mindspore.dataset.text.JiebaTokenizer

class mindspore.dataset.text.JiebaTokenizer(hmm_path, mp_path, mode=JiebaMode.MIX, with_offsets=False)[源代码]

使用Jieba分词器对中文字符串进行分词。

说明

隐式马尔可夫模型（Hidden Markov Model）分词和最大概率法（Max Probability）分词所使用的词典文件可通过 cppjieba开源仓获取，请保证文件的有效性与完整性。

参数：

hmm_path (str) - 隐式马尔可夫模型分词所使用的词典文件路径。
mp_path (str) - 最大概率法分词所使用的词典文件路径。
mode (JiebaMode, 可选) - 想要使用的分词算法。可选值详见 JiebaMode 。
with_offsets (bool, 可选) - 是否输出各Token在原字符串中的起始和结束偏移量。默认值： False 。

异常：

TypeError - 当 hmm_path 不为str类型。
TypeError - 当 mp_path 不为str类型。
TypeError - 当 mode 不为 JiebaMode 类型。
TypeError - 当 with_offsets 不为bool类型。

支持平台：

CPU

样例：

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> from mindspore.dataset.text import JiebaMode
>>>
>>> # Use the transform in dataset pipeline mode
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=["床前明月光"], column_names=["text"])
>>>
>>> # 1) If with_offsets=False, return one data column {["text", dtype=str]}
>>> # The paths to jieba_hmm_file and jieba_mp_file can be downloaded directly from the mindspore repository.
>>> # Refer to https://gitee.com/mindspore/mindspore/blob/v2.3.0-rc2/tests/ut/data/dataset/jiebadict/hmm_model.utf8
>>> # and https://gitee.com/mindspore/mindspore/blob/v2.3.0-rc2/tests/ut/data/dataset/jiebadict/jieba.dict.utf8
>>> jieba_hmm_file = "tests/ut/data/dataset/jiebadict/hmm_model.utf8"
>>> jieba_mp_file = "tests/ut/data/dataset/jiebadict/jieba.dict.utf8"
>>> tokenizer_op = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP, with_offsets=False)
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=tokenizer_op)
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["text"])
['床' '前' '明月光']
>>>
>>> # 2) If with_offsets=True, return three columns {["token", dtype=str], ["offsets_start", dtype=uint32],
>>> #                                                ["offsets_limit", dtype=uint32]}
>>> numpy_slices_dataset = ds.NumpySlicesDataset(data=["床前明月光"], column_names=["text"])
>>> tokenizer_op = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP, with_offsets=True)
>>> numpy_slices_dataset = numpy_slices_dataset.map(operations=tokenizer_op, input_columns=["text"],
...                                                 output_columns=["token", "offsets_start", "offsets_limit"])
>>> for item in numpy_slices_dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
...     print(item["token"], item["offsets_start"], item["offsets_limit"])
['床' '前' '明月光'] [0 3 6] [ 3  6 15]
>>>
>>> # Use the transform in eager mode
>>> data = "床前明月光"
>>> output = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP)(data)
>>> print(output)
['床' '前' '明月光']

教程样例：

文本变换样例库

add_dict(user_dict)[源代码]

添加指定的词映射字典到Vocab中。

参数：

user_dict (Union[str, dict[str, int]]) - 待添加到Vocab中的词映射。若输入类型为str，表示存储待添加词映射的文件路径，文件的每一行需包含两个字段，间隔一个空格，其中第一个字段表示词本身，第二个字段需为数字，表示词频。无效的行将被忽略，且不返回错误或告警。若输入类型为dict[str, int]，表示存储待添加词映射的字典，其中键名为词本身，键值为词频。

样例：

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> from mindspore.dataset.text import JiebaMode
>>>
>>> jieba_hmm_file = "/path/to/jieba/hmm/file"
>>> jieba_mp_file = "/path/to/jieba/mp/file"
>>> user_dict = {"男默女泪": 10}
>>> jieba_op = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP)
>>> jieba_op.add_dict(user_dict)
>>>
>>> text_file_list = ["/path/to/text_file_dataset_file"]
>>> text_file_dataset = ds.TextFileDataset(dataset_files=text_file_list)
>>> text_file_dataset = text_file_dataset.map(operations=jieba_op, input_columns=["text"])

add_word(word, freq=None)[源代码]

添加一个指定的词映射到Vocab中。

参数：

word (str) - 待添加到Vocab中的词。
freq (int，可选) - 待添加词的词频。词频越高，单词被分词的机会就越大。默认值： None ，使用默认词频。

样例：

>>> import mindspore.dataset as ds
>>> import mindspore.dataset.text as text
>>> from mindspore.dataset.text import JiebaMode
>>>
>>> jieba_hmm_file = "/path/to/jieba/hmm/file"
>>> jieba_mp_file = "/path/to/jieba/mp/file"
>>> jieba_op = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP)
>>> sentence_piece_vocab_file = "/path/to/sentence/piece/vocab/file"
>>> with open(sentence_piece_vocab_file, 'r') as f:
...     for line in f:
...         word = line.split(',')[0]
...         jieba_op.add_word(word)
>>>
>>> text_file_list = ["/path/to/text_file_dataset_file"]
>>> text_file_dataset = ds.TextFileDataset(dataset_files=text_file_list)
>>> text_file_dataset = text_file_dataset.map(operations=jieba_op, input_columns=["text"])