Trainers
Python
Rust
Node
BpeTrainer
class tokenizers.trainers.BpeTrainer
( )
Parameters
- vocab_size (
int, optional) — The size of the final vocabulary, including all tokens and alphabet. - min_frequency (
int, optional) — The minimum frequency a pair should have in order to be merged. - show_progress (
bool, optional) — Whether to show progress bars while training. - special_tokens (
List[Union[str, AddedToken]], optional) — A list of special tokens the model should know of. - limit_alphabet (
int, optional) — The maximum different characters to keep in the alphabet. - initial_alphabet (
List[str], optional) — A list of characters to include in the initial alphabet, even if not seen in the training dataset. If the strings contain more than one character, only the first one is kept. - continuing_subword_prefix (
str, optional) — A prefix to be used for every subword that is not a beginning-of-word. - end_of_word_suffix (
str, optional) — A suffix to be used for every subword that is a end-of-word. - max_token_length (
int, optional) — Prevents creating tokens longer than the specified size. This can help with reducing polluting your vocabulary with highly repetitive tokens like ====== for wikipedia
Trainer capable of training a BPE model
UnigramTrainer
class tokenizers.trainers.UnigramTrainer
( vocab_size = 8000 show_progress = True special_tokens = [] shrinking_factor = 0.75 unk_token = None max_piece_length = 16 n_sub_iterations = 2 )
Parameters
- vocab_size (
int) — The size of the final vocabulary, including all tokens and alphabet. - show_progress (
bool) — Whether to show progress bars while training. - special_tokens (
List[Union[str, AddedToken]]) — A list of special tokens the model should know of. - initial_alphabet (
List[str]) — A list of characters to include in the initial alphabet, even if not seen in the training dataset. If the strings contain more than one character, only the first one is kept. - shrinking_factor (
float) — The shrinking factor used at each step of the training to prune the vocabulary. - unk_token (
str) — The token used for out-of-vocabulary tokens. - max_piece_length (
int) — The maximum length of a given token. - n_sub_iterations (
int) — The number of iterations of the EM algorithm to perform before pruning the vocabulary.
Trainer capable of training a Unigram model
WordLevelTrainer
class tokenizers.trainers.WordLevelTrainer
( )
Parameters
- vocab_size (
int, optional) — The size of the final vocabulary, including all tokens and alphabet. - min_frequency (
int, optional) — The minimum frequency a pair should have in order to be merged. - show_progress (
bool, optional) — Whether to show progress bars while training. - special_tokens (
List[Union[str, AddedToken]]) — A list of special tokens the model should know of.
Trainer capable of training a WorldLevel model
WordPieceTrainer
class tokenizers.trainers.WordPieceTrainer
( vocab_size = 30000 min_frequency = 0 show_progress = True special_tokens = [] limit_alphabet = None initial_alphabet = [] continuing_subword_prefix = '##' end_of_word_suffix = None )
Parameters
- vocab_size (
int, optional) — The size of the final vocabulary, including all tokens and alphabet. - min_frequency (
int, optional) — The minimum frequency a pair should have in order to be merged. - show_progress (
bool, optional) — Whether to show progress bars while training. - special_tokens (
List[Union[str, AddedToken]], optional) — A list of special tokens the model should know of. - limit_alphabet (
int, optional) — The maximum different characters to keep in the alphabet. - initial_alphabet (
List[str], optional) — A list of characters to include in the initial alphabet, even if not seen in the training dataset. If the strings contain more than one character, only the first one is kept. - continuing_subword_prefix (
str, optional) — A prefix to be used for every subword that is not a beginning-of-word. - end_of_word_suffix (
str, optional) — A suffix to be used for every subword that is a end-of-word.
Trainer capable of training a WordPiece model