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
    results:
      - task:
          name: Text2text Generation
          type: text2text-generation
        dataset:
          name: lmqg/qg_itquad
          type: default
          args: default
        metrics:
          - name: BLEU4
            type: bleu4
            value: 0.07374845292566005
          - name: ROUGE-L
            type: rouge-l
            value: 0.2192586325405669
          - name: METEOR
            type: meteor
            value: 0.17566508622690377
          - name: BERTScore
            type: bertscore
            value: 0.8079826348452711
          - name: MoverScore
            type: moverscore
            value: 0.5678645897809871

Language Models Fine-tuning on Question Generation: `lmqg/mt5-small-itquad`

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

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: TBA

Usage


from transformers import pipeline

model_path = 'lmqg/mt5-small-itquad'
pipe = pipeline("text2text-generation", model_path)

# Question Generation
question = pipe('<hl> Dopo il 1971 <hl> , l' OPEC ha tardato ad adeguare i prezzi per riflettere tale deprezzamento.')

Evaluation Metrics

Metrics

Dataset	Type	BLEU4	ROUGE-L	METEOR	BERTScore	MoverScore	Link
lmqg/qg_itquad	default	0.074	0.219	0.176	0.808	0.568	link

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

TBA

Language Models Fine-tuning on Question Generation: lmqg/mt5-small-itquad