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vocabtrimmer/
mt5-small-trimmed-es-60000-esquad-qg

Name: mt5-small-trimmed-es-60000-esquad-qg
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Model Card of `vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg`

This model is fine-tuned version of ckpts/mt5-small-trimmed-es-60000 for question generation task on the lmqg/qg_esquad (dataset_name: default) via lmqg.

Overview

Language model: ckpts/mt5-small-trimmed-es-60000
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="vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg")

# model prediction
questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")

With transformers

python

from transformers import pipeline

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg")
output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	84.4	default	lmqg/qg_esquad
Bleu_1	26.37	default	lmqg/qg_esquad
Bleu_2	18.03	default	lmqg/qg_esquad
Bleu_3	13.1	default	lmqg/qg_esquad
Bleu_4	9.79	default	lmqg/qg_esquad
METEOR	22.64	default	lmqg/qg_esquad
MoverScore	59.16	default	lmqg/qg_esquad
ROUGE_L	24.63	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
output_types: question
prefix_types: None
model: ckpts/mt5-small-trimmed-es-60000
max_length: 512
max_length_output: 32
epoch: 13
batch: 16
lr: 0.001
fp16: False
random_seed: 1
gradient_accumulation_steps: 4
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",
}

vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg

Model Card of vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg