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: >-
              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
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 81.63
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 82.28
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 81.04
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 55.85
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 56.14
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 55.6

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

Language model: google/mt5-small
Language: it
Training data: lmqg/qg_itquad (default)
Online Demo: https://autoqg.net/
Repository: https://github.com/asahi417/lm-question-generation
Paper: https://arxiv.org/abs/2210.03992

Usage

With lmqg

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

Metric (Question Generation): raw metric file

	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
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

Metric (Question & Answer Generation, Pipeline Approach): Each question is generated on the answer generated by lmqg/mt5-small-itquad-ae. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	81.63	default	lmqg/qg_itquad
QAAlignedF1Score (MoverScore)	55.85	default	lmqg/qg_itquad
QAAlignedPrecision (BERTScore)	81.04	default	lmqg/qg_itquad
QAAlignedPrecision (MoverScore)	55.6	default	lmqg/qg_itquad
QAAlignedRecall (BERTScore)	82.28	default	lmqg/qg_itquad
QAAlignedRecall (MoverScore)	56.14	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",
}