metadata

license: cc-by-4.0
metrics:
  - bleu4
  - meteor
  - rouge-l
  - bertscore
  - moverscore
language: es
datasets:
  - lmqg/qg_esquad
pipeline_tag: text2text-generation
tags:
  - question generation
widget:
  - text: del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.
    example_title: Question Generation Example 1
  - text: a <hl> noviembre <hl> , que es también la estación lluviosa.
    example_title: Question Generation Example 2
  - text: >-
      como <hl> el gobierno de Abbott <hl> que asumió el cargo el 18 de
      septiembre de 2013.
    example_title: Question Generation Example 3
model-index:
  - name: lmqg/mt5-small-esquad-qg
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_esquad
          type: default
          args: default
        metrics:
          - name: BLEU4 (Question Generation)
            type: bleu4_question_generation
            value: 9.61
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 24.62
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 22.71
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 84.07
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 59.06
          - 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: 89.43
          - 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: 89.41
          - 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: 89.44
          - 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: 63.73
          - 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: 63.72
          - 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: 63.75
          - name: >-
              QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
            value: 79.89
          - name: >-
              QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
            value: 82.56
          - name: >-
              QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_bertscore_question_answer_generation_gold_answer
            value: 77.46
          - name: >-
              QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
            value: 54.82
          - name: >-
              QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_recall_moverscore_question_answer_generation_gold_answer
            value: 56.52
          - name: >-
              QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
              Answer]
            type: >-
              qa_aligned_precision_moverscore_question_answer_generation_gold_answer
            value: 53.31

Model Card of `lmqg/mt5-small-esquad-qg`

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

Overview

Language model: google/mt5-small
Language: es
Training data: lmqg/qg_esquad (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="es", model="lmqg/mt5-small-esquad-qg")

# model prediction
questions = model.generate_q(list_context="a noviembre , que es también la estación lluviosa.", list_answer="noviembre")

With transformers

from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mt5-small-esquad-qg")
output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")

Evaluation

Metric (Question Generation): raw metric file

	Score	Type	Dataset
BERTScore	84.07	default	lmqg/qg_esquad
Bleu_1	26.03	default	lmqg/qg_esquad
Bleu_2	17.75	default	lmqg/qg_esquad
Bleu_3	12.88	default	lmqg/qg_esquad
Bleu_4	9.61	default	lmqg/qg_esquad
METEOR	22.71	default	lmqg/qg_esquad
MoverScore	59.06	default	lmqg/qg_esquad
ROUGE_L	24.62	default	lmqg/qg_esquad

Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	89.43	default	lmqg/qg_esquad
QAAlignedF1Score (MoverScore)	63.73	default	lmqg/qg_esquad
QAAlignedPrecision (BERTScore)	89.44	default	lmqg/qg_esquad
QAAlignedPrecision (MoverScore)	63.75	default	lmqg/qg_esquad
QAAlignedRecall (BERTScore)	89.41	default	lmqg/qg_esquad
QAAlignedRecall (MoverScore)	63.72	default	lmqg/qg_esquad

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

	Score	Type	Dataset
QAAlignedF1Score (BERTScore)	79.89	default	lmqg/qg_esquad
QAAlignedF1Score (MoverScore)	54.82	default	lmqg/qg_esquad
QAAlignedPrecision (BERTScore)	77.46	default	lmqg/qg_esquad
QAAlignedPrecision (MoverScore)	53.31	default	lmqg/qg_esquad
QAAlignedRecall (BERTScore)	82.56	default	lmqg/qg_esquad
QAAlignedRecall (MoverScore)	56.52	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']
output_types: ['question']
prefix_types: None
model: google/mt5-small
max_length: 512
max_length_output: 32
epoch: 16
batch: 64
lr: 0.0005
fp16: False
random_seed: 1
gradient_accumulation_steps: 1
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",
}