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

# RoBERTa Turkish medium Character-level (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 Character-level, which means that text is split by individual characters. Vocabulary size is 384. 

## Note that this model does not include a tokenizer file, because it uses ByT5Tokenizer. The following code can be used for model loading and tokenization, example max length(1024) can be changed:
```
	model = AutoModel.from_pretrained([model_path])
	#for sequence classification:
	#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])

	tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
	tokenizer.mask_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][0]
	tokenizer.cls_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][1]
	tokenizer.bos_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][1]
	tokenizer.sep_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][2]
	tokenizer.eos_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][2]
	tokenizer.pad_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][3]
	tokenizer.unk_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][3]
	tokenizer.model_max_length = 1024
```

The details can be found at this paper: 
https://arxiv.org/...

### BibTeX entry and citation info
```bibtex
@article{}
```