mindspore.dataset.text.transforms.BasicTokenizer

class mindspore.dataset.text.transforms.BasicTokenizer(lower_case=False, keep_whitespace=False, normalization_form=NormalizeForm.NONE, preserve_unused_token=True, with_offsets=False)

Tokenize the input UTF-8 encoded string by specific rules.

Note

BasicTokenizer is not supported on Windows platform yet.

Parameters

• lower_case (bool, optional) – Whether to perform lowercase processing on the text. If True, will fold the text to lower case and strip accented characters. If False, will only perform normalization on the text, with mode specified by normalization_form. Default: False.

• keep_whitespace (bool, optional) – If True, the whitespace will be kept in the output. Default: False.

• normalization_form (NormalizeForm, optional) –

  Unicode normalization forms, only valid when lower_case is False, can be NormalizeForm.NONE, NormalizeForm.NFC, NormalizeForm.NFKC, NormalizeForm.NFD or NormalizeForm.NFKD. Default: NormalizeForm.NONE.

  • NormalizeForm.NONE, no normalization.

  • NormalizeForm.NFC, Canonical Decomposition, followed by Canonical Composition.

  • NormalizeForm.NFKC, Compatibility Decomposition, followed by Canonical Composition.

  • NormalizeForm.NFD, Canonical Decomposition.

  • NormalizeForm.NFKD, Compatibility Decomposition.

• preserve_unused_token (bool, optional) – Whether to preserve special tokens. If True, will not split special tokens like ‘[CLS]’, ‘[SEP]’, ‘[UNK]’, ‘[PAD]’, ‘[MASK]’. Default: True.

• with_offsets (bool, optional) – Whether to return the offsets of tokens. Default: False.

Raises

• TypeError – If lower_case is not of type bool.

• TypeError – If keep_whitespace is not of type bool.

• TypeError – If normalization_form is not of type mindspore.dataset.text.NormalizeForm.

• TypeError – If preserve_unused_token is not of type bool.

• TypeError – If with_offsets is not of type bool.

• RuntimeError – If dtype of input Tensor is not str.

Supported Platforms:

CPU

Examples

>>> from mindspore.dataset.text import NormalizeForm
>>>
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> tokenizer_op = text.BasicTokenizer(lower_case=False,
...                                    keep_whitespace=False,
...                                    normalization_form=NormalizeForm.NONE,
...                                    preserve_unused_token=True,
...                                    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.BasicTokenizer(lower_case=False,
...                                    keep_whitespace=False,
...                                    normalization_form=NormalizeForm.NONE,
...                                    preserve_unused_token=True,
...                                    with_offsets=True)
>>> text_file_dataset_1 = text_file_dataset_1.map(operations=tokenizer_op, input_columns=["text"],
...                                               output_columns=["token", "offsets_start",
...                                                               "offsets_limit"],
...                                               column_order=["token", "offsets_start",
...                                                             "offsets_limit"])