|
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--- |
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license: cc-by-4.0 |
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metrics: |
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- bleu4 |
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- meteor |
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- rouge-l |
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- bertscore |
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- moverscore |
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language: en |
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datasets: |
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- lmqg/qg_squad |
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pipeline_tag: text2text-generation |
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tags: |
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- question generation |
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widget: |
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- text: "<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records." |
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example_title: "Question Generation Example 1" |
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- text: "Beyonce further expanded her acting career, starring as blues singer <hl> Etta James <hl> in the 2008 musical biopic, Cadillac Records." |
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example_title: "Question Generation Example 2" |
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- text: "Beyonce further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, <hl> Cadillac Records <hl> ." |
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example_title: "Question Generation Example 3" |
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model-index: |
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- name: lmqg/bart-large-squad |
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results: |
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- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squad |
|
type: default |
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args: default |
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metrics: |
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- name: BLEU4 |
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type: bleu4 |
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value: 0.26168385362299557 |
|
- name: ROUGE-L |
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type: rouge-l |
|
value: 0.5384959163821219 |
|
- name: METEOR |
|
type: meteor |
|
value: 0.27073122286541956 |
|
- name: BERTScore |
|
type: bertscore |
|
value: 0.9100413219045603 |
|
- name: MoverScore |
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type: moverscore |
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value: 0.6499011626820898 |
|
- task: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_squadshifts |
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type: reddit |
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args: reddit |
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metrics: |
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- 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 |
|
- task: |
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name: Text2text Generation |
|
type: text2text-generation |
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dataset: |
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name: lmqg/qg_squadshifts |
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type: new_wiki |
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args: new_wiki |
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metrics: |
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- 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: |
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name: Text2text Generation |
|
type: text2text-generation |
|
dataset: |
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name: lmqg/qg_subjqa |
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type: tripadvisor |
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args: tripadvisor |
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metrics: |
|
- name: BLEU4 |
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type: bleu4 |
|
value: 8.380171318718442e-07 |
|
- 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: |
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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: |
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name: lmqg/qg_subjqa |
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type: restaurants |
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args: restaurants |
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metrics: |
|
- name: BLEU4 |
|
type: bleu4 |
|
value: 1.1301750984972448e-06 |
|
- 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: |
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name: lmqg/qg_subjqa |
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type: electronics |
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args: electronics |
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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: |
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name: lmqg/qg_subjqa |
|
type: books |
|
args: books |
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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: |
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name: Text2text Generation |
|
type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: movies |
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args: movies |
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metrics: |
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- name: BLEU4 |
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type: bleu4 |
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value: 1.0121579426501661e-06 |
|
- 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: |
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name: Text2text Generation |
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type: text2text-generation |
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dataset: |
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name: lmqg/qg_subjqa |
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type: grocery |
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args: grocery |
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metrics: |
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- name: BLEU4 |
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type: bleu4 |
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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: |
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name: Text2text Generation |
|
type: text2text-generation |
|
dataset: |
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name: lmqg/qg_squadshifts |
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type: amazon |
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args: amazon |
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metrics: |
|
- name: BLEU4 |
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type: bleu4 |
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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 |
|
--- |
|
|
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# Model Card of `lmqg/bart-large-squad` |
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This model is fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) for question generation task on the |
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[lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (dataset_name: default) via [`lmqg`](https://github.com/asahi417/lm-question-generation). |
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Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992)). |
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|
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``` |
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|
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@inproceedings{ushio-etal-2022-generative, |
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title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration", |
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author = "Ushio, Asahi and |
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Alva-Manchego, Fernando and |
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Camacho-Collados, Jose", |
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booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing", |
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month = dec, |
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year = "2022", |
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address = "Abu Dhabi, U.A.E.", |
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publisher = "Association for Computational Linguistics", |
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} |
|
|
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``` |
|
|
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### Overview |
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- **Language model:** [facebook/bart-large](https://huggingface.co/facebook/bart-large) |
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- **Language:** en |
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- **Training data:** [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) (default) |
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- **Online Demo:** [https://autoqg.net/](https://autoqg.net/) |
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- **Repository:** [https://github.com/asahi417/lm-question-generation](https://github.com/asahi417/lm-question-generation) |
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- **Paper:** [https://arxiv.org/abs/2210.03992](https://arxiv.org/abs/2210.03992) |
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|
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### Usage |
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- With [`lmqg`](https://github.com/asahi417/lm-question-generation#lmqg-language-model-for-question-generation-) |
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```python |
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|
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from lmqg import TransformersQG |
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# initialize model |
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model = TransformersQG(language='en', model='lmqg/bart-large-squad') |
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# model prediction |
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question = model.generate_q(list_context=["William Turner was an English painter who specialised in watercolour landscapes"], list_answer=["William Turner"]) |
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|
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``` |
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|
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- With `transformers` |
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```python |
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|
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from transformers import pipeline |
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# initialize model |
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pipe = pipeline("text2text-generation", 'lmqg/bart-large-squad') |
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# question generation |
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question = pipe('<hl> Beyonce <hl> further expanded her acting career, starring as blues singer Etta James in the 2008 musical biopic, Cadillac Records.') |
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|
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``` |
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|
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## Evaluation Metrics |
|
|
|
|
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### Metrics |
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|
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| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link | |
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|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:| |
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| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.262 | 0.538 | 0.271 | 0.91 | 0.65 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) | |
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|
|
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|
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### Out-of-domain Metrics |
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|
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| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link | |
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|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:| |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | reddit | 0.06 | 0.224 | 0.215 | 0.91 | 0.606 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.reddit.json) | |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | new_wiki | 0.111 | 0.297 | 0.273 | 0.932 | 0.662 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.new_wiki.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | tripadvisor | 0.0 | 0.14 | 0.137 | 0.889 | 0.56 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.tripadvisor.json) | |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | nyt | 0.081 | 0.253 | 0.253 | 0.925 | 0.641 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.nyt.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | restaurants | 0.0 | 0.131 | 0.124 | 0.88 | 0.554 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.restaurants.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | electronics | 0.009 | 0.16 | 0.153 | 0.878 | 0.563 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.electronics.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | books | 0.006 | 0.124 | 0.116 | 0.881 | 0.556 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.books.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | movies | 0.0 | 0.125 | 0.119 | 0.875 | 0.553 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.movies.json) | |
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| [lmqg/qg_subjqa](https://huggingface.co/datasets/lmqg/qg_subjqa) | grocery | 0.005 | 0.123 | 0.151 | 0.878 | 0.57 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_subjqa.grocery.json) | |
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| [lmqg/qg_squadshifts](https://huggingface.co/datasets/lmqg/qg_squadshifts) | amazon | 0.065 | 0.25 | 0.223 | 0.909 | 0.609 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval_ood/metric.first.sentence.paragraph_answer.question.lmqg_qg_squadshifts.amazon.json) | |
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|
|
|
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## Training hyperparameters |
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|
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The following hyperparameters were used during fine-tuning: |
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- dataset_path: lmqg/qg_squad |
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- dataset_name: default |
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- input_types: ['paragraph_answer'] |
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- output_types: ['question'] |
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- prefix_types: None |
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- model: facebook/bart-large |
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- max_length: 512 |
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- max_length_output: 32 |
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- epoch: 4 |
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- batch: 32 |
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- lr: 5e-05 |
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- fp16: False |
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- random_seed: 1 |
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- gradient_accumulation_steps: 4 |
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- label_smoothing: 0.15 |
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|
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The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-large-squad/raw/main/trainer_config.json). |
|
|
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## 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", |
|
} |
|
|
|
``` |
|
|
