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

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-esquad-qg")

# model prediction
questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")
  • With transformers
from transformers import pipeline

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

Evaluation

Score Type Dataset
BERTScore 84.19 default lmqg/qg_esquad
Bleu_1 25.92 default lmqg/qg_esquad
Bleu_2 17.66 default lmqg/qg_esquad
Bleu_3 12.76 default lmqg/qg_esquad
Bleu_4 9.52 default lmqg/qg_esquad
METEOR 22.26 default lmqg/qg_esquad
MoverScore 58.91 default lmqg/qg_esquad
ROUGE_L 24.24 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: vocabtrimmer/mt5-small-trimmed-es
  • max_length: 512
  • max_length_output: 32
  • epoch: 15
  • batch: 32
  • lr: 0.0005
  • 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",
}
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Dataset used to train vocabtrimmer/mt5-small-trimmed-es-esquad-qg

Evaluation results