---
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: 0.9553719667577645
    - name: QAAlignedRecall (BERTScore)
      type: qa_aligned_recall_bertscore
      value: 0.9548501701962565
    - name: QAAlignedPrecision (BERTScore)
      type: qa_aligned_precision_bertscore
      value: 0.9559103034487555
    - name: QAAlignedF1Score (MoverScore)
      type: qa_aligned_f1_score_moverscore
      value: 0.708244616864839
    - name: QAAlignedRecall (MoverScore)
      type: qa_aligned_recall_moverscore
      value: 0.7053901691540012
    - name: QAAlignedPrecision (MoverScore)
      type: qa_aligned_precision_moverscore
      value: 0.7112501965782075
  - 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
  - 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_subjqa
      type: tripadvisor
      args: tripadvisor
    metrics:
    - name: BLEU4
      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:
      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: restaurants
      args: restaurants
    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:
      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_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: movies
      args: movies
    metrics:
    - name: BLEU4
      type: bleu4
      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:
      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: 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
---

# Model Card of `lmqg/bart-large-squad`
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).


Please cite our paper if you use the model ([https://arxiv.org/abs/2210.03992](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](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')
# 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`
```python

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](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) | 


### Metrics (QAG)

| Dataset | Type | QA Aligned F1 Score (BERTScore) | QA Aligned F1 Score (MoverScore) | Link |
|:--------|:-----|--------------------------------:|---------------------------------:|-----:|
| [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) | default | 0.955 | 0.708 | [link](https://huggingface.co/lmqg/bart-large-squad/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json) | 
    


### Out-of-domain Metrics
        
| Dataset | Type | BLEU4 | ROUGE-L | METEOR | BERTScore | MoverScore | Link |
|:--------|:-----|------:|--------:|-------:|----------:|-----------:|-----:|
| [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) |
| [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) |
| [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) |
| [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) |
| [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) |
| [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) |
| [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) |
| [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) |
| [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) |
| [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) |


## 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/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",
}

```