metadata

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

RoBERTa Turkish medium Morph-level 7k (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 Morph-level, which means that text is split according to a Turkish morphological analyzer (Zemberek). Vocabulary size is 7.5k.

Note that this model needs a preprocessing step before running, because the tokenizer file is not a morphological anaylzer. That is, the test dataset can not be split into morphemes with the tokenizer file. The user needs to process any test dataset by a Turkish morphological analyzer (Zemberek in this case) before running evaluation.

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

The following code can be used for model loading and tokenization, example max length (514) 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 = 514

BibTeX entry and citation info

@article{}