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---
language:
- tr
tags:
- roberta
license: cc-by-nc-sa-4.0
---
# RoBERTweetTurkCovid (uncased)
Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased.
The pretrained corpus is a Turkish tweets collection related to COVID-19.
Model architecture is similar to RoBERTa-base (12 layers, 12 heads, and 768 hidden size). Tokenization algorithm is WordPiece. Vocabulary size is 30k.
The details of pretraining can be found at this paper:
```bibtex
@InProceedings{clef-checkthat:2022:task1:oguzhan,
author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Umitcan Sahin},
title = "{ARC-NLP at CheckThat! 2022:} Contradiction for Harmful Tweet Detection",
year = {2022},
booktitle = "Working Notes of {CLEF} 2022 - Conference and Labs of the Evaluation Forum",
editor = {Faggioli, Guglielmo andd Ferro, Nicola and Hanbury, Allan and Potthast, Martin},
series = {CLEF~'2022},
address = {Bologna, Italy},
}
```
The following code can be used for model loading and tokenization, example max length (768) 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 = 768
```
### BibTeX entry and citation info
```bibtex
@InProceedings{clef-checkthat:2022:task1:oguzhan,
author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Umitcan Sahin},
title = "{ARC-NLP at CheckThat! 2022:} Contradiction for Harmful Tweet Detection",
year = {2022},
booktitle = "Working Notes of {CLEF} 2022 - Conference and Labs of the Evaluation Forum",
editor = {Faggioli, Guglielmo andd Ferro, Nicola and Hanbury, Allan and Potthast, Martin},
series = {CLEF~'2022},
address = {Bologna, Italy},
}
```
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