# Copyright 2020-2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
The module text.transforms is inherited from _c_dataengine
and is implemented based on ICU4C and cppjieba in C++.
It's a high performance module to process NLP text.
Users can use Vocab to build their own dictionary,
use appropriate tokenizers to split sentences into different tokens,
and use Lookup to find the index of tokens in Vocab.
.. Note::
A constructor's arguments for every class in this module must be saved into the
class attributes (self.xxx) to support save() and load().
Examples:
>>> text_file_dataset_dir = ["/path/to/text_file_dataset_file"] # contains 1 or multiple text files
>>> # Create a dataset for text sentences saved as line data in a file
>>> text_file_dataset = ds.TextFileDataset(dataset_files=text_file_dataset_dir, shuffle=False)
>>> # Tokenize sentences to unicode characters
>>> tokenizer = text.UnicodeCharTokenizer()
>>> # Load vocabulary from list
>>> vocab = text.Vocab.from_list(word_list=['深', '圳', '欢', '迎', '您'])
>>> # Use Lookup operator to map tokens to ids
>>> lookup = text.Lookup(vocab=vocab)
>>> text_file_dataset = text_file_dataset.map(operations=[tokenizer, lookup])
>>> # if text line in dataset_file is:
>>> # 深圳欢迎您
>>> # then the output will be:
>>> # {'text': array([0, 1, 2, 3, 4], dtype=int32)}
"""
import json
import os
import re
import platform
import numpy as np
import mindspore._c_dataengine as cde
from mindspore.common import dtype as mstype
from .utils import JiebaMode, NormalizeForm, to_str, SPieceTokenizerOutType, SPieceTokenizerLoadType, SentencePieceVocab
from .validators import check_lookup, check_jieba_add_dict, check_to_vectors, \
check_jieba_add_word, check_jieba_init, check_with_offsets, check_unicode_script_tokenizer, \
check_wordpiece_tokenizer, check_regex_replace, check_regex_tokenizer, check_basic_tokenizer, check_ngram, \
check_pair_truncate, check_to_number, check_bert_tokenizer, check_python_tokenizer, check_slidingwindow, \
check_sentence_piece_tokenizer
from ..core.datatypes import mstype_to_detype
from ..core.validator_helpers import replace_none
from ..transforms.py_transforms_util import Implementation
from ..transforms.transforms import TensorOperation
from ..transforms.validators import invalidate_callable
class TextTensorOperation(TensorOperation):
"""
Base class of Text Tensor Ops
"""
def __init__(self):
super().__init__()
self.implementation = Implementation.C
def parse(self):
raise NotImplementedError("TextTensorOperation has to implement parse() method.")
DE_C_INTER_JIEBA_MODE = {
JiebaMode.MIX: cde.JiebaMode.DE_JIEBA_MIX,
JiebaMode.MP: cde.JiebaMode.DE_JIEBA_MP,
JiebaMode.HMM: cde.JiebaMode.DE_JIEBA_HMM
}
DE_C_INTER_SENTENCEPIECE_LOADTYPE = {
SPieceTokenizerLoadType.FILE: cde.SPieceTokenizerLoadType.DE_SPIECE_TOKENIZER_LOAD_KFILE,
SPieceTokenizerLoadType.MODEL: cde.SPieceTokenizerLoadType.DE_SPIECE_TOKENIZER_LOAD_KMODEL
}
DE_C_INTER_SENTENCEPIECE_OUTTYPE = {
SPieceTokenizerOutType.STRING: cde.SPieceTokenizerOutType.DE_SPIECE_TOKENIZER_OUTTYPE_KString,
SPieceTokenizerOutType.INT: cde.SPieceTokenizerOutType.DE_SPIECE_TOKENIZER_OUTTYPE_KINT
}
[docs]class JiebaTokenizer(TextTensorOperation):
"""
Tokenize Chinese string into words based on dictionary.
Note:
The integrity of the HMMSEgment algorithm and MPSegment algorithm files must be confirmed.
Args:
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).
Raises:
ValueError: If path of HMMSegment dict is not provided.
ValueError: If path of MPSegment dict is not provided.
TypeError: If `hmm_path` or `mp_path` is not of type string.
TypeError: If `with_offsets` is not of type bool.
Supported Platforms:
``CPU``
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"])
"""
@check_jieba_init
def __init__(self, hmm_path, mp_path, mode=JiebaMode.MIX, with_offsets=False):
super().__init__()
if not isinstance(mode, JiebaMode):
raise TypeError("Wrong input type for mode, should be JiebaMode.")
self.mode = mode
self.__check_path__(hmm_path)
self.hmm_path = hmm_path
self.__check_path__(mp_path)
self.mp_path = mp_path
self.with_offsets = with_offsets
self.words = []
def __check_path__(self, model_path):
"""check model path"""
if not os.path.exists(os.path.realpath(model_path)):
raise ValueError(
" jieba mode file {} is not exist.".format(model_path))
def parse(self):
jieba_tokenizer = cde.JiebaTokenizerOperation(self.hmm_path, self.mp_path,
DE_C_INTER_JIEBA_MODE.get(self.mode),
self.with_offsets)
for word in self.words:
jieba_tokenizer.add_word(word[0], word[1])
return jieba_tokenizer
[docs] @invalidate_callable
@check_jieba_add_word
def add_word(self, word, freq=None):
"""
Add a user defined word to JiebaTokenizer's dictionary.
Args:
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"])
"""
if freq is None:
self.words.append((word, 0))
else:
self.words.append((word, freq))
[docs] @invalidate_callable
@check_jieba_add_dict
def add_dict(self, user_dict):
"""
Add a user defined word to JiebaTokenizer's dictionary.
Args:
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:
.. code-block::
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"])
"""
if isinstance(user_dict, str):
self.__add_dict_py_file(user_dict)
elif isinstance(user_dict, dict):
for k, v in user_dict.items():
self.add_word(k, v)
else:
raise TypeError("The type of user_dict must str or dict.")
def __add_dict_py_file(self, file_path):
"""Add user defined word by file"""
words_list = self.__parser_file(file_path)
for data in words_list:
if data[1] is None:
freq = 0
else:
freq = int(data[1])
self.add_word(data[0], freq)
def __decode(self, data):
"""decode the dict file to utf8"""
try:
data = data.decode('utf-8')
except UnicodeDecodeError:
raise ValueError("user dict file must be utf8 format.")
return data.lstrip('\ufeff')
def __parser_file(self, file_path):
"""parser user defined word by file"""
if not os.path.exists(file_path):
raise ValueError(
"user dict file {} is not exist.".format(file_path))
real_file_path = os.path.realpath(file_path)
file_dict = open(real_file_path)
data_re = re.compile('^\\s*([^\\s*]+?)\\s*([0-9]+)?\\s*$', re.U)
words_list = []
for item in file_dict:
data = item.strip()
if not isinstance(data, str):
data = self.__decode(data)
tmp = data_re.match(data)
if not tmp:
continue
words = tmp.groups()
words_list.append(words)
file_dict.close()
return words_list
[docs]class Lookup(TextTensorOperation):
"""
Look up a word into an id according to the input vocabulary table.
Args:
vocab (Vocab): A vocabulary object.
unknown_token (str, optional): Word is used for lookup. In case of the word is out of vocabulary (OOV),
the result of lookup will be replaced with unknown_token. If the unknown_token is not specified or
it is OOV, runtime error will be thrown (default=None, means no unknown_token is specified).
data_type (mindspore.dtype, optional): The data type that lookup operation maps
string to(default=mindspore.int32).
Raises:
TypeError: If `vocab` is not of type text.Vocab.
TypeError: If `unknown_token` is not of type string.
TypeError: If `data_type` is not of type mindspore.dtype.
Supported Platforms:
``CPU``
Examples:
>>> # Load vocabulary from list
>>> vocab = text.Vocab.from_list(['深', '圳', '欢', '迎', '您'])
>>> # Use Lookup operator to map tokens to ids
>>> lookup = text.Lookup(vocab)
>>> text_file_dataset = text_file_dataset.map(operations=[lookup])
"""
@check_lookup
def __init__(self, vocab, unknown_token=None, data_type=mstype.int32):
super().__init__()
self.vocab = vocab
self.unknown_token = unknown_token
self.data_type = data_type
def parse(self):
return cde.LookupOperation(self.vocab.c_vocab, self.unknown_token, str(mstype_to_detype(self.data_type)))
[docs]class Ngram(TextTensorOperation):
"""
Generate n-gram from a 1-D string Tensor.
Refer to https://en.wikipedia.org/wiki/N-gram#Examples for an overview of what n-gram is and how it works.
Args:
n (list[int]): n in n-gram, which is a list of positive integers. For example, if n=[4, 3], then the result
would be a 4-gram followed by a 3-gram in the same tensor. If the number of words is not enough to make up
for a n-gram, an empty string will be returned. For example, 3 grams on ["mindspore", "best"] will result in
an empty string produced.
left_pad (tuple, optional): Padding performed on left side of the sequence shaped like ("pad_token", pad_width).
`pad_width` will be capped at n-1. For example, specifying left_pad=("_", 2) would pad left side of the
sequence with "__" (default=("", 0)).
right_pad (tuple, optional): Padding performed on right side of the sequence shaped like
("pad_token", pad_width). `pad_width` will be capped at n-1. For example, specifying right_pad=("_", 2)
would pad right side of the sequence with "__" (default=("", 0)).
separator (str, optional): Symbol used to join strings together. For example, if 2-gram is
["mindspore", "amazing"] with separator="-", the result would be ["mindspore-amazing"]
(default=" ", which will use whitespace as separator).
Raises:
TypeError: If values of `n` not positive is not of type int.
ValueError: If values of `n` not positive.
ValueError: If `left_pad` is not a tuple of length 2.
ValueError: If `right_pad` is not a tuple of length 2.
TypeError: If `separator` is not of type string.
Supported Platforms:
``CPU``
Examples:
>>> ngram_op = text.Ngram(3, separator="-")
>>> output = ngram_op(["WildRose Country", "Canada's Ocean Playground", "Land of Living Skies"])
>>> # output
>>> # ["WildRose Country-Canada's Ocean Playground-Land of Living Skies"]
>>> # same ngram_op called through map
>>> text_file_dataset = text_file_dataset.map(operations=ngram_op)
"""
@check_ngram
def __init__(self, n, left_pad=("", 0), right_pad=("", 0), separator=" "):
super().__init__()
self.ngrams = n
self.left_pad = left_pad
self.right_pad = right_pad
self.separator = separator
def parse(self):
return cde.NgramOperation(self.ngrams, self.left_pad, self.right_pad, self.separator)
[docs]class PythonTokenizer:
"""
Class that applies user-defined string tokenizer into input string.
Args:
tokenizer (Callable): Python function that takes a `str` and returns a list of `str` as tokens.
Raises:
TypeError: If `tokenizer` is not a callable Python function.
Supported Platforms:
``CPU``
Examples:
>>> def my_tokenizer(line):
... return line.split()
>>> text_file_dataset = text_file_dataset.map(operations=text.PythonTokenizer(my_tokenizer))
"""
@check_python_tokenizer
def __init__(self, tokenizer):
self.pyfunc = tokenizer
self.tokenizer = np.vectorize(lambda x: np.array(tokenizer(x), dtype='U'), signature='()->(n)')
self.random = False
def __call__(self, in_array):
if not isinstance(in_array, np.ndarray):
raise TypeError("input should be a NumPy array. Got {}.".format(type(in_array)))
if in_array.dtype.type is np.bytes_:
in_array = to_str(in_array)
try:
tokens = self.tokenizer(in_array)
except Exception as e:
raise RuntimeError("Error occurred in Pyfunc [" + str(self.pyfunc.__name__) + "], error message: " + str(e))
return tokens
def to_json(self):
json_obj = {}
json_obj["tensor_op_name"] = self.pyfunc.__name__
json_obj["python_module"] = self.__class__.__module__
return json.dumps(json_obj)
[docs]class SentencePieceTokenizer(TextTensorOperation):
"""
Tokenize scalar token or 1-D tokens to tokens by sentencepiece.
Args:
mode (Union[str, SentencePieceVocab]): SentencePiece model.
If the input parameter is a file, it represents the path of SentencePiece mode to be loaded.
If the input parameter is a SentencePieceVocab object, it should be constructed in advanced.
out_type (SPieceTokenizerOutType): The type of output, it can be any of [SPieceTokenizerOutType.STRING,
SPieceTokenizerOutType.INT].
- SPieceTokenizerOutType.STRING, means output type of SentencePice Tokenizer is string.
- SPieceTokenizerOutType.INT, means output type of SentencePice Tokenizer is int.
Raises:
TypeError: If `mode` is not of type string or SentencePieceVocab.
TypeError: If `out_type` is not of type SPieceTokenizerOutType.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.text import SentencePieceModel, SPieceTokenizerOutType
>>> sentence_piece_vocab_file = "/path/to/sentence/piece/vocab/file"
>>> vocab = text.SentencePieceVocab.from_file([sentence_piece_vocab_file], 5000, 0.9995,
... SentencePieceModel.UNIGRAM, {})
>>> tokenizer = text.SentencePieceTokenizer(vocab, out_type=SPieceTokenizerOutType.STRING)
>>> text_file_dataset = text_file_dataset.map(operations=tokenizer)
"""
@check_sentence_piece_tokenizer
def __init__(self, mode, out_type):
super().__init__()
self.mode = mode
self.out_type = out_type
def parse(self):
self.mode = self.mode.c_sentence_piece_vocab if isinstance(self.mode, SentencePieceVocab) else self.mode
return cde.SentencePieceTokenizerOperation(self.mode, DE_C_INTER_SENTENCEPIECE_OUTTYPE.get(self.out_type))
[docs]class SlidingWindow(TextTensorOperation):
"""
Construct a tensor from given data (only support 1-D for now), where each element in the dimension axis
is a slice of data starting at the corresponding position, with a specified width.
Args:
width (int): The width of the window. It must be an integer and greater than zero.
axis (int, optional): The axis along which the sliding window is computed (default=0).
Raises:
TypeError: If `width` is not of type int.
ValueError: If value of `width` is not positive.
TypeError: If `axis` is not of type int.
Supported Platforms:
``CPU``
Examples:
>>> dataset = ds.NumpySlicesDataset(data=[[1, 2, 3, 4, 5]], column_names="col1")
>>> # Data before
>>> # | col1 |
>>> # +--------------+
>>> # | [[1, 2, 3, 4, 5]] |
>>> # +--------------+
>>> dataset = dataset.map(operations=text.SlidingWindow(3, 0))
>>> # Data after
>>> # | col1 |
>>> # +--------------+
>>> # | [[1, 2, 3], |
>>> # | [2, 3, 4], |
>>> # | [3, 4, 5]] |
>>> # +--------------+
"""
@check_slidingwindow
def __init__(self, width, axis=0):
super().__init__()
self.width = width
self.axis = axis
def parse(self):
return cde.SlidingWindowOperation(self.width, self.axis)
[docs]class ToNumber(TextTensorOperation):
"""
Tensor operation to convert every element of a string tensor to a number.
Strings are cast according to the rules specified in the following links, except that any strings which represent
negative numbers cannot be cast to an unsigned integer type, rules links are as follows:
https://en.cppreference.com/w/cpp/string/basic_string/stof,
https://en.cppreference.com/w/cpp/string/basic_string/stoul,
Args:
data_type (mindspore.dtype): Type to be cast to. Must be a numeric type in mindspore.dtype.
Raises:
TypeError: If `data_type` is not of type mindspore.dtype.
RuntimeError: If strings are invalid to cast, or are out of range after being cast.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore import dtype as mstype
>>> data = [["1", "2", "3"]]
>>> dataset = ds.NumpySlicesDataset(data)
>>> to_number_op = text.ToNumber(mstype.int8)
>>> dataset = dataset.map(operations=to_number_op)
"""
@check_to_number
def __init__(self, data_type):
super().__init__()
data_type = mstype_to_detype(data_type)
self.data_type = str(data_type)
def parse(self):
return cde.ToNumberOperation(self.data_type)
[docs]class ToVectors(TextTensorOperation):
"""
Look up a token into vectors according to the input vector table.
Args:
vectors (Vectors): A vectors object.
unk_init (sequence, optional): Sequence used to initialize out-of-vectors (OOV) token
(default=None, initialize with zero vectors).
lower_case_backup (bool, optional): Whether to look up the token in the lower case. If False, each token in the
original case will be looked up; if True, each token in the original case will be looked up first, if not
found in the keys of the property stoi, the token in the lower case will be looked up (default=False).
Raises:
TypeError: If `unk_init` is not of type sequence.
TypeError: If elements of `unk_init` is not of type float or int.
TypeError: If `lower_case_backup` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> # Load vectors from file
>>> vectors = text.Vectors.from_file("/path/to/vectors/file")
>>> # Use ToVectors operator to map tokens to vectors
>>> to_vectors = text.ToVectors(vectors)
>>> text_file_dataset = text_file_dataset.map(operations=[to_vectors])
"""
@check_to_vectors
def __init__(self, vectors, unk_init=None, lower_case_backup=False):
super().__init__()
self.vectors = vectors
self.unk_init = unk_init if unk_init is not None else []
self.lower_case_backup = lower_case_backup
def parse(self):
return cde.ToVectorsOperation(self.vectors, self.unk_init, self.lower_case_backup)
[docs]class TruncateSequencePair(TextTensorOperation):
"""
Truncate a pair of rank-1 tensors such that the total length is less than max_length.
This operation takes two input tensors and returns two output Tensors.
Args:
max_length (int): Maximum length required.
Raises:
TypeError: If `max_length` is not of type int.
Supported Platforms:
``CPU``
Examples:
>>> dataset = ds.NumpySlicesDataset(data={"col1": [[1, 2, 3]], "col2": [[4, 5]]})
>>> # Data before
>>> # | col1 | col2 |
>>> # +-----------+-----------|
>>> # | [1, 2, 3] | [4, 5] |
>>> # +-----------+-----------+
>>> truncate_sequence_pair_op = text.TruncateSequencePair(max_length=4)
>>> dataset = dataset.map(operations=truncate_sequence_pair_op)
>>> # Data after
>>> # | col1 | col2 |
>>> # +-----------+-----------+
>>> # | [1, 2] | [4, 5] |
>>> # +-----------+-----------+
"""
@check_pair_truncate
def __init__(self, max_length):
super().__init__()
self.max_length = max_length
def parse(self):
return cde.TruncateSequencePairOperation(self.max_length)
[docs]class UnicodeCharTokenizer(TextTensorOperation):
"""
Tokenize a scalar tensor of UTF-8 string to Unicode characters.
Args:
with_offsets (bool, optional): Whether or not output offsets of tokens (default=False).
Raises:
TypeError: If `with_offsets` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> tokenizer_op = text.UnicodeCharTokenizer(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.UnicodeCharTokenizer(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"])
"""
@check_with_offsets
def __init__(self, with_offsets=False):
super().__init__()
self.with_offsets = with_offsets
def parse(self):
return cde.UnicodeCharTokenizerOperation(self.with_offsets)
[docs]class WordpieceTokenizer(TextTensorOperation):
"""
Tokenize the input text to subword tokens.
Args:
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 :class:`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"])
"""
@check_wordpiece_tokenizer
def __init__(self, vocab, suffix_indicator='##', max_bytes_per_token=100, unknown_token='[UNK]',
with_offsets=False):
super().__init__()
self.vocab = vocab
self.suffix_indicator = suffix_indicator
self.max_bytes_per_token = max_bytes_per_token
self.unknown_token = unknown_token
self.with_offsets = with_offsets
def parse(self):
return cde.WordpieceTokenizerOperation(self.vocab.c_vocab, self.suffix_indicator, self.max_bytes_per_token,
self.unknown_token, self.with_offsets)
if platform.system().lower() != 'windows':
DE_C_INTER_NORMALIZE_FORM = {
NormalizeForm.NONE: cde.NormalizeForm.DE_NORMALIZE_NONE,
NormalizeForm.NFC: cde.NormalizeForm.DE_NORMALIZE_NFC,
NormalizeForm.NFKC: cde.NormalizeForm.DE_NORMALIZE_NFKC,
NormalizeForm.NFD: cde.NormalizeForm.DE_NORMALIZE_NFD,
NormalizeForm.NFKD: cde.NormalizeForm.DE_NORMALIZE_NFKD
}
[docs] class BasicTokenizer(TextTensorOperation):
"""
Tokenize the input UTF-8 encoded string by specific rules.
Note:
`BasicTokenizer` is not supported on Windows platform yet.
Args:
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 <http://unicode.org/reports/tr15/>`_, 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 :class:`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"])
"""
@check_basic_tokenizer
def __init__(self, lower_case=False, keep_whitespace=False, normalization_form=NormalizeForm.NONE,
preserve_unused_token=True, with_offsets=False):
super().__init__()
if not isinstance(normalization_form, NormalizeForm):
raise TypeError("Wrong input type for normalization_form, should be enum of 'NormalizeForm'.")
self.lower_case = lower_case
self.keep_whitespace = keep_whitespace
self.normalization_form = DE_C_INTER_NORMALIZE_FORM.get(normalization_form)
self.preserve_unused_token = preserve_unused_token
self.with_offsets = with_offsets
def parse(self):
return cde.BasicTokenizerOperation(self.lower_case, self.keep_whitespace, self.normalization_form,
self.preserve_unused_token, self.with_offsets)
[docs] class BertTokenizer(TextTensorOperation):
"""
Tokenizer used for Bert text process.
Note:
`BertTokenizer` is not supported on Windows platform yet.
Args:
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]'.
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 <http://unicode.org/reports/tr15/>`_, 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 `vocab` is not of type :class:`mindspore.dataset.text.Vocab`.
TypeError: If `suffix_indicator` is not of type str.
TypeError: If `max_bytes_per_token` is not of type int.
ValueError: If `max_bytes_per_token` is negative.
TypeError: If `unknown_token` is not of type str.
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 :class:`mindspore.dataset.text.NormalizeForm`.
TypeError: If `preserve_unused_token` is not of type bool.
TypeError: If `with_offsets` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.text import NormalizeForm
>>>
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> vocab_list = ["床", "前", "明", "月", "光", "疑", "是", "地", "上", "霜", "举", "头", "望", "低",
... "思", "故", "乡","繁", "體", "字", "嘿", "哈", "大", "笑", "嘻", "i", "am", "mak",
... "make", "small", "mistake", "##s", "during", "work", "##ing", "hour", "😀", "😃",
... "😄", "😁", "+", "/", "-", "=", "12", "28", "40", "16", " ", "I", "[CLS]", "[SEP]",
... "[UNK]", "[PAD]", "[MASK]", "[unused1]", "[unused10]"]
>>> vocab = text.Vocab.from_list(vocab_list)
>>> tokenizer_op = text.BertTokenizer(vocab=vocab, suffix_indicator='##', max_bytes_per_token=100,
... unknown_token='[UNK]', 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.BertTokenizer(vocab=vocab, suffix_indicator='##', max_bytes_per_token=100,
... unknown_token='[UNK]', 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"])
"""
@check_bert_tokenizer
def __init__(self, vocab, suffix_indicator='##', max_bytes_per_token=100, unknown_token='[UNK]',
lower_case=False, keep_whitespace=False, normalization_form=NormalizeForm.NONE,
preserve_unused_token=True, with_offsets=False):
super().__init__()
if not isinstance(normalization_form, NormalizeForm):
raise TypeError("Wrong input type for normalization_form, should be enum of 'NormalizeForm'.")
self.vocab = vocab
self.suffix_indicator = suffix_indicator
self.max_bytes_per_token = max_bytes_per_token
self.unknown_token = unknown_token
self.lower_case = lower_case
self.keep_whitespace = keep_whitespace
self.normalization_form = DE_C_INTER_NORMALIZE_FORM.get(normalization_form)
self.preserve_unused_token = preserve_unused_token
self.with_offsets = with_offsets
def parse(self):
return cde.BertTokenizerOperation(self.vocab.c_vocab, self.suffix_indicator, self.max_bytes_per_token,
self.unknown_token, self.lower_case, self.keep_whitespace,
self.normalization_form, self.preserve_unused_token, self.with_offsets)
[docs] class CaseFold(TextTensorOperation):
"""
Apply case fold operation on UTF-8 string tensor, which is aggressive that can convert more characters into
lower case. Supported normalization forms please refer to
`ICU_Normalizer2 <https://unicode-org.github.io/icu-docs/apidoc/released/icu4c/classicu_1_1Normalizer2.html>`_ .
Note:
CaseFold is not supported on Windows platform yet.
Supported Platforms:
``CPU``
Examples:
>>> case_op = text.CaseFold()
>>> text_file_dataset = text_file_dataset.map(operations=case_op)
"""
def parse(self):
return cde.CaseFoldOperation()
[docs] class FilterWikipediaXML(TextTensorOperation):
"""
Filter Wikipedia XML dumps to "clean" text consisting only of lowercase letters (a-z, converted from A-Z),
and spaces (never consecutive).
Note:
FilterWikipediaXML is not supported on Windows platform yet.
Supported Platforms:
``CPU``
Examples:
>>> import mindspore.dataset.text.transforms as text
>>>
>>> replace_op = text.FilterWikipediaXML()
>>> text_file_dataset = text_file_dataset.map(operations=replace_op)
"""
def parse(self):
return cde.FilterWikipediaXMLOperation()
[docs] class NormalizeUTF8(TextTensorOperation):
"""
Apply normalize operation on UTF-8 string tensor.
Note:
NormalizeUTF8 is not supported on Windows platform yet.
Args:
normalize_form (NormalizeForm, optional): Valid values can be [NormalizeForm.NONE, NormalizeForm.NFC,
NormalizeForm.NFKC, NormalizeForm.NFD, NormalizeForm.NFKD] any of the four unicode
normalized forms(default=NormalizeForm.NFKC).
See http://unicode.org/reports/tr15/ for details.
- NormalizeForm.NONE, do nothing for input string tensor.
- NormalizeForm.NFC, normalize with Normalization Form C.
- NormalizeForm.NFKC, normalize with Normalization Form KC.
- NormalizeForm.NFD, normalize with Normalization Form D.
- NormalizeForm.NFKD, normalize with Normalization Form KD.
Raises:
TypeError: If `normalize_form` is not of type NormalizeForm.
Supported Platforms:
``CPU``
Examples:
>>> from mindspore.dataset.text import NormalizeForm
>>> normalize_op = text.NormalizeUTF8(normalize_form=NormalizeForm.NFC)
>>> text_file_dataset = text_file_dataset.map(operations=normalize_op)
"""
def __init__(self, normalize_form=NormalizeForm.NFKC):
super().__init__()
if not isinstance(normalize_form, NormalizeForm):
raise TypeError("Wrong input type for normalization_form, should be enum of 'NormalizeForm'.")
normalize_form = replace_none(normalize_form, NormalizeForm.NFKC)
self.normalize_form = DE_C_INTER_NORMALIZE_FORM.get(normalize_form)
def parse(self):
return cde.NormalizeUTF8Operation(self.normalize_form)
[docs] class RegexReplace(TextTensorOperation):
"""
Replace a part of UTF-8 string tensor with given text according to regular expressions.
See https://unicode-org.github.io/icu/userguide/strings/regexp.html for supported regex pattern.
Note:
RegexReplace is not supported on Windows platform yet.
Args:
pattern (str): the regex expression patterns.
replace (str): the string to replace matched element.
replace_all (bool, optional): If False, only replace first matched element;
if True, replace all matched elements (default=True).
Raises:
TypeError: If `pattern` is not of type string.
TypeError: If `replace` is not of type string.
TypeError: If `replace_all` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> pattern = 'Canada'
>>> replace = 'China'
>>> replace_op = text.RegexReplace(pattern, replace)
>>> text_file_dataset = text_file_dataset.map(operations=replace_op)
"""
@check_regex_replace
def __init__(self, pattern, replace, replace_all=True):
super().__init__()
self.pattern = pattern
self.replace = replace
self.replace_all = replace_all
def parse(self):
return cde.RegexReplaceOperation(self.pattern, self.replace, self.replace_all)
[docs] class RegexTokenizer(TextTensorOperation):
"""
Tokenize a scalar tensor of UTF-8 string by regex expression pattern.
See https://unicode-org.github.io/icu/userguide/strings/regexp.html for supported regex pattern.
Note:
RegexTokenizer is not supported on Windows platform yet.
Args:
delim_pattern (str): The pattern of regex delimiters.
The original string will be split by matched elements.
keep_delim_pattern (str, optional): The string matched by 'delim_pattern' can be kept as a token
if it can be matched by 'keep_delim_pattern'. The default value is an empty str
which means that delimiters will not be kept as an output token (default='').
with_offsets (bool, optional): Whether or not output offsets of tokens(default=False).
Raises:
TypeError: If `delim_pattern` is not of type string.
TypeError: If `keep_delim_pattern` is not of type string.
TypeError: If `with_offsets` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> # If with_offsets=False, default output is one column {["text", dtype=str]}
>>> delim_pattern = r"[ |,]"
>>> tokenizer_op = text.RegexTokenizer(delim_pattern, 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.RegexTokenizer(delim_pattern, 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"])
"""
@check_regex_tokenizer
def __init__(self, delim_pattern, keep_delim_pattern='', with_offsets=False):
super().__init__()
self.delim_pattern = delim_pattern
self.keep_delim_pattern = keep_delim_pattern
self.with_offsets = with_offsets
def parse(self):
return cde.RegexTokenizerOperation(self.delim_pattern, self.keep_delim_pattern, self.with_offsets)
[docs] class UnicodeScriptTokenizer(TextTensorOperation):
"""
Tokenize a scalar tensor of UTF-8 string based on Unicode script boundaries.
Note:
UnicodeScriptTokenizer is not supported on Windows platform yet.
Args:
keep_whitespace (bool, optional): Whether or not emit whitespace tokens (default=False).
with_offsets (bool, optional): Whether or not output offsets of tokens (default=False).
Raises:
TypeError: If `keep_whitespace` is not of type bool.
TypeError: If `with_offsets` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> tokenizer_op = text.UnicodeScriptTokenizer(keep_whitespace=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.UnicodeScriptTokenizer(keep_whitespace=True, 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"])
"""
@check_unicode_script_tokenizer
def __init__(self, keep_whitespace=False, with_offsets=False):
super().__init__()
keep_whitespace = replace_none(keep_whitespace, False)
with_offsets = replace_none(with_offsets, False)
self.keep_whitespace = keep_whitespace
self.with_offsets = with_offsets
def parse(self):
return cde.UnicodeScriptTokenizerOperation(self.keep_whitespace, self.with_offsets)
[docs] class WhitespaceTokenizer(TextTensorOperation):
"""
Tokenize a scalar tensor of UTF-8 string on ICU4C defined whitespaces, such as: ' ', '\\\\t', '\\\\r', '\\\\n'.
Note:
WhitespaceTokenizer is not supported on Windows platform yet.
Args:
with_offsets (bool, optional): Whether or not output offsets of tokens (default=False).
Raises:
TypeError: If `with_offsets` is not of type bool.
Supported Platforms:
``CPU``
Examples:
>>> # If with_offsets=False, default output one column {["text", dtype=str]}
>>> tokenizer_op = text.WhitespaceTokenizer(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.WhitespaceTokenizer(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"])
"""
@check_with_offsets
def __init__(self, with_offsets=False):
super().__init__()
self.with_offsets = with_offsets
def parse(self):
return cde.WhitespaceTokenizerOperation(self.with_offsets)