README.md

metadata

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. The details of the data can be found at this paper: https://arxiv.org/...

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: https://arxiv.org/...

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

@article{}