
# 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",
}

```

{"language": "en", "license": "cc-by-4.0", "tags": ["question generation"], "datasets": ["lmqg/qg_squad"], "metrics": ["bleu4", "meteor", "rouge-l", "bertscore", "moverscore"], "pipeline_tag": "text2text-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": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squad", "type": "default", "args": "default"}, "metrics": [{"type": "bleu4_question_generation", "value": 26.17, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 53.85, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 27.07, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 91.0, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 64.99, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 95.54, "name": "QAAlignedF1Score-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": "QAAlignedRecall-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": "QAAlignedPrecision-BERTScore (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": "QAAlignedF1Score-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": "QAAlignedRecall-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": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer", "value": 93.23, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_gold_answer", "value": 93.35, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_gold_answer", "value": 93.13, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer", "value": 64.76, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_gold_answer", "value": 64.63, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_gold_answer", "value": 64.98, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer]"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "amazon", "args": "amazon"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.06530369842068952, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.25030985091008146, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.2229994442645732, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9092814804525936, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6086538514008419, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "new_wiki", "args": "new_wiki"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.11118273173452982, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2967546690273089, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.27315087810722966, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9322739617807421, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6623000084761579, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "nyt", "args": "nyt"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.08117757543966063, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.25292097720734297, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.25254205113198686, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9249009759439454, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6406329128556304, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_squadshifts", "type": "reddit", "args": "reddit"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.059525104157825456, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.22365090580055863, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.21499800504546457, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9095144685254328, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6059332247878408, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "books", "args": "books"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.006278914808207679, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.12368226019088967, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.11576293675813865, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8807110440044503, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5555905941686486, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "electronics", "args": "electronics"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.00866799444965211, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1601628874804186, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.15348605312210778, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8783386920680519, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5634845371093992, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "grocery", "args": "grocery"}, "metrics": [{"type": "bleu4_question_generation", "value": 0.00528043272450429, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.12343711316491492, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.15133496445452477, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8778951253890991, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5701949938103265, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "movies", "args": "movies"}, "metrics": [{"type": "bleu4_question_generation", "value": 1.0121579426501661e-06, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.12508697028506718, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.11862284941640638, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8748829724726739, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5528899173535703, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "restaurants", "args": "restaurants"}, "metrics": [{"type": "bleu4_question_generation", "value": 1.1301750984972448e-06, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.13083168975354642, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.12419733006916912, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8797711839570719, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5542757411268555, "name": "MoverScore (Question Generation)"}]}, {"task": {"type": "text2text-generation", "name": "Text2text Generation"}, "dataset": {"name": "lmqg/qg_subjqa", "type": "tripadvisor", "args": "tripadvisor"}, "metrics": [{"type": "bleu4_question_generation", "value": 8.380171318718442e-07, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1402922852924756, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1372146070365174, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8891002409937424, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5604572211470809, "name": "MoverScore (Question Generation)"}]}]}]}
transformers pytorch bart text2text-generation question generation en dataset:lmqg/qg_squad arxiv:2210.03992 license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 

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: URL
* Repository: URL
* Paper: URL


### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/bart-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.



### Overview


* Language model: facebook/bart-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/bart-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.
TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 ### Overview


* Language model: facebook/bart-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL ### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/bart-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.
passage: TAGS
#transformers #pytorch #bart #text2text-generation #question generation #en #dataset-lmqg/qg_squad #arxiv-2210.03992 #license-cc-by-4.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
### Overview


* Language model: facebook/bart-large
* Language: en
* Training data: lmqg/qg\_squad (default)
* Online Demo: URL
* Repository: URL
* Paper: URL### Usage


* With 'lmqg'
* With 'transformers'


Evaluation
----------


* *Metric (Question Generation)*: raw metric file



* *Metric (Question & Answer Generation, Reference Answer)*: Each question is generated from *the gold answer*. raw metric file



* *Metric (Question & Answer Generation, Pipeline Approach)*: Each question is generated on the answer generated by 'lmqg/bart-large-squad-ae'. raw metric file



* *Metrics (Question Generation, Out-of-Domain)*



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.