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--- |
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language: |
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- tr |
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tags: |
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- roberta |
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license: cc-by-nc-sa-4.0 |
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datasets: |
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- oscar |
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--- |
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# RoBERTa Turkish medium WordPiece 44k (uncased) |
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Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. |
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The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned. |
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Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 44.5k. |
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The details can be found at this paper: |
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https://arxiv.org/abs/2204.08832 |
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The following code can be used for model loading and tokenization, example max length (514) can be changed: |
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``` |
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model = AutoModel.from_pretrained([model_path]) |
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#for sequence classification: |
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#model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes]) |
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tokenizer = PreTrainedTokenizerFast(tokenizer_file=[file_path]) |
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tokenizer.mask_token = "[MASK]" |
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tokenizer.cls_token = "[CLS]" |
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tokenizer.sep_token = "[SEP]" |
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tokenizer.pad_token = "[PAD]" |
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tokenizer.unk_token = "[UNK]" |
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tokenizer.bos_token = "[CLS]" |
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tokenizer.eos_token = "[SEP]" |
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tokenizer.model_max_length = 514 |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@misc{https://doi.org/10.48550/arxiv.2204.08832, |
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doi = {10.48550/ARXIV.2204.08832}, |
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url = {https://arxiv.org/abs/2204.08832}, |
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author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Impact of Tokenization on Language Models: An Analysis for Turkish}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International} |
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} |
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``` |
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