readme: ---
  language: 
  - tr
  tags:
  - roberta
  license: cc-by-nc-sa-4.0
  datasets:
  - oscar
  ---
  
  # RoBERTa Turkish medium BPE 16k (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 16.7k. 
  
  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}
  }
  ```
  