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Model Card of lmqg/mt5-base-ruquad-qg

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

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="ru", model="lmqg/mt5-base-ruquad-qg")

# model prediction
questions = model.generate_q(list_context="Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, в мае 1860 года провёл серию опытов.", list_answer="в мае 1860 года")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-base-ruquad-qg")
output = pipe("Нелишним будет отметить, что, развивая это направление, Д. И. Менделеев, поначалу априорно выдвинув идею о температуре, при которой высота мениска будет нулевой, <hl> в мае 1860 года <hl> провёл серию опытов.")

Evaluation

Score Type Dataset
BERTScore 85.82 default lmqg/qg_ruquad
Bleu_1 33.04 default lmqg/qg_ruquad
Bleu_2 26.31 default lmqg/qg_ruquad
Bleu_3 21.42 default lmqg/qg_ruquad
Bleu_4 17.63 default lmqg/qg_ruquad
METEOR 28.48 default lmqg/qg_ruquad
MoverScore 64.56 default lmqg/qg_ruquad
ROUGE_L 33.02 default lmqg/qg_ruquad
  • Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 91.1 default lmqg/qg_ruquad
QAAlignedF1Score (MoverScore) 70.06 default lmqg/qg_ruquad
QAAlignedPrecision (BERTScore) 91.11 default lmqg/qg_ruquad
QAAlignedPrecision (MoverScore) 70.07 default lmqg/qg_ruquad
QAAlignedRecall (BERTScore) 91.09 default lmqg/qg_ruquad
QAAlignedRecall (MoverScore) 70.04 default lmqg/qg_ruquad
Score Type Dataset
QAAlignedF1Score (BERTScore) 77.03 default lmqg/qg_ruquad
QAAlignedF1Score (MoverScore) 55.61 default lmqg/qg_ruquad
QAAlignedPrecision (BERTScore) 73.44 default lmqg/qg_ruquad
QAAlignedPrecision (MoverScore) 53.27 default lmqg/qg_ruquad
QAAlignedRecall (BERTScore) 81.17 default lmqg/qg_ruquad
QAAlignedRecall (MoverScore) 58.39 default lmqg/qg_ruquad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_ruquad
  • dataset_name: default
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: None
  • model: google/mt5-base
  • max_length: 512
  • max_length_output: 32
  • epoch: 16
  • batch: 4
  • lr: 0.0005
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 16
  • 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 lmqg/mt5-base-ruquad-qg

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_ruquad
    self-reported
    17.630
  • ROUGE-L (Question Generation) on lmqg/qg_ruquad
    self-reported
    33.020
  • METEOR (Question Generation) on lmqg/qg_ruquad
    self-reported
    28.480
  • BERTScore (Question Generation) on lmqg/qg_ruquad
    self-reported
    85.820
  • MoverScore (Question Generation) on lmqg/qg_ruquad
    self-reported
    64.560
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_ruquad
    self-reported
    91.100
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_ruquad
    self-reported
    91.090
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_ruquad
    self-reported
    91.110
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_ruquad
    self-reported
    70.060
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_ruquad
    self-reported
    70.040