
# Model Card of `lmqg/bart-base-squad-qg`
This model is fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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-base](https://huggingface.co/facebook/bart-base)   
- **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-base-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-base-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-base-squad-qg/raw/main/eval/metric.first.sentence.paragraph_answer.question.lmqg_qg_squad.default.json) 

|            |   Score | Type    | Dataset                                                        |
|:-----------|--------:|:--------|:---------------------------------------------------------------|
| BERTScore  |   90.87 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_1     |   56.92 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_2     |   40.98 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_3     |   31.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| Bleu_4     |   24.68 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| METEOR     |   26.05 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| MoverScore |   64.47 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| ROUGE_L    |   52.66 | 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-base-squad-qg/raw/main/eval/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   95.49 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   70.38 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   95.55 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   70.67 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   95.44 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   70.1  | 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-base-squad-ae`](https://huggingface.co/lmqg/bart-base-squad-ae). [raw metric file](https://huggingface.co/lmqg/bart-base-squad-qg/raw/main/eval_pipeline/metric.first.answer.paragraph.questions_answers.lmqg_qg_squad.default.lmqg_bart-base-squad-ae.json)

|                                 |   Score | Type    | Dataset                                                        |
|:--------------------------------|--------:|:--------|:---------------------------------------------------------------|
| QAAlignedF1Score (BERTScore)    |   92.84 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedF1Score (MoverScore)   |   64.24 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (BERTScore)  |   92.75 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedPrecision (MoverScore) |   64.46 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (BERTScore)     |   92.95 | default | [lmqg/qg_squad](https://huggingface.co/datasets/lmqg/qg_squad) |
| QAAlignedRecall (MoverScore)    |   64.11 | 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 | 90.49 | 5.82 | 21.27 | 60.27 | 23.82 | [link](https://huggingface.co/lmqg/bart-base-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 | 93.07 | 10.73 | 26.23 | 65.67 | 28.44 | [link](https://huggingface.co/lmqg/bart-base-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 | 92.36 | 7.65 | 24.43 | 63.69 | 23.9 | [link](https://huggingface.co/lmqg/bart-base-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 | 90.57 | 5.38 | 20.4 | 60.14 | 21.41 | [link](https://huggingface.co/lmqg/bart-base-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 | 87.75 | 0.0 | 11.52 | 55.21 | 10.77 | [link](https://huggingface.co/lmqg/bart-base-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 | 87.6 | 0.0 | 14.87 | 56.07 | 14.29 | [link](https://huggingface.co/lmqg/bart-base-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 | 87.38 | 0.6 | 15.53 | 56.63 | 12.49 | [link](https://huggingface.co/lmqg/bart-base-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 | 87.73 | 1.08 | 12.86 | 55.55 | 13.9 | [link](https://huggingface.co/lmqg/bart-base-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 | 87.71 | 0.0 | 11.47 | 54.91 | 12.16 | [link](https://huggingface.co/lmqg/bart-base-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 | 88.78 | 1.02 | 13.92 | 55.91 | 13.41 | [link](https://huggingface.co/lmqg/bart-base-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-base
 - max_length: 512
 - max_length_output: 32
 - epoch: 7
 - batch: 32
 - lr: 0.0001
 - fp16: False
 - random_seed: 1
 - gradient_accumulation_steps: 8
 - label_smoothing: 0.15

The full configuration can be found at [fine-tuning config file](https://huggingface.co/lmqg/bart-base-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-base-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": 24.68, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 52.66, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 26.05, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 90.87, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 64.47, "name": "MoverScore (Question Generation)"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_with_gold_answer_gold_answer", "value": 95.49, "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": 95.44, "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": 95.55, "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": 70.38, "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": 70.1, "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": 70.67, "name": "QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer]"}, {"type": "qa_aligned_f1_score_bertscore_question_answer_generation_gold_answer", "value": 92.84, "name": "QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_bertscore_question_answer_generation_gold_answer", "value": 92.95, "name": "QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_bertscore_question_answer_generation_gold_answer", "value": 92.75, "name": "QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_f1_score_moverscore_question_answer_generation_gold_answer", "value": 64.24, "name": "QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_recall_moverscore_question_answer_generation_gold_answer", "value": 64.11, "name": "QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer]"}, {"type": "qa_aligned_precision_moverscore_question_answer_generation_gold_answer", "value": 64.46, "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": 0.05824165264328302, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.23816054441894524, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.2126541577267873, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9049284884636415, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6026811246610306, "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": 0.10732253983426589, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2843539251435107, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.26233713078026283, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9307303692241476, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.656720781293701, "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": 0.07645313983751752, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2390325229516282, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.244330483594333, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.9235989114144583, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6368628469746445, "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": 0.053789810023704955, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.2141155595451475, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.20395821936787215, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.905714302466044, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.6013927660089013, "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": 1.4952813458186383e-10, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.10769136267285535, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.11520101781020654, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8774975922095214, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5520873074919223, "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": 1.3766381900873328e-06, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.14287460464803423, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.14866637711177003, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8759880110997111, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5607199201429516, "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": 0.006003840641121225, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1248840598199836, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1553374628831024, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8737966828346252, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5662545638649026, "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": 0.0108258720771249, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1389815289507374, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.12855849168399078, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8773110466344016, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5555164603510797, "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": 1.7873892359263582e-10, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.12160976589996819, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1146979295288459, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8771339668070569, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5490739019998478, "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": 0.010174680918435602, "name": "BLEU4 (Question Generation)"}, {"type": "rouge_l_question_generation", "value": 0.1341425139885307, "name": "ROUGE-L (Question Generation)"}, {"type": "meteor_question_generation", "value": 0.1391725168440533, "name": "METEOR (Question Generation)"}, {"type": "bertscore_question_generation", "value": 0.8877592491739579, "name": "BERTScore (Question Generation)"}, {"type": "moverscore_question_generation", "value": 0.5590591813016728, "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-base-squad-qg'
=======================================


This model is fine-tuned version of facebook/bart-base for question generation task on the lmqg/qg\_squad (dataset\_name: default) via 'lmqg'.


### Overview


* Language model: facebook/bart-base
* 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-base-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-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 32
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.



### Overview


* Language model: facebook/bart-base
* 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-base-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-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 32
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* 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-base
* 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-base-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-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 32
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* 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-base
* 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-base-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-base
* max\_length: 512
* max\_length\_output: 32
* epoch: 7
* batch: 32
* lr: 0.0001
* fp16: False
* random\_seed: 1
* gradient\_accumulation\_steps: 8
* label\_smoothing: 0.15


The full configuration can be found at fine-tuning config file.