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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 = 5141@misc{https://doi.org/10.48550/arxiv.2204.08832,
2 doi = {10.48550/ARXIV.2204.08832},
3 url = {https://arxiv.org/abs/2204.08832},
4 author = {Toraman, Cagri and Yilmaz, Eyup Halit and Şahinuç, Furkan and Ozcelik, Oguzhan},
5 keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
6 title = {Impact of Tokenization on Language Models: An Analysis for Turkish},
7 publisher = {arXiv},
8 year = {2022},
9 copyright = {Creative Commons Attribution Non Commercial Share Alike 4.0 International}
10}