
# ai-soco-c++-roberta-tiny-96-clas

## Model description

`ai-soco-c++-roberta-tiny-96` model fine-tuned on [AI-SOCO](https://sites.google.com/view/ai-soco-2020) task.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized from [`ai-soco-c++-roberta-tiny-96`](https://github.com/huggingface/transformers/blob/master/model_cards/aliosm/ai-soco-c++-roberta-tiny-96) model and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset to do text classification.

## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization.

## Eval results

The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.

### BibTeX entry and citation info

```bibtex
@inproceedings{ai-soco-2020-fire,
    title = "Overview of the {PAN@FIRE} 2020 Task on {Authorship Identification of SOurce COde (AI-SOCO)}",
    author = "Fadel, Ali and Musleh, Husam and Tuffaha, Ibraheem and Al-Ayyoub, Mahmoud and Jararweh, Yaser and Benkhelifa, Elhadj and Rosso, Paolo",
    booktitle = "Proceedings of The 12th meeting of the Forum for Information Retrieval Evaluation (FIRE 2020)",
    year = "2020"
}
```

<a href="https://huggingface.co/exbert/?model=aliosm/ai-soco-c++-roberta-tiny-96-clas">
	<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>

{"language": "c++", "license": "mit", "tags": ["exbert", "authorship-identification", "fire2020", "pan2020", "ai-soco", "classification"], "datasets": ["ai-soco"], "metrics": ["accuracy"]}
exbert authorship-identification fire2020 pan2020 ai-soco classification dataset:ai-soco license:mit region:us
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us 


# ai-soco-c++-roberta-tiny-96-clas

## Model description

'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task.

#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.

#### Limitations and bias

The model is limited to C++ programming language only.

## Training data

The model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification.

## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.

## Eval results

The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.

### BibTeX entry and citation info



<a href="URL
	<img width="300px" src="URL
</a>

# ai-soco-c++-roberta-tiny-96-clas ## Model description

'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task. #### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files. #### Limitations and bias

The model is limited to C++ programming language only. ## Training data

The model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification. ## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization. ## Eval results

The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place. ### BibTeX entry and citation info



<a href="URL
	<img width="300px" src="URL
</a>
TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us 
 # ai-soco-c++-roberta-tiny-96-clas ## Model description

'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task. #### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files. #### Limitations and bias

The model is limited to C++ programming language only. ## Training data

The model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification. ## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization. ## Eval results

The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place. ### BibTeX entry and citation info



<a href="URL
	<img width="300px" src="URL
</a>
passage: TAGS
#exbert #authorship-identification #fire2020 #pan2020 #ai-soco #classification #dataset-ai-soco #license-mit #region-us 
# ai-soco-c++-roberta-tiny-96-clas## Model description

'ai-soco-c++-roberta-tiny-96' model fine-tuned on AI-SOCO task.#### How to use

You can use the model directly after tokenizing the text using the provided tokenizer with the model files.#### Limitations and bias

The model is limited to C++ programming language only.## Training data

The model initialized from 'ai-soco-c++-roberta-tiny-96' model and trained using AI-SOCO dataset to do text classification.## Training procedure

The model trained on Google Colab platform using V100 GPU for 10 epochs, 16 batch size, 512 max sequence length (sequences larger than 512 were truncated). Each continues 4 spaces were converted to a single tab character ('\t') before tokenization.## Eval results

The model achieved 91.12%/91.02% accuracy on AI-SOCO task and ranked in the 7th place.### BibTeX entry and citation info



<a href="URL
	<img width="300px" src="URL
</a>