metadata
license: cc-by-4.0
metrics:
- bleu4
- meteor
- rouge-l
- bertscore
- moverscore
language: en
datasets:
- lmqg/qg_squad
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-squad
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4
type: bleu4
value: 0.26168385362299557
- name: ROUGE-L
type: rouge-l
value: 0.5384959163821219
- name: METEOR
type: meteor
value: 0.27073122286541956
- name: BERTScore
type: bertscore
value: 0.9100413219045603
- name: MoverScore
type: moverscore
value: 0.6499011626820898
- name: QAAlignedF1Score (BERTScore)
type: qa_aligned_f1_score_bertscore
value: 0.9553719665829591
- name: QAAlignedRecall (BERTScore)
type: qa_aligned_recall_bertscore
value: 0.9553719676636558
- name: QAAlignedPrecision (BERTScore)
type: qa_aligned_precision_bertscore
value: 0.9553719676636558
- name: QAAlignedF1Score (MoverScore)
type: qa_aligned_f1_score_moverscore
value: 0.7082452551815105
- name: QAAlignedRecall (MoverScore)
type: qa_aligned_recall_moverscore
value: 0.7082445720362622
- name: QAAlignedPrecision (MoverScore)
type: qa_aligned_precision_moverscore
value: 0.7082445720362622
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: tripadvisor
args: tripadvisor
metrics:
- name: BLEU4
type: bleu4
value: 8.380171318718442e-7
- name: ROUGE-L
type: rouge-l
value: 0.1402922852924756
- name: METEOR
type: meteor
value: 0.1372146070365174
- name: BERTScore
type: bertscore
value: 0.8891002409937424
- name: MoverScore
type: moverscore
value: 0.5604572211470809
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: amazon
args: amazon
metrics:
- name: BLEU4
type: bleu4
value: 0.06530369842068952
- name: ROUGE-L
type: rouge-l
value: 0.25030985091008146
- name: METEOR
type: meteor
value: 0.2229994442645732
- name: BERTScore
type: bertscore
value: 0.9092814804525936
- name: MoverScore
type: moverscore
value: 0.6086538514008419
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: books
args: books
metrics:
- name: BLEU4
type: bleu4
value: 0.006278914808207679
- name: ROUGE-L
type: rouge-l
value: 0.12368226019088967
- name: METEOR
type: meteor
value: 0.11576293675813865
- name: BERTScore
type: bertscore
value: 0.8807110440044503
- name: MoverScore
type: moverscore
value: 0.5555905941686486
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: restaurants
args: restaurants
metrics:
- name: BLEU4
type: bleu4
value: 0.0000011301750984972448
- name: ROUGE-L
type: rouge-l
value: 0.13083168975354642
- name: METEOR
type: meteor
value: 0.12419733006916912
- name: BERTScore
type: bertscore
value: 0.8797711839570719
- name: MoverScore
type: moverscore
value: 0.5542757411268555
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: movies
args: movies
metrics:
- name: BLEU4
type: bleu4
value: 0.0000010121579426501661
- name: ROUGE-L
type: rouge-l
value: 0.12508697028506718
- name: METEOR
type: meteor
value: 0.11862284941640638
- name: BERTScore
type: bertscore
value: 0.8748829724726739
- name: MoverScore
type: moverscore
value: 0.5528899173535703
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: grocery
args: grocery
metrics:
- name: BLEU4
type: bleu4
value: 0.00528043272450429
- name: ROUGE-L
type: rouge-l
value: 0.12343711316491492
- name: METEOR
type: meteor
value: 0.15133496445452477
- name: BERTScore
type: bertscore
value: 0.8778951253890991
- name: MoverScore
type: moverscore
value: 0.5701949938103265
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: nyt
args: nyt
metrics:
- name: BLEU4
type: bleu4
value: 0.08117757543966063
- name: ROUGE-L
type: rouge-l
value: 0.25292097720734297
- name: METEOR
type: meteor
value: 0.25254205113198686
- name: BERTScore
type: bertscore
value: 0.9249009759439454
- name: MoverScore
type: moverscore
value: 0.6406329128556304
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: electronics
args: electronics
metrics:
- name: BLEU4
type: bleu4
value: 0.00866799444965211
- name: ROUGE-L
type: rouge-l
value: 0.1601628874804186
- name: METEOR
type: meteor
value: 0.15348605312210778
- name: BERTScore
type: bertscore
value: 0.8783386920680519
- name: MoverScore
type: moverscore
value: 0.5634845371093992
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: new_wiki
args: new_wiki
metrics:
- name: BLEU4
type: bleu4
value: 0.11118273173452982
- name: ROUGE-L
type: rouge-l
value: 0.2967546690273089
- name: METEOR
type: meteor
value: 0.27315087810722966
- name: BERTScore
type: bertscore
value: 0.9322739617807421
- name: MoverScore
type: moverscore
value: 0.6623000084761579
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: reddit
args: reddit
metrics:
- name: BLEU4
type: bleu4
value: 0.059525104157825456
- name: ROUGE-L
type: rouge-l
value: 0.22365090580055863
- name: METEOR
type: meteor
value: 0.21499800504546457
- name: BERTScore
type: bertscore
value: 0.9095144685254328
- name: MoverScore
type: moverscore
value: 0.6059332247878408
Model Card of lmqg/bart-large-squad
This model is fine-tuned version of facebook/bart-large for question generation task on the
lmqg/qg_squad (dataset_name: default) 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: facebook/bart-large
- Language: en
- Training data: lmqg/qg_squad (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/bart-large-squad')
# 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/bart-large-squad')
# 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_squad | default | 0.262 | 0.538 | 0.271 | 0.91 | 0.65 | link |
Metrics (QAG)
| Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
|---|---|---|---|---|
| lmqg/qg_squad | default | 0.955 | 0.708 | link |
Out-of-domain Metrics
| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|---|---|---|---|---|---|---|---|
| lmqg/qg_subjqa | tripadvisor | 0.0 | 0.14 | 0.137 | 0.889 | 0.56 | link |
| lmqg/qg_squadshifts | amazon | 0.065 | 0.25 | 0.223 | 0.909 | 0.609 | link |
| lmqg/qg_subjqa | books | 0.006 | 0.124 | 0.116 | 0.881 | 0.556 | link |
| lmqg/qg_subjqa | restaurants | 0.0 | 0.131 | 0.124 | 0.88 | 0.554 | link |
| lmqg/qg_subjqa | movies | 0.0 | 0.125 | 0.119 | 0.875 | 0.553 | link |
| lmqg/qg_subjqa | grocery | 0.005 | 0.123 | 0.151 | 0.878 | 0.57 | link |
| lmqg/qg_squadshifts | nyt | 0.081 | 0.253 | 0.253 | 0.925 | 0.641 | link |
| lmqg/qg_subjqa | electronics | 0.009 | 0.16 | 0.153 | 0.878 | 0.563 | link |
| lmqg/qg_squadshifts | new_wiki | 0.111 | 0.297 | 0.273 | 0.932 | 0.662 | link |
| lmqg/qg_squadshifts | 0.06 | 0.224 | 0.215 | 0.91 | 0.606 | link |
Training hyperparameters
The following hyperparameters were used during fine-tuning:
- dataset_path: lmqg/qg_squad
- dataset_name: default
- 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: 5e-05
- 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",
}