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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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--- |
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# RoBERTweetTurkCovid (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 a Turkish tweets collection related to COVID-19. The details of the data can be found at this paper: |
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https://arxiv.org/... |
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Model architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 30k. |
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The details of pretraining can be found at this paper: |
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https://arxiv.org/... |
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The following code can be used for model loading and tokenization, example max length (768) 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 = 768 |
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``` |
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### BibTeX entry and citation info |
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```bibtex |
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@article{} |
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``` |
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