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",
}
Downloads last month
23
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

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