metadata
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_subjqa
pipeline_tag: text2text-generation
tags:
- question generation
widget:
- text: >-
<hl> Beyonce <hl> further expanded her acting career, starring as blues
singer Etta James in the 2008 musical biopic, Cadillac Records.
example_title: Question Generation Example 1
- text: >-
Beyonce further expanded her acting career, starring as blues singer <hl>
Etta James <hl> in the 2008 musical biopic, Cadillac Records.
example_title: Question Generation Example 2
- text: >-
Beyonce further expanded her acting career, starring as blues singer Etta
James in the 2008 musical biopic, <hl> Cadillac Records <hl> .
example_title: Question Generation Example 3
model-index:
- name: lmqg/bart-base-subjqa-restaurants
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: restaurants
args: restaurants
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 3.43
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 24.26
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 21.35
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 93.23
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 62.67
Model Card of lmqg/bart-base-subjqa-restaurants
This model is fine-tuned version of lmqg/bart-base-squad for question generation task on the lmqg/qg_subjqa (dataset_name: restaurants) via lmqg
.
This model is continuously fine-tuned with lmqg/bart-base-squad.
Overview
- Language model: lmqg/bart-base-squad
- Language: en
- Training data: lmqg/qg_subjqa (restaurants)
- 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="en", model="lmqg/bart-base-subjqa-restaurants")
# model prediction
questions = model.generate_q(list_context="William Turner was an English painter who specialised in watercolour landscapes", list_answer="William Turner")
- With
transformers
from transformers import pipeline
pipe = pipeline("text2text-generation", "lmqg/bart-base-subjqa-restaurants")
output = pipe("<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.")
Evaluation
- Metric (Question Generation): raw metric file
Score | Type | Dataset | |
---|---|---|---|
BERTScore | 93.23 | restaurants | lmqg/qg_subjqa |
Bleu_1 | 22.9 | restaurants | lmqg/qg_subjqa |
Bleu_2 | 14.6 | restaurants | lmqg/qg_subjqa |
Bleu_3 | 6.11 | restaurants | lmqg/qg_subjqa |
Bleu_4 | 3.43 | restaurants | lmqg/qg_subjqa |
METEOR | 21.35 | restaurants | lmqg/qg_subjqa |
MoverScore | 62.67 | restaurants | lmqg/qg_subjqa |
ROUGE_L | 24.26 | restaurants | lmqg/qg_subjqa |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_subjqa
- dataset_name: restaurants
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: None
- model: lmqg/bart-base-squad
- max_length: 512
- max_length_output: 32
- epoch: 4
- batch: 32
- lr: 5e-05
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 2
- 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",
}