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: vocabtrimmer/mt5-small-trimmed-it-15000-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: 0
          - name: ROUGE-L (Question Generation)
            type: rouge_l_question_generation
            value: 0.46
          - name: METEOR (Question Generation)
            type: meteor_question_generation
            value: 0.37
          - name: BERTScore (Question Generation)
            type: bertscore_question_generation
            value: 56.13
          - name: MoverScore (Question Generation)
            type: moverscore_question_generation
            value: 45.75

Model Card of `vocabtrimmer/mt5-small-trimmed-it-15000-itquad-qg`

This model is fine-tuned version of vocabtrimmer/mt5-small-trimmed-it-15000 for question generation task on the lmqg/qg_itquad (dataset_name: default) via lmqg.

Overview

Language model: vocabtrimmer/mt5-small-trimmed-it-15000
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="vocabtrimmer/mt5-small-trimmed-it-15000-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", "vocabtrimmer/mt5-small-trimmed-it-15000-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	56.13	default	lmqg/qg_itquad
Bleu_1	0.49	default	lmqg/qg_itquad
Bleu_2	0	default	lmqg/qg_itquad
Bleu_3	0	default	lmqg/qg_itquad
Bleu_4	0	default	lmqg/qg_itquad
METEOR	0.37	default	lmqg/qg_itquad
MoverScore	45.75	default	lmqg/qg_itquad
ROUGE_L	0.46	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: vocabtrimmer/mt5-small-trimmed-it-15000
max_length: 512
max_length_output: 32
epoch: 5
batch: 16
lr: 0.0005
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",
}