mindspore.dataset.text.transforms.JiebaTokenizer

class mindspore.dataset.text.transforms.JiebaTokenizer(hmm_path, mp_path, mode=JiebaMode.MIX, with_offsets=False)

Tokenize Chinese string into words based on dictionary.

Note

The integrity of the HMMSEgment algorithm and MPSegment algorithm files must be confirmed.

Parameters

• hmm_path (str) – Dictionary file is used by HMMSegment algorithm. The dictionary can be obtained on the official website of cppjieba.

• mp_path (str) – Dictionary file is used by MPSegment algorithm. The dictionary can be obtained on the official website of cppjieba.

• mode (JiebaMode, optional) –

  Valid values can be any of [JiebaMode.MP, JiebaMode.HMM, JiebaMode.MIX](default=JiebaMode.MIX).

  • JiebaMode.MP, tokenize with MPSegment algorithm.

  • JiebaMode.HMM, tokenize with Hidden Markov Model Segment algorithm.

  • JiebaMode.MIX, tokenize with a mix of MPSegment and HMMSegment algorithm.

• with_offsets (bool, optional) – Whether or not output offsets of tokens (default=False).

Examples

>>> from mindspore.dataset.text import JiebaMode
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> jieba_hmm_file = "/path/to/jieba/hmm/file"
>>> jieba_mp_file = "/path/to/jieba/mp/file"
>>> tokenizer_op = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP, with_offsets=False)
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer_op)
>>> # If with_offsets=False, then output three columns {["token", dtype=str], ["offsets_start", dtype=uint32],
>>> #                                                   ["offsets_limit", dtype=uint32]}
>>> tokenizer_op = text.JiebaTokenizer(jieba_hmm_file, jieba_mp_file, mode=JiebaMode.MP, 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"])

add_dict(user_dict)

Add a user defined word to JiebaTokenizer’s dictionary.

Parameters

user_dict (Union[str, dict]) –

One of the two loading methods is file path(str) loading (according to the Jieba dictionary format) and the other is Python dictionary(dict) loading, Python Dict format: {word1:freq1, word2:freq2,…}. Jieba dictionary format : word(required), freq(optional), such as:

word1 freq1
word2 None
word3 freq3

Only valid word-freq pairs in user provided file will be added into the dictionary. Rows containing invalid input will be ignored. No error nor warning Status is returned.

Examples

>>> 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_dataset = text_file_dataset.map(operations=jieba_op, input_columns=["text"])

add_word(word, freq=None)

Add a user defined word to JiebaTokenizer’s dictionary.

Parameters

• word (str) – The word to be added to the JiebaTokenizer instance. The added word will not be written into the built-in dictionary on disk.

• freq (int, optional) – The frequency of the word to be added. The higher the frequency, the better chance the word will be tokenized (default=None, use default frequency).

Examples

>>> 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_dataset = text_file_dataset.map(operations=jieba_op, input_columns=["text"])