mindspore.dataset.text.transforms.WordpieceTokenizer

class mindspore.dataset.text.transforms.WordpieceTokenizer(vocab, suffix_indicator='##', max_bytes_per_token=100, unknown_token='[UNK]', with_offsets=False)[source]

Tokenize the input text to subword tokens.

Parameters

vocab (Vocab) – Vocabulary used to look up words.
suffix_indicator (str, optional) – Prefix flags used to indicate subword suffixes. Default: ‘##’.
max_bytes_per_token (int, optional) – The maximum length of tokenization, words exceeding this length will not be split. Default: 100.
unknown_token (str, optional) – The output for unknown words. When set to an empty string, the corresponding unknown word will be directly returned as the output. Otherwise, the set string will be returned as the output. Default: ‘[UNK]’.
with_offsets (bool, optional) – Whether to return the offsets of tokens. Default: False.

Raises

TypeError – If vocab is not of type mindspore.dataset.text.Vocab.
TypeError – If suffix_indicator is not of type str.
TypeError – If max_bytes_per_token is not of type int.
TypeError – If unknown_token is not of type str.
TypeError – If with_offsets is not of type bool.
ValueError – If max_bytes_per_token is negative.

Supported Platforms:: CPU

Examples

>>> vocab_list = ["book", "cholera", "era", "favor", "##ite", "my", "is", "love", "dur", "##ing", "the"]
>>> vocab = text.Vocab.from_list(vocab_list)
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> tokenizer_op = text.WordpieceTokenizer(vocab=vocab, unknown_token='[UNK]',
...                                        max_bytes_per_token=100, with_offsets=False)
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op)
>>> # If with_offsets=True, then output three columns {["token", dtype=str], ["offsets_start", dtype=uint32],
>>> #                                                   ["offsets_limit", dtype=uint32]}
>>> tokenizer_op = text.WordpieceTokenizer(vocab=vocab, unknown_token='[UNK]',
...                                       max_bytes_per_token=100, with_offsets=True)
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op, input_columns=["text"],
...                                           output_columns=["token", "offsets_start", "offsets_limit"],
...                                           column_order=["token", "offsets_start", "offsets_limit"])