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Model Card of vocabtrimmer/mt5-small-trimmed-it-30000-itquad-qg

This model is fine-tuned version of ckpts/mt5-small-trimmed-it-30000 for question generation task on the lmqg/qg_itquad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="it", model="vocabtrimmer/mt5-small-trimmed-it-30000-itquad-qg")

# model prediction
questions = model.generate_q(list_context="Dopo il 1971 , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.", list_answer="Dopo il 1971")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "vocabtrimmer/mt5-small-trimmed-it-30000-itquad-qg")
output = pipe("<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.")

Evaluation

Score Type Dataset
BERTScore 80.58 default lmqg/qg_itquad
Bleu_1 22.25 default lmqg/qg_itquad
Bleu_2 14.52 default lmqg/qg_itquad
Bleu_3 10.03 default lmqg/qg_itquad
Bleu_4 7.14 default lmqg/qg_itquad
METEOR 17.1 default lmqg/qg_itquad
MoverScore 56.53 default lmqg/qg_itquad
ROUGE_L 21.38 default lmqg/qg_itquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_itquad
  • dataset_name: default
  • input_types: paragraph_answer
  • output_types: question
  • prefix_types: None
  • model: ckpts/mt5-small-trimmed-it-30000
  • max_length: 512
  • max_length_output: 32
  • epoch: 15
  • 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",
}
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Dataset used to train vocabtrimmer/mt5-small-trimmed-it-30000-itquad-qg

Evaluation results