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-qg
results:
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squad
type: default
args: default
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 26.17
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 53.85
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 27.07
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 91
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 64.99
- name: >-
QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
value: 95.54
- name: >-
QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold
Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
value: 95.49
- name: >-
QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_precision_bertscore_question_answer_generation_gold_answer
value: 95.59
- name: >-
QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
value: 70.82
- name: >-
QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_recall_moverscore_question_answer_generation_gold_answer
value: 70.54
- name: >-
QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold
Answer]
type: >-
qa_aligned_precision_moverscore_question_answer_generation_gold_answer
value: 71.13
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: amazon
args: amazon
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.06530369842068952
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.25030985091008146
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.2229994442645732
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.9092814804525936
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.6086538514008419
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: new_wiki
args: new_wiki
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.11118273173452982
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.2967546690273089
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.27315087810722966
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.9322739617807421
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.6623000084761579
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: nyt
args: nyt
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.08117757543966063
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.25292097720734297
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.25254205113198686
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.9249009759439454
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.6406329128556304
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_squadshifts
type: reddit
args: reddit
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.059525104157825456
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.22365090580055863
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.21499800504546457
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.9095144685254328
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.6059332247878408
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: books
args: books
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.006278914808207679
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.12368226019088967
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.11576293675813865
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.8807110440044503
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.5555905941686486
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: electronics
args: electronics
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.00866799444965211
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.1601628874804186
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.15348605312210778
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.8783386920680519
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.5634845371093992
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: grocery
args: grocery
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.00528043272450429
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.12343711316491492
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.15133496445452477
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.8778951253890991
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.5701949938103265
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: movies
args: movies
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 0.0000010121579426501661
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.12508697028506718
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.11862284941640638
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.8748829724726739
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.5528899173535703
- 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: 0.0000011301750984972448
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.13083168975354642
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.12419733006916912
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.8797711839570719
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.5542757411268555
- task:
name: Text2text Generation
type: text2text-generation
dataset:
name: lmqg/qg_subjqa
type: tripadvisor
args: tripadvisor
metrics:
- name: BLEU4 (Question Generation)
type: bleu4_question_generation
value: 8.380171318718442e-7
- name: ROUGE-L (Question Generation)
type: rouge_l_question_generation
value: 0.1402922852924756
- name: METEOR (Question Generation)
type: meteor_question_generation
value: 0.1372146070365174
- name: BERTScore (Question Generation)
type: bertscore_question_generation
value: 0.8891002409937424
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 0.5604572211470809
Model Card of lmqg/bart-large-squad-qg
This model is fine-tuned version of facebook/bart-large for question generation task on the lmqg/qg_squad (dataset_name: default) via lmqg.
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-qg")
# 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-large-squad-qg")
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 | 91 | default | lmqg/qg_squad |
| Bleu_1 | 58.79 | default | lmqg/qg_squad |
| Bleu_2 | 42.79 | default | lmqg/qg_squad |
| Bleu_3 | 33.11 | default | lmqg/qg_squad |
| Bleu_4 | 26.17 | default | lmqg/qg_squad |
| METEOR | 27.07 | default | lmqg/qg_squad |
| MoverScore | 64.99 | default | lmqg/qg_squad |
| ROUGE_L | 53.85 | default | lmqg/qg_squad |
- Metric (Question & Answer Generation): QAG metrics are computed with the gold answer and generated question on it for this model, as the model cannot provide an answer. raw metric file
| Score | Type | Dataset | |
|---|---|---|---|
| QAAlignedF1Score (BERTScore) | 95.54 | default | lmqg/qg_squad |
| QAAlignedF1Score (MoverScore) | 70.82 | default | lmqg/qg_squad |
| QAAlignedPrecision (BERTScore) | 95.59 | default | lmqg/qg_squad |
| QAAlignedPrecision (MoverScore) | 71.13 | default | lmqg/qg_squad |
| QAAlignedRecall (BERTScore) | 95.49 | default | lmqg/qg_squad |
| QAAlignedRecall (MoverScore) | 70.54 | default | lmqg/qg_squad |
- Metrics (Question Generation, Out-of-Domain)
| Dataset | Type | BERTScore | Bleu_4 | METEOR | MoverScore | ROUGE_L | Link |
|---|---|---|---|---|---|---|---|
| lmqg/qg_squadshifts | amazon | 90.93 | 6.53 | 22.3 | 60.87 | 25.03 | link |
| lmqg/qg_squadshifts | new_wiki | 93.23 | 11.12 | 27.32 | 66.23 | 29.68 | link |
| lmqg/qg_squadshifts | nyt | 92.49 | 8.12 | 25.25 | 64.06 | 25.29 | link |
| lmqg/qg_squadshifts | 90.95 | 5.95 | 21.5 | 60.59 | 22.37 | link | |
| lmqg/qg_subjqa | books | 88.07 | 0.63 | 11.58 | 55.56 | 12.37 | link |
| lmqg/qg_subjqa | electronics | 87.83 | 0.87 | 15.35 | 56.35 | 16.02 | link |
| lmqg/qg_subjqa | grocery | 87.79 | 0.53 | 15.13 | 57.02 | 12.34 | link |
| lmqg/qg_subjqa | movies | 87.49 | 0.0 | 11.86 | 55.29 | 12.51 | link |
| lmqg/qg_subjqa | restaurants | 87.98 | 0.0 | 12.42 | 55.43 | 13.08 | link |
| lmqg/qg_subjqa | tripadvisor | 88.91 | 0.0 | 13.72 | 56.05 | 14.03 | 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",
}