
# 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},
}
```

{"language": ["tr"], "license": "cc-by-nc-sa-4.0", "tags": ["roberta"]}
transformers pytorch roberta fill-mask tr license:cc-by-nc-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 


# 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: 


The following code can be used for model loading and tokenization, example max length (768) can be changed:


### BibTeX entry and citation info


# 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: 


The following code can be used for model loading and tokenization, example max length (768) can be changed: ### BibTeX entry and citation info
TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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: 


The following code can be used for model loading and tokenization, example max length (768) can be changed: ### BibTeX entry and citation info
passage: TAGS
#transformers #pytorch #roberta #fill-mask #tr #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# 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: 


The following code can be used for model loading and tokenization, example max length (768) can be changed:### BibTeX entry and citation info