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Model Card of lmqg/mt5-base-esquad-qg-ae

This model is fine-tuned version of google/mt5-base for question generation and answer extraction jointly on the lmqg/qg_esquad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

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

# 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-qg-ae")

# answer extraction
answer = pipe("generate question: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")

# question generation
question = pipe("extract answers: <hl> En la diáspora somalí, múltiples eventos islámicos de recaudación de fondos se llevan a cabo cada año en ciudades como Birmingham, Londres, Toronto y Minneapolis, donde los académicos y profesionales somalíes dan conferencias y responden preguntas de la audiencia. <hl> El propósito de estos eventos es recaudar dinero para nuevas escuelas o universidades en Somalia, para ayudar a los somalíes que han sufrido como consecuencia de inundaciones y / o sequías, o para reunir fondos para la creación de nuevas mezquitas como.")

Evaluation

Score Type Dataset
BERTScore 83.97 default lmqg/qg_esquad
Bleu_1 25.88 default lmqg/qg_esquad
Bleu_2 17.67 default lmqg/qg_esquad
Bleu_3 12.84 default lmqg/qg_esquad
Bleu_4 9.62 default lmqg/qg_esquad
METEOR 23.11 default lmqg/qg_esquad
MoverScore 59.15 default lmqg/qg_esquad
ROUGE_L 24.82 default lmqg/qg_esquad
Score Type Dataset
QAAlignedF1Score (BERTScore) 79.67 default lmqg/qg_esquad
QAAlignedF1Score (MoverScore) 54.82 default lmqg/qg_esquad
QAAlignedPrecision (BERTScore) 77.14 default lmqg/qg_esquad
QAAlignedPrecision (MoverScore) 53.27 default lmqg/qg_esquad
QAAlignedRecall (BERTScore) 82.44 default lmqg/qg_esquad
QAAlignedRecall (MoverScore) 56.56 default lmqg/qg_esquad
Score Type Dataset
AnswerExactMatch 57.98 default lmqg/qg_esquad
AnswerF1Score 75.33 default lmqg/qg_esquad
BERTScore 90.04 default lmqg/qg_esquad
Bleu_1 37.35 default lmqg/qg_esquad
Bleu_2 32.53 default lmqg/qg_esquad
Bleu_3 28.86 default lmqg/qg_esquad
Bleu_4 25.75 default lmqg/qg_esquad
METEOR 43.74 default lmqg/qg_esquad
MoverScore 80.94 default lmqg/qg_esquad
ROUGE_L 49.61 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', 'paragraph_sentence']
  • output_types: ['question', 'answer']
  • prefix_types: ['qg', 'ae']
  • model: google/mt5-base
  • max_length: 512
  • max_length_output: 32
  • epoch: 7
  • batch: 32
  • lr: 0.001
  • 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 lmqg/mt5-base-esquad-qg-ae

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_esquad
    self-reported
    9.620
  • ROUGE-L (Question Generation) on lmqg/qg_esquad
    self-reported
    24.820
  • METEOR (Question Generation) on lmqg/qg_esquad
    self-reported
    23.110
  • BERTScore (Question Generation) on lmqg/qg_esquad
    self-reported
    83.970
  • MoverScore (Question Generation) on lmqg/qg_esquad
    self-reported
    59.150
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_esquad
    self-reported
    79.670
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_esquad
    self-reported
    82.440
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_esquad
    self-reported
    77.140
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_esquad
    self-reported
    54.820
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_esquad
    self-reported
    56.560
  • QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) on lmqg/qg_esquad
    self-reported
    53.270
  • BLEU4 (Answer Extraction) on lmqg/qg_esquad
    self-reported
    25.750
  • ROUGE-L (Answer Extraction) on lmqg/qg_esquad
    self-reported
    49.610
  • METEOR (Answer Extraction) on lmqg/qg_esquad
    self-reported
    43.740
  • BERTScore (Answer Extraction) on lmqg/qg_esquad
    self-reported
    90.040
  • MoverScore (Answer Extraction) on lmqg/qg_esquad
    self-reported
    80.940
  • AnswerF1Score (Answer Extraction) on lmqg/qg_esquad
    self-reported
    75.330
  • AnswerExactMatch (Answer Extraction) on lmqg/qg_esquad
    self-reported
    57.980