bart-large-squad-qg / README.md
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model update
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---
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: 91.0
- name: MoverScore (Question Generation)
type: moverscore_question_generation
value: 64.99
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 95.54
- name: QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 95.49
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_precision_bertscore_question_answer_generation_with_gold_answer_gold_answer
value: 95.59
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_f1_score_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 70.82
- name: QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_recall_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 70.54
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]
type: qa_aligned_precision_moverscore_question_answer_generation_with_gold_answer_gold_answer
value: 71.13
- name: QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer
value: 93.23
- name: QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_recall_bertscore_question_answer_generation_gold_answer
value: 93.35
- name: QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_precision_bertscore_question_answer_generation_gold_answer
value: 93.13
- name: QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer
value: 64.76
- name: QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_recall_moverscore_question_answer_generation_gold_answer
value: 64.63
- name: QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]
type: qa_aligned_precision_moverscore_question_answer_generation_gold_answer
value: 64.98
- 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: 1.0121579426501661e-06
- 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: 1.1301750984972448e-06
- 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-07
- 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](https://huggingface.co/facebook/bart-large) for question generation task on the [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation).
### Overview
- **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
- **Language:** en
- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default)
- **Online Demo:** [https://autoqg.net/](https://autoqg.net/)
- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation)
- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)
### Usage
- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-)
```python
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`
```python
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](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore | 91 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1 | 58.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2 | 42.79 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3 | 33.11 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4 | 26.17 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR | 27.07 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore | 64.99 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L | 53.85 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metric (Question & Answer Generation, Reference Answer)***: Each question is generated from *the gold answer*. [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 95.54 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore) | 70.82 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore) | 95.59 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) | 71.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore) | 95.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore) | 70.54 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metric (Question & Answer Generation, Pipeline Approach)***: Each question is generated on the answer generated by [`lmqg/bart-large-squad-ae`](https://huggingface.co/lmqg/bart-large-squad-ae). [raw metric file](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_bart-large-squad-ae.json)
| | Score | Type | Dataset |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore) | 93.23 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore) | 64.76 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore) | 93.13 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) | 64.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore) | 93.35 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore) | 64.63 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
- ***Metrics (Question Generation, Out-of-Domain)***
| Dataset | Type | BERTScore| Bleu_4 | METEOR | MoverScore | ROUGE_L | Link |
|:--------|:-----|---------:|-------:|-------:|-----------:|--------:|-----:|
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 90.93 | 6.53 | 22.3 | 60.87 | 25.03 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) |
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 93.23 | 11.12 | 27.32 | 66.23 | 29.68 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) |
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 92.49 | 8.12 | 25.25 | 64.06 | 25.29 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) |
| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 90.95 | 5.95 | 21.5 | 60.59 | 22.37 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 88.07 | 0.63 | 11.58 | 55.56 | 12.37 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 87.83 | 0.87 | 15.35 | 56.35 | 16.02 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 87.79 | 0.53 | 15.13 | 57.02 | 12.34 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 87.49 | 0.0 | 11.86 | 55.29 | 12.51 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 87.98 | 0.0 | 12.42 | 55.43 | 13.08 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) |
| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 88.91 | 0.0 | 13.72 | 56.05 | 14.03 | [link](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) |
## 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](https://huggingface.co/lmqg/bart-large-squad-qg/raw/main/trainer_config.json).
## 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",
}
```