hezar.preprocessors.tokenizers.bpe module

class hezar.preprocessors.tokenizers.bpe.BPEConfig(max_length: 'int' = 'deprecated', truncation: 'str' = 'deprecated', truncation_side: str = 'right', padding: 'str' = 'deprecated', padding_side: str = 'right', stride: int = 0, pad_to_multiple_of: int = 0, pad_token_type_id: 'int' = 0, bos_token: str = '<s>', eos_token: str = '</s>', unk_token: str = '<unk>', sep_token: str = '<sep>', pad_token: str = '<pad>', cls_token: str = '<cls>', mask_token: str = '<mask>', additional_special_tokens: List[str] = None, dropout: float = None, continuing_subword_prefix: str = '', end_of_word_suffix: str = '', fuse_unk: bool = False, vocab_size: int = 30000, min_frequency: int = 2, limit_alphabet: int = 1000, initial_alphabet: list = <factory>, show_progress: bool = True)

Bases: TokenizerConfig

additional_special_tokens: List[str] = None

bos_token: str = '<s>'

cls_token: str = '<cls>'

continuing_subword_prefix: str = ''

dropout: float = None

end_of_word_suffix: str = ''

eos_token: str = '</s>'

fuse_unk: bool = False

initial_alphabet: list

limit_alphabet: int = 1000

mask_token: str = '<mask>'

min_frequency: int = 2

name: str = 'bpe_tokenizer'

pad_to_multiple_of: int = 0

pad_token: str = '<pad>'

padding_side: str = 'right'

sep_token: str = '<sep>'

show_progress: bool = True

stride: int = 0

truncation_side: str = 'right'

unk_token: str = '<unk>'

vocab_size: int = 30000

class hezar.preprocessors.tokenizers.bpe.BPETokenizer(config, tokenizer_file=None, **kwargs)

Bases: Tokenizer

A standard Byte-level BPE tokenizer using 🤗HuggingFace Tokenizers

Parameters:

• config – Preprocessor config for the tokenizer

• **kwargs – Extra/manual config parameters

build()

Build the tokenizer.

Returns:

The built tokenizer.

Return type:

HFTokenizer

required_backends: List[str | Backends] = [Backends.TOKENIZERS]

token_ids_name = 'token_ids'

tokenizer_config_filename = 'tokenizer_config.yaml'

tokenizer_filename = 'tokenizer.json'

train(files: List[str], **train_kwargs)

Train the model using the given files

train_from_iterator(dataset: List[str], **train_kwargs)

Train the model using the given files