--- language: "c++" tags: - exbert - authorship-identification - fire2020 - pan2020 - ai-soco license: "mit" datasets: - ai-soco metrics: - perplexity --- # ai-soco-c++-roberta-tiny ## Model description From scratch pre-trained RoBERTa model with 1 layers and 12 attention heads using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which consists of C++ codes crawled from CodeForces website. ## Intended uses & limitations The model can be used to do code classification, authorship identification and other downstream tasks on C++ programming language. #### 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 randomly and trained using [AI-SOCO](https://sites.google.com/view/ai-soco-2020) dataset which contains 100K C++ source codes. ## Training procedure The model trained on Google Colab platform with 8 TPU cores for 200 epochs, 32\*8 batch size, 512 max sequence length and MLM objective. Other parameters were defaulted to the values mentioned in [`run_language_modelling.py`](https://github.com/huggingface/transformers/blob/master/examples/language-modeling/run_language_modeling.py) script. Each continues 4 spaces were converted to a single tab character (`\t`) before tokenization. ### 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" } ```