metadata
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_dequad
pipeline_tag: text2text-generation
tags:
- question generation
- answer extraction
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
- text: >-
<hl> Beyonce further expanded her acting career, starring as blues singer
Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her
performance in the film received praise from critics, and she garnered
several nominations for her portrayal of James, including a Satellite
Award nomination for Best Supporting Actress, and a NAACP Image Award
nomination for Outstanding Supporting Actress.
example_title: Answer Extraction Example 1
- text: >-
Beyonce further expanded her acting career, starring as blues singer Etta
James in the 2008 musical biopic, Cadillac Records. <hl> Her performance
in the film received praise from critics, and she garnered several
nominations for her portrayal of James, including a Satellite Award
nomination for Best Supporting Actress, and a NAACP Image Award nomination
for Outstanding Supporting Actress. <hl>
example_title: Answer Extraction Example 2
model-index:
- name: lmqg/mt5-small-dequad-multitask
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_dequad
type: default
args: default
metrics:
- name: BLEU4
type: bleu4
value: 0.008153318257935705
- name: ROUGE-L
type: rouge-l
value: 0.10153326763371277
- name: METEOR
type: meteor
value: 0.12181097136639749
- name: BERTScore
type: bertscore
value: 0.8038890473051649
- name: MoverScore
type: moverscore
value: 0.551016955735025
- name: QAAlignedF1Score (BERTScore)
type: qa_aligned_f1_score_bertscore
value: 0.799873268285167
- name: QAAlignedRecall (BERTScore)
type: qa_aligned_recall_bertscore
value: 0.812945931926931
- name: QAAlignedPrecision (BERTScore)
type: qa_aligned_precision_bertscore
value: 0.7878875412489514
- name: QAAlignedF1Score (MoverScore)
type: qa_aligned_f1_score_moverscore
value: 0.5388739369566669
- name: QAAlignedRecall (MoverScore)
type: qa_aligned_recall_moverscore
value: 0.5426169060178441
- name: QAAlignedPrecision (MoverScore)
type: qa_aligned_precision_moverscore
value: 0.535616846379365
Model Card of lmqg/mt5-small-dequad-multitask
This model is fine-tuned version of google/mt5-small for question generation task on the
lmqg/qg_dequad (dataset_name: default) via lmqg
.
This model is fine-tuned on the answer extraction task as well as the question generation.
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: google/mt5-small
- Language: en
- Training data: lmqg/qg_dequad (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='en', model='lmqg/mt5-small-dequad-multitask')
# model prediction
question_answer = model.generate_qa("William Turner was an English painter who specialised in watercolour landscapes")
- With
transformers
from transformers import pipeline
# initialize model
pipe = pipeline("text2text-generation", 'lmqg/mt5-small-dequad-multitask')
# answer extraction
answer = pipe('extract answers: <hl> Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records. <hl> Her performance in the film received praise from critics, and she garnered several nominations for her portrayal of James, including a Satellite Award nomination for Best Supporting Actress, and a NAACP Image Award nomination for Outstanding Supporting Actress.')
# 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_dequad | default | 0.008 | 0.102 | 0.122 | 0.804 | 0.551 | link |
Metrics (QAG)
Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
---|---|---|---|---|
lmqg/qg_dequad | default | 0.8 | 0.539 | link |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_dequad
- dataset_name: default
- input_types: ['paragraph_answer', 'paragraph_sentence']
- output_types: ['question', 'answer']
- prefix_types: ['qg', 'ae']
- model: google/mt5-small
- max_length: 512
- max_length_output: 32
- epoch: 15
- batch: 16
- lr: 0.001
- fp16: False
- random_seed: 1
- gradient_accumulation_steps: 4
- 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",
}