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: >-
generate question: <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: >-
generate question: 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: >-
generate question: 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/t5-large-subjqa-vanilla-books
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: books
args: books
metrics:
- name: BLEU4
type: bleu4
value: 4.518689535419543e-11
- name: ROUGE-L
type: rouge-l
value: 0.06362608202312194
- name: METEOR
type: meteor
value: 0.0463163512336036
- name: BERTScore
type: bertscore
value: 0.8212118666833608
- name: MoverScore
type: moverscore
value: 0.5029136160777198
Model Card of lmqg/t5-large-subjqa-vanilla-books
This model is fine-tuned version of t5-large for question generation task on the
lmqg/qg_subjqa (dataset_name: books) via lmqg
.
Please cite our paper if you use the model (https://arxiv.org/abs/2210.03992).
@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",
}
Overview
- Language model: t5-large
- Language: en
- Training data: lmqg/qg_subjqa (books)
- 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/t5-large-subjqa-vanilla-books')
# model prediction
question = 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
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/t5-large-subjqa-vanilla-books')
# question generation
question = pipe('generate question: <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_subjqa | books | 0.0 | 0.064 | 0.046 | 0.821 | 0.503 | link |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_subjqa
- dataset_name: books
- input_types: ['paragraph_answer']
- output_types: ['question']
- prefix_types: ['qg']
- model: t5-large
- max_length: 512
- max_length_output: 32
- epoch: 1
- batch: 16
- lr: 1e-05
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 8
- 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",
}