mindspore.dataset.text.SentencePieceTokenizer

class mindspore.dataset.text.SentencePieceTokenizer(mode, out_type)[源代码]

使用SentencePiece分词器对字符串进行分词。

参数：

mode (Union[str, SentencePieceVocab]) - SentencePiece模型。如果输入是字符串类型，则代表要加载的SentencePiece模型文件的路径；如果输入是SentencePieceVocab类型，则要求是构造好的 SentencePieceVocab 对象。
out_type (SPieceTokenizerOutType) - 分词器输出的类型，可以取值为 SPieceTokenizerOutType.STRING 或 SPieceTokenizerOutType.INT。
- SPieceTokenizerOutType.STRING，表示SentencePice分词器的输出类型是str。
- SPieceTokenizerOutType.INT，表示SentencePice分词器的输出类型是int。

异常：

TypeError - 参数 mode 的类型不是字符串或 mindspore.dataset.text.SentencePieceVocab 。
TypeError - 参数 out_type 的类型不是 mindspore.dataset.text.SPieceTokenizerOutType 。

支持平台：

CPU

样例：

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> from mindspore.dataset.text import SentencePieceModel, SPieceTokenizerOutType
>>>
>>> # Use the transform in dataset pipeline mode
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=['Hello world'], column_names=["text"])
>>> # The paths to sentence_piece_vocab_file can be downloaded directly from the mindspore repository. Refer to
>>> # https://gitee.com/mindspore/mindspore/blob/r2.3.1/tests/ut/data/dataset/test_sentencepiece/vocab.txt
>>> sentence_piece_vocab_file = "tests/ut/data/dataset/test_sentencepiece/vocab.txt"
>>> vocab = text.SentencePieceVocab.from_file([sentence_piece_vocab_file], 512, 0.9995,
...                                            SentencePieceModel.UNIGRAM, {})
>>> tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=tokenizer)
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["text"])
['▁H' 'e' 'l' 'lo' '▁w' 'o' 'r' 'l' 'd']
>>>
>>> # Use the transform in eager mode
>>> data = "Hello world"
>>> vocab = text.SentencePieceVocab.from_file([sentence_piece_vocab_file], 100, 0.9995,
...                                           SentencePieceModel.UNIGRAM, {})
>>> output = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)(data)
>>> print(output)
['▁' 'H' 'e' 'l' 'l' 'o' '▁' 'w' 'o' 'r' 'l' 'd']

教程样例：

文本变换样例库