README.md

---
language: 
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
datasets:
- oscar
---

# RoBERTa Turkish medium BPE 28k (uncased)

Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. 
The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. 

Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is BPE. Vocabulary size is 28.6k. 

The details and performance comparisons can be found at this paper: 
https://arxiv.org/abs/2204.08832

The following code can be used for model loading and tokenization, example max length (514) can be changed:
```
	model = AutoModel.from_pretrained([model_path])
	#for sequence classification:
	#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])

	tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path])
	tokenizer.mask_token = "[MASK]"
	tokenizer.cls_token = "[CLS]"
	tokenizer.sep_token = "[SEP]"
	tokenizer.pad_token = "[PAD]"
	tokenizer.unk_token = "[UNK]"
	tokenizer.bos_token = "[CLS]"
	tokenizer.eos_token = "[SEP]"
	tokenizer.model_max_length = 514
```
### BibTeX entry and citation info
```bibtex
@misc{https://doi.org/10.48550/arxiv.2204.08832,
  doi = {10.48550/ARXIV.2204.08832},
  url = {https://arxiv.org/abs/2204.08832},
  author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
  keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
  publisher = {arXiv},
  year = {2022},
  copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
}
```