# Copyright 2020-2021 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.utils provides some general methods for NLP text processing.
For example, you can use Vocab to build a dictionary,
use to_bytes and to_str to encode and decode strings into a specified format.
"""
from enum import IntEnum
import numpy as np
import mindspore._c_dataengine as cde
from .validators import check_vocab, check_from_file, check_from_list, check_from_dict, check_from_dataset, \
check_from_dataset_sentencepiece, check_from_file_sentencepiece, check_save_model, \
check_from_file_vectors, check_tokens_to_ids, check_ids_to_tokens
__all__ = [
"Vocab", "SentencePieceVocab", "to_str", "to_bytes", "Vectors", "FastText", "GloVe", "CharNGram"
]
[docs]class Vocab:
"""
Vocab object that is used to save pairs of words and ids.
It contains a map that maps each word(str) to an id(int) or reverse.
"""
def __init__(self):
self.c_vocab = None
[docs] def vocab(self):
"""
Get the vocabory table in dict type.
Returns:
A vocabulary consisting of word and id pairs.
Examples:
>>> vocab = text.Vocab.from_list(["word_1", "word_2", "word_3", "word_4"])
>>> vocabory_dict = vocab.vocab()
"""
check_vocab(self.c_vocab)
return self.c_vocab.vocab()
[docs] @check_tokens_to_ids
def tokens_to_ids(self, tokens):
"""
Converts a token string or a sequence of tokens in a single integer id or a sequence of ids.
If token does not exist, return id with value -1.
Args:
tokens(Union[str, list[str]]): One or several token(s) to convert to token id(s).
Returns:
The token id or list of token ids.
Examples:
>>> vocab = text.Vocab.from_list(["w1", "w2", "w3"], special_tokens=["<unk>"], special_first=True)
>>> ids = vocab.tokens_to_ids(["w1", "w3"])
"""
check_vocab(self.c_vocab)
if isinstance(tokens, str):
tokens = [tokens]
return self.c_vocab.tokens_to_ids(tokens)
[docs] @check_ids_to_tokens
def ids_to_tokens(self, ids):
"""
Converts a single index or a sequence of indices in a token or a sequence of tokens.
If id does not exist, return empty string.
Args:
ids(Union[int, list[int]]): The token id (or token ids) to convert to tokens.
Returns:
The decoded token(s).
Examples:
>>> vocab = text.Vocab.from_list(["w1", "w2", "w3"], special_tokens=["<unk>"], special_first=True)
>>> token = vocab.ids_to_tokens(0)
"""
check_vocab(self.c_vocab)
if isinstance(ids, int):
ids = [ids]
return self.c_vocab.ids_to_tokens(ids)
[docs] @classmethod
@check_from_dataset
def from_dataset(cls, dataset, columns=None, freq_range=None, top_k=None, special_tokens=None, special_first=True):
"""
Build a vocab from a dataset.
This would collect all unique words in a dataset and return a vocab within
the frequency range specified by user in freq_range. User would be warned if no words fall into the frequency.
Words in vocab are ordered from the highest frequency to the lowest frequency. Words with the same frequency
would be ordered lexicographically.
Args:
dataset(Dataset): dataset to build vocab from.
columns(list[str], optional): column names to get words from. It can be a list of column names.
(default=None, where all columns will be used. If any column isn't string type, will return error).
freq_range(tuple, optional): A tuple of integers (min_frequency, max_frequency). Words within the frequency
range would be kept. 0 <= min_frequency <= max_frequency <= total_words. min_frequency=0 is the same as
min_frequency=1. max_frequency > total_words is the same as max_frequency = total_words.
min_frequency/max_frequency can be None, which corresponds to 0/total_words separately
(default=None, all words are included).
top_k(int, optional): top_k is greater than 0. Number of words to be built into vocab. top_k means most
frequent words are taken. top_k is taken after freq_range. If not enough top_k, all words will be taken
(default=None, all words are included).
special_tokens(list, optional): A list of strings, each one is a special token. For example
special_tokens=["<pad>","<unk>"] (default=None, no special tokens will be added).
special_first(bool, optional): Whether special_tokens will be prepended/appended to vocab. If special_tokens
is specified and special_first is set to True, special_tokens will be prepended (default=True).
Returns:
Vocab, vocab built from the dataset.
Examples:
>>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
>>> vocab = text.Vocab.from_dataset(dataset, "text", freq_range=None, top_k=None,
... special_tokens=["<pad>", "<unk>"],
... special_first=True)
>>> dataset = dataset.map(operations=text.Lookup(vocab, "<unk>"), input_columns=["text"])
"""
vocab = Vocab()
vocab.c_vocab = dataset.build_vocab(columns, freq_range, top_k, special_tokens, special_first)
return vocab
[docs] @classmethod
@check_from_list
def from_list(cls, word_list, special_tokens=None, special_first=True):
"""
Build a vocab object from a list of word.
Args:
word_list(list): A list of string where each element is a word of type string.
special_tokens(list, optional): A list of strings, each one is a special token. For example
special_tokens=["<pad>","<unk>"] (default=None, no special tokens will be added).
special_first(bool, optional): Whether special_tokens is prepended or appended to vocab. If special_tokens
is specified and special_first is set to True, special_tokens will be prepended (default=True).
Returns:
Vocab, vocab built from the `list`.
Examples:
>>> vocab = text.Vocab.from_list(["w1", "w2", "w3"], special_tokens=["<unk>"], special_first=True)
"""
if special_tokens is None:
special_tokens = []
vocab = Vocab()
vocab.c_vocab = cde.Vocab.from_list(word_list, special_tokens, special_first)
return vocab
[docs] @classmethod
@check_from_file
def from_file(cls, file_path, delimiter="", vocab_size=None, special_tokens=None, special_first=True):
"""
Build a vocab object from a list of word.
Args:
file_path (str): Path to the file which contains the vocab list.
delimiter (str, optional): A delimiter to break up each line in file, the first element is taken to be
the word (default="", the whole line will be treated as a word).
vocab_size (int, optional): Number of words to read from file_path (default=None, all words are taken).
special_tokens (list, optional): A list of strings, each one is a special token. For example
special_tokens=["<pad>","<unk>"] (default=None, no special tokens will be added).
special_first (bool, optional): Whether special_tokens will be prepended/appended to vocab,
If special_tokens is specified and special_first is set to True,
special_tokens will be prepended (default=True).
Returns:
Vocab, vocab built from the file.
Examples:
>>> # Assume vocab file contains the following content:
>>> # --- begin of file ---
>>> # apple,apple2
>>> # banana, 333
>>> # cat,00
>>> # --- end of file ---
>>>
>>> # Read file through this API and specify "," as delimiter.
>>> # The delimiter will break up each line in file, then the first element is taken to be the word.
>>> vocab = text.Vocab.from_file("/path/to/simple/vocab/file", ",", None, ["<pad>", "<unk>"], True)
>>>
>>> # Finally, there are 5 words in the vocab: "<pad>", "<unk>", "apple", "banana", "cat".
>>> vocabulary = vocab.vocab()
"""
if vocab_size is None:
vocab_size = -1
if special_tokens is None:
special_tokens = []
vocab = Vocab()
vocab.c_vocab = cde.Vocab.from_file(file_path, delimiter, vocab_size, special_tokens, special_first)
return vocab
[docs] @classmethod
@check_from_dict
def from_dict(cls, word_dict):
"""
Build a vocab object from a dict.
Args:
word_dict (dict): Dict contains word and id pairs, where word should be str and id be int. id is recommended
to start from 0 and be continuous. ValueError will be raised if id is negative.
Returns:
Vocab, vocab built from the `dict`.
Examples:
>>> vocab = text.Vocab.from_dict({"home": 3, "behind": 2, "the": 4, "world": 5, "<unk>": 6})
"""
vocab = Vocab()
vocab.c_vocab = cde.Vocab.from_dict(word_dict)
return vocab
[docs]class SentencePieceVocab(cde.SentencePieceVocab):
"""
SentencePiece object that is used to do words segmentation.
"""
[docs] @classmethod
@check_from_dataset_sentencepiece
def from_dataset(cls, dataset, col_names, vocab_size, character_coverage, model_type, params):
"""
Build a SentencePiece from a dataset.
Args:
dataset(Dataset): Dataset to build SentencePiece.
col_names(list): The list of the col name.
vocab_size(int): Vocabulary size.
character_coverage(float): Amount of characters covered by the model, good defaults are: 0.9995 for
languages with rich character set like Japanese or Chinese and 1.0 for other languages with small
character set.
model_type(SentencePieceModel): It can be any of [SentencePieceModel.UNIGRAM, SentencePieceModel.BPE,
SentencePieceModel.CHAR, SentencePieceModel.WORD], default is SentencePieceModel.UNIGRAM. The input
sentence must be pre-tokenized when using SentencePieceModel.WORD type.
- SentencePieceModel.UNIGRAM, Unigram Language Model means the next word in the sentence is assumed to
be independent of the previous words generated by the model.
- SentencePieceModel.BPE, refers to byte pair encoding algorithm, which replaces the most frequent pair
of bytes in a sentence with a single, unused byte.
- SentencePieceModel.CHAR, refers to char based sentencePiece Model type.
- SentencePieceModel.WORD, refers to word based sentencePiece Model type.
params(dict): A dictionary with no incoming parameters.
Returns:
SentencePieceVocab, vocab built from the dataset.
Examples:
>>> from mindspore.dataset.text import SentencePieceModel
>>> dataset = ds.TextFileDataset("/path/to/sentence/piece/vocab/file", shuffle=False)
>>> vocab = text.SentencePieceVocab.from_dataset(dataset, ["text"], 5000, 0.9995,
... SentencePieceModel.UNIGRAM, {})
"""
return dataset.build_sentencepiece_vocab(col_names, vocab_size, character_coverage,
model_type, params)
[docs] @classmethod
@check_from_file_sentencepiece
def from_file(cls, file_path, vocab_size, character_coverage, model_type, params):
"""
Build a SentencePiece object from a list of word.
Args:
file_path(list): Path to the file which contains the SentencePiece list.
vocab_size(int): Vocabulary size.
character_coverage(float): Amount of characters covered by the model, good defaults are: 0.9995 for
languages with rich character set like Japanese or Chinese and 1.0 for other languages with small
character set.
model_type(SentencePieceModel): It can be any of [SentencePieceModel.UNIGRAM, SentencePieceModel.BPE,
SentencePieceModel.CHAR, SentencePieceModel.WORD], default is SentencePieceModel.UNIGRAM. The input
sentence must be pre-tokenized when using SentencePieceModel.WORD type.
- SentencePieceModel.UNIGRAM, Unigram Language Model means the next word in the sentence is assumed to
be independent of the previous words generated by the model.
- SentencePieceModel.BPE, refers to byte pair encoding algorithm, which replaces the most frequent pair
of bytes in a sentence with a single, unused byte.
- SentencePieceModel.CHAR, refers to char based sentencePiece Model type.
- SentencePieceModel.WORD, refers to word based sentencePiece Model type.
params(dict): A dictionary with no incoming parameters(The parameters are derived from SentencePiece
library).
.. code-block::
input_sentence_size 0
max_sentencepiece_length 16
Returns:
SentencePieceVocab, vocab built from the file.
Examples:
>>> from mindspore.dataset.text import SentencePieceModel
>>> vocab = text.SentencePieceVocab.from_file(["/path/to/sentence/piece/vocab/file"], 5000, 0.9995,
... SentencePieceModel.UNIGRAM, {})
"""
return super().from_file(file_path, vocab_size, character_coverage,
DE_C_INTER_SENTENCEPIECE_MODE[model_type], params)
[docs] @classmethod
@check_save_model
def save_model(cls, vocab, path, filename):
"""
Save model into given filepath.
Args:
vocab(SentencePieceVocab): A SentencePiece object.
path(str): Path to store model.
filename(str): The name of the file.
Examples:
>>> from mindspore.dataset.text import SentencePieceModel
>>> vocab = text.SentencePieceVocab.from_file(["/path/to/sentence/piece/vocab/file"], 5000, 0.9995,
... SentencePieceModel.UNIGRAM, {})
>>> text.SentencePieceVocab.save_model(vocab, "./", "m.model")
"""
super().save_model(vocab, path, filename)
[docs]def to_str(array, encoding='utf8'):
"""
Convert NumPy array of `bytes` to array of `str` by decoding each element based on charset `encoding`.
Args:
array (numpy.ndarray): Array of `bytes` type representing strings.
encoding (str): Indicating the charset for decoding (default='utf8').
Returns:
numpy.ndarray, NumPy array of `str`.
Examples:
>>> text_file_dataset_dir = ["/path/to/text_file_dataset_file"]
>>> dataset = ds.TextFileDataset(dataset_files=text_file_dataset_dir, shuffle=False)
>>> for item in dataset.create_dict_iterator(num_epochs=1, output_numpy=True):
... data = text.to_str(item["text"])
"""
if not isinstance(array, np.ndarray):
raise TypeError('input should be a NumPy array.')
return np.char.decode(array, encoding)
[docs]def to_bytes(array, encoding='utf8'):
"""
Convert NumPy array of `str` to array of `bytes` by encoding each element based on charset `encoding`.
Args:
array (numpy.ndarray): Array of `str` type representing strings.
encoding (str): Indicating the charset for encoding (default='utf8').
Returns:
numpy.ndarray, NumPy array of `bytes`.
"""
if not isinstance(array, np.ndarray):
raise ValueError('input should be a NumPy array.')
return np.char.encode(array, encoding)
[docs]class JiebaMode(IntEnum):
"""
An enumeration for JiebaTokenizer.
Possible enumeration values are: JiebaMode.MIX, JiebaMode.MP, JiebaMode.HMM.
- JiebaMode.MIX: tokenize with a mix of MPSegment and HMMSegment algorithm.
- JiebaMode.MP: tokenize with MPSegment algorithm.
- JiebaMode.HMM: tokenize with Hidden Markov Model Segment algorithm.
"""
MIX = 0
MP = 1
HMM = 2
[docs]class SentencePieceModel(IntEnum):
"""
An enumeration for SentencePieceModel.
Possible enumeration values are: SentencePieceModel.UNIGRAM, SentencePieceModel.BPE, SentencePieceModel.CHAR,
SentencePieceModel.WORD.
- SentencePieceModel,UNIGRAM: Unigram Language Model means the next word in the sentence is assumed to be
independent of the previous words generated by the model.
- SentencePieceModel.BPE: refers to byte pair encoding algorithm, which replaces the most frequent pair of bytes in
a sentence with a single, unused byte.
- SentencePieceModel.CHAR: refers to char based sentencePiece Model type.
- SentencePieceModel.WORD: refers to word based sentencePiece Model type.
"""
UNIGRAM = 0
BPE = 1
CHAR = 2
WORD = 3
DE_C_INTER_SENTENCEPIECE_MODE = {
SentencePieceModel.UNIGRAM: cde.SentencePieceModel.DE_SENTENCE_PIECE_UNIGRAM,
SentencePieceModel.BPE: cde.SentencePieceModel.DE_SENTENCE_PIECE_BPE,
SentencePieceModel.CHAR: cde.SentencePieceModel.DE_SENTENCE_PIECE_CHAR,
SentencePieceModel.WORD: cde.SentencePieceModel.DE_SENTENCE_PIECE_WORD
}
[docs]class SPieceTokenizerOutType(IntEnum):
"""
An enumeration for SPieceTokenizerOutType.
Possible enumeration values are: SPieceTokenizerOutType.STRING, SPieceTokenizerOutType.INT.
- SPieceTokenizerOutType.STRING: means output type of SentencePice Tokenizer is string.
- SPieceTokenizerOutType.INT: means output type of SentencePice Tokenizer is int.
"""
STRING = 0
INT = 1
[docs]class SPieceTokenizerLoadType(IntEnum):
"""
An enumeration for SPieceTokenizerLoadType.
Possible enumeration values are: SPieceTokenizerLoadType.FILE, SPieceTokenizerLoadTypeMODEL.
- SPieceTokenizerLoadType.FILE: Load sentencepiece tokenizer from local sentencepiece vocab file.
- SPieceTokenizerLoadType.MODEL: Load sentencepiece tokenizer from sentencepiece vocab instance.
"""
FILE = 0
MODEL = 1
class Vectors(cde.Vectors):
"""
Vectors object that is used to map tokens into vectors.
"""
@classmethod
@check_from_file_vectors
def from_file(cls, file_path, max_vectors=None):
"""
Build a vector from a file.
Args:
file_path (str): Path of the file that contains the vectors.
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
situations where the entire set doesn’t fit in memory, or is not needed for another reason,
passing max_vectors can limit the size of the loaded set (default=None, no limit).
Examples:
>>> vector = text.Vectors.from_file("/path/to/vectors/file", max_vectors=None)
"""
max_vectors = max_vectors if max_vectors is not None else 0
return super().from_file(file_path, max_vectors)
class FastText(cde.FastText):
"""
FastText object that is used to map tokens into vectors.
"""
@classmethod
@check_from_file_vectors
def from_file(cls, file_path, max_vectors=None):
"""
Build a FastText vector from a file.
Args:
file_path (str): Path of the file that contains the vectors. The shuffix of pre-trained vector sets
must be `*.vec`.
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
situations where the entire set doesn’t fit in memory, or is not needed for another reason,
passing max_vectors can limit the size of the loaded set (default=None, no limit).
Examples:
>>> fast_text = text.FastText.from_file("/path/to/fast_text/file", max_vectors=None)
"""
max_vectors = max_vectors if max_vectors is not None else 0
return super().from_file(file_path, max_vectors)
class GloVe(cde.GloVe):
"""
GloVe object that is used to map tokens into vectors.
"""
@classmethod
@check_from_file_vectors
def from_file(cls, file_path, max_vectors=None):
"""
Build a GloVe vector from a file.
Args:
file_path (str): Path of the file that contains the vectors. The format of pre-trained vector sets
must be `glove.6B.*.txt`.
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
situations where the entire set doesn’t fit in memory, or is not needed for another reason,
passing max_vectors can limit the size of the loaded set (default=None, no limit).
Examples:
>>> glove = text.GloVe.from_file("/path/to/glove/file", max_vectors=None)
"""
max_vectors = max_vectors if max_vectors is not None else 0
return super().from_file(file_path, max_vectors)
class CharNGram(cde.CharNGram):
"""
CharNGram object that is used to map tokens into pre-trained vectors.
"""
@classmethod
@check_from_file_vectors
def from_file(cls, file_path, max_vectors=None):
"""
Build a CharNGram vector from a file.
Args:
file_path (str): Path of the file that contains the CharNGram vectors.
max_vectors (int, optional): This can be used to limit the number of pre-trained vectors loaded.
Most pre-trained vector sets are sorted in the descending order of word frequency. Thus, in
situations where the entire set doesn’t fit in memory, or is not needed for another reason,
passing max_vectors can limit the size of the loaded set (default=None, no limit).
Examples:
>>> char_n_gram = text.CharNGram.from_file("/path/to/char_n_gram/file", max_vectors=None)
"""
max_vectors = max_vectors if max_vectors is not None else 0
return super().from_file(file_path, max_vectors)