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

# RoBERTa Turkish medium WordPiece 66k (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 WordPiece. Vocabulary size is 66.7k. 

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

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
@article{}
```