mt5-small-itquad-qg / README.md
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metadata
license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: it
datasets:
  - lmqg/qg_itquad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: >-
      <hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per
      riflettere tale deprezzamento.
    example_title: Question Generation Example 1
  - text: >-
      L' individuazione del petrolio e lo sviluppo di nuovi giacimenti
      richiedeva in genere <hl> da cinque a dieci anni <hl> prima di una
      produzione significativa.
    example_title: Question Generation Example 2
  - text: il <hl> Giappone <hl> è stato il paese più dipendente dal petrolio arabo.
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-small-itquad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_itquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 7.37
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 21.93
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 17.57
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 80.8
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 56.79
          - name: BLEU4 (Question & Answer Generation (with Gold Answer))
            type: bleu4_question_answer_generation_with_gold_answer
            value: 15.44
          - name: ROUGE-L (Question & Answer Generation (with Gold Answer))
            type: rouge_l_question_answer_generation_with_gold_answer
            value: 40.08
          - name: METEOR (Question & Answer Generation (with Gold Answer))
            type: meteor_question_answer_generation_with_gold_answer
            value: 34.31
          - name: BERTScore (Question & Answer Generation (with Gold Answer))
            type: bertscore_question_answer_generation_with_gold_answer
            value: 86.62
          - name: MoverScore (Question & Answer Generation (with Gold Answer))
            type: moverscore_question_answer_generation_with_gold_answer
            value: 60.68
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 87.66
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold
              Answer)) [Gold Answer]
            type: >-
              qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 87.57
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
            value: 87.76
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
            value: 61.6
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
            value: 61.48
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation (with
              Gold Answer)) [Gold Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
            value: 61.73

Model Card of lmqg/mt5-small-itquad-qg

This model is fine-tuned version of google/mt5-small 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="lmqg/mt5-small-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", "lmqg/mt5-small-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 80.8 default lmqg/qg_itquad
Bleu_1 22.78 default lmqg/qg_itquad
Bleu_2 14.93 default lmqg/qg_itquad
Bleu_3 10.34 default lmqg/qg_itquad
Bleu_4 7.37 default lmqg/qg_itquad
METEOR 17.57 default lmqg/qg_itquad
MoverScore 56.79 default lmqg/qg_itquad
ROUGE_L 21.93 default lmqg/qg_itquad
  • Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score Type Dataset
BERTScore 86.62 default lmqg/qg_itquad
Bleu_1 40.5 default lmqg/qg_itquad
Bleu_2 28.64 default lmqg/qg_itquad
Bleu_3 20.78 default lmqg/qg_itquad
Bleu_4 15.44 default lmqg/qg_itquad
METEOR 34.31 default lmqg/qg_itquad
MoverScore 60.68 default lmqg/qg_itquad
QAAlignedF1Score (BERTScore) 87.66 default lmqg/qg_itquad
QAAlignedF1Score (MoverScore) 61.6 default lmqg/qg_itquad
QAAlignedPrecision (BERTScore) 87.76 default lmqg/qg_itquad
QAAlignedPrecision (MoverScore) 61.73 default lmqg/qg_itquad
QAAlignedRecall (BERTScore) 87.57 default lmqg/qg_itquad
QAAlignedRecall (MoverScore) 61.48 default lmqg/qg_itquad
ROUGE_L 40.08 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: google/mt5-small
  • max_length: 512
  • max_length_output: 32
  • epoch: 15
  • batch: 16
  • lr: 0.0005
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • label_smoothing: 0.0

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",
}