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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: vocabtrimmer/mt5-small-trimmed-es-60000-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: 9.79
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 24.63
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 22.64
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 84.4
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 59.16

Model Card of vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg

This model is fine-tuned version of ckpts/mt5-small-trimmed-es-60000 for question generation task on the lmqg/qg_esquad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="es", model="vocabtrimmer/mt5-small-trimmed-es-60000-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", "vocabtrimmer/mt5-small-trimmed-es-60000-esquad-qg")
output = pipe("del <hl> Ministerio de Desarrollo Urbano <hl> , Gobierno de la India.")

Evaluation

Score Type Dataset
BERTScore 84.4 default lmqg/qg_esquad
Bleu_1 26.37 default lmqg/qg_esquad
Bleu_2 18.03 default lmqg/qg_esquad
Bleu_3 13.1 default lmqg/qg_esquad
Bleu_4 9.79 default lmqg/qg_esquad
METEOR 22.64 default lmqg/qg_esquad
MoverScore 59.16 default lmqg/qg_esquad
ROUGE_L 24.63 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: ckpts/mt5-small-trimmed-es-60000
  • max_length: 512
  • max_length_output: 32
  • epoch: 13
  • batch: 16
  • lr: 0.001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 4
  • 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",
}