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-base-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: 7.7
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 22.51
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 18
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 81.16
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 57.11
Model Card of lmqg/mt5-base-itquad-qg
This model is fine-tuned version of google/mt5-base for question generation task on the lmqg/qg_itquad (dataset_name: default) via lmqg
.
Overview
- Language model: google/mt5-base
- 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="lmqg/mt5-base-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", "lmqg/mt5-base-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 | 81.16 | default | lmqg/qg_itquad |
Bleu_1 | 23.29 | default | lmqg/qg_itquad |
Bleu_2 | 15.37 | default | lmqg/qg_itquad |
Bleu_3 | 10.72 | default | lmqg/qg_itquad |
Bleu_4 | 7.7 | default | lmqg/qg_itquad |
METEOR | 18 | default | lmqg/qg_itquad |
MoverScore | 57.11 | default | lmqg/qg_itquad |
ROUGE_L | 22.51 | 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: google/mt5-base
- max_length: 512
- max_length_output: 32
- epoch: 11
- batch: 4
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 16
- 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",
}