license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squadshifts
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-large-squadshifts-vanilla-new_wiki
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: new_wiki
args: new_wiki
metrics:
- name: BLEU4
type: bleu4
value: 0.10851725243592192
- name: ROUGE-L
type: rouge-l
value: 0.2806503932128761
- name: METEOR
type: meteor
value: 0.25619049406746713
- name: BERTScore
type: bertscore
value: 0.9285402424467387
- name: MoverScore
type: moverscore
value: 0.6497009107577334
Model Card of lmqg/bart-large-squadshifts-vanilla-new_wiki
This model is fine-tuned version of facebook/bart-large for question generation task on the
lmqg/qg_squadshifts (dataset_name: new_wiki) via lmqg
.
Please cite our paper if you use the model (TBA).
@inproceedings{ushio-etal-2022-generative,
title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration: {A} {U}nified {B}enchmark and {E}valuation",
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",
}
Overview
- Language model: facebook/bart-large
- Language: en
- Training data: lmqg/qg_squadshifts (new_wiki)
- Online Demo: https://autoqg.net/
- Repository: https://github.com/asahi417/lm-question-generation
- Paper: TBA
Usage
from transformers import pipeline
model_path = 'lmqg/bart-large-squadshifts-vanilla-new_wiki'
pipe = pipeline("text2text-generation", model_path)
# Question Generation
question = pipe('<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.')
Evaluation Metrics
Metrics
Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
---|---|---|---|---|---|---|---|
lmqg/qg_squadshifts | new_wiki | 0.109 | 0.281 | 0.256 | 0.929 | 0.65 | link |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squadshifts
- dataset_name: new_wiki
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: None
- model: facebook/bart-large
- max_length: 512
- max_length_output: 32
- epoch: 4
- batch: 32
- lr: 0.0001
- 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: {A} {U}nified {B}enchmark and {E}valuation", 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", }