--- language: - tr tags: - roberta license: cc-by-nc-sa-4.0 datasets: - oscar --- # RoBERTa Turkish medium Word-level 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 Word-level, which means text is split by white space. Vocabulary size is 66.7k. The details 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} } ```