model: facebook/mbart-large-cc25
Language: es
Training data: lmqg/qag_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-qag")

# 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-qag")
output = pipe("del Ministerio de Desarrollo Urbano , Gobierno de la India.")

Evaluation

Metric (Question & Answer Generation): raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	78.8	default	lmqg/qag_esquad
QAAlignedF1Score (MoverScore)	54	default	lmqg/qag_esquad
QAAlignedPrecision (BERTScore)	76.59	default	lmqg/qag_esquad
QAAlignedPrecision (MoverScore)	52.57	default	lmqg/qag_esquad
QAAlignedRecall (BERTScore)	81.21	default	lmqg/qag_esquad
QAAlignedRecall (MoverScore)	55.63	default	lmqg/qag_esquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

dataset_path: lmqg/qag_esquad
dataset_name: default
input_types: ['paragraph']
output_types: ['questions_answers']
prefix_types: None
model: facebook/mbart-large-cc25
max_length: 512
max_length_output: 256
epoch: 6
batch: 8
lr: 0.0001
fp16: False
random_seed: 1
gradient_accumulation_steps: 8
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/mbart-large-cc25-esquad-qag

Model Card of lmqg/mbart-large-cc25-esquad-qag