README.md

metadata

language:
  - tr
tags:
  - roberta
license: cc-by-nc-sa-4.0
datasets:
  - oscar

RoBERTa Turkish medium Character-level 16k (uncased)

Pretrained model on Turkish language using a masked language modeling (MLM) objective. The model is uncased. The pretrained corpus is OSCAR's Turkish split, but it is further filtered and cleaned.

Model architecture is similar to bert-medium (8 layers, 8 heads, and 512 hidden size). Tokenization algorithm is Character-level, which means that text is split by individual characters. Vocabulary size is 16.7k.

Note that this model does not include a tokenizer file, because it uses ByT5Tokenizer. The following code can be used for model loading and tokenization, example max length(1024) can be changed:

    model = AutoModel.from_pretrained([model_path])
    #for sequence classification:
    #model = AutoModelForSequenceClassification.from_pretrained([model_path], num_labels=[num_classes])

    tokenizer = ByT5Tokenizer.from_pretrained("google/byt5-small")
    tokenizer.mask_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][0]
    tokenizer.cls_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][1]
    tokenizer.bos_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][1]
    tokenizer.sep_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][2]
    tokenizer.eos_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][2]
    tokenizer.pad_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][3]
    tokenizer.unk_token = tokenizer.special_tokens_map_extended['additional_special_tokens'][3]
    tokenizer.model_max_length = 1024

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

BibTeX entry and citation info

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