mt5-base-esquad-qag

Model Card of lmqg/mt5-base-esquad-qag

This model is fine-tuned version of google/mt5-base for question & answer pair generation task on the lmqg/qag_esquad (dataset_name: default) via lmqg.

Overview

• Language model: google/mt5-base
• 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

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="es", model="lmqg/mt5-base-esquad-qag")

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

• With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-base-esquad-qag")
output = pipe("del Ministerio de Desarrollo Urbano , Gobierno de la India.")

Evaluation

• Metric (Question & Answer Generation): raw metric file

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: google/mt5-base
• max_length: 512
• max_length_output: 256
• epoch: 13
• batch: 2
• lr: 0.0005
• fp16: False
• random_seed: 1
• gradient_accumulation_steps: 32
• 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",
}