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-5000-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: 9.41
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 23.51
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 21.88
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 84.07
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 58.84

Model Card of `vocabtrimmer/mt5-small-trimmed-es-5000-esquad-qg`

This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-es-5000 for question generation task on the lmqg/qg_esquad (dataset_name: default) via lmqg.

Overview

Language model: vocabtrimmer/mt5-small-trimmed-es-5000
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="vocabtrimmer/mt5-small-trimmed-es-5000-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-5000-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	25.67	default	lmqg/qg_esquad
Bleu_2	17.4	default	lmqg/qg_esquad
Bleu_3	12.59	default	lmqg/qg_esquad
Bleu_4	9.41	default	lmqg/qg_esquad
METEOR	21.88	default	lmqg/qg_esquad
MoverScore	58.84	default	lmqg/qg_esquad
ROUGE_L	23.51	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: vocabtrimmer/mt5-small-trimmed-es-5000
max_length: 512
max_length_output: 32
epoch: 12
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",
}