Source code for torchtext.data.functional
import io
import re
import torch
__all__ = [
"generate_sp_model",
"load_sp_model",
"sentencepiece_numericalizer",
"sentencepiece_tokenizer",
"numericalize_tokens_from_iterator",
"filter_wikipedia_xml",
"to_map_style_dataset",
]
"""
This file contains experimental functionality.
All of these are experimental, unstable, and subject to change or deletion.
"""
[docs]def generate_sp_model(filename, vocab_size=20000, model_type="unigram", model_prefix="m_user"):
r"""Train a SentencePiece tokenizer.
Args:
filename: the data file for training SentencePiece model.
vocab_size: the size of vocabulary (Default: 20,000).
model_type: the type of SentencePiece model, including unigram,
bpe, char, word.
model_prefix: the prefix of the files saving model and vocab.
Outputs:
The model and vocab are saved in two separate files with
model_prefix.
Examples:
>>> from torchtext.data.functional import generate_sp_model
>>> generate_sp_model('test.csv', vocab_size=23456, model_prefix='spm_user')
"""
torch.ops.torchtext.generate_sp_model(filename, vocab_size, model_type, model_prefix)
[docs]def load_sp_model(spm):
r"""Load a sentencepiece model for file.
Args:
spm: the file path or a file object saving the sentencepiece model.
Outputs:
output: a SentencePiece model.
Examples:
>>> from torchtext.data.functional import load_sp_model
>>> sp_model = load_sp_model("m_user.model")
>>> sp_model = load_sp_model(open("m_user.model", 'rb'))
"""
if isinstance(spm, str):
return torch.ops.torchtext.load_sp_model(spm)
elif isinstance(spm, io.BufferedReader):
return torch.ops.torchtext.load_sp_model_string(spm.read())
else:
raise TypeError(
f"Unsupported type for spm argument: {type(spm).__name__}. "
+ "Supported types are: "
+ ", ".join(["str", "io.BufferedReader"])
)
[docs]def sentencepiece_numericalizer(sp_model):
r"""A sentencepiece model to numericalize a text sentence into
a generator over the ids.
Args:
sp_model: a SentencePiece model.
Outputs:
output: a generator with the input of text sentence and the output of the
corresponding ids based on SentencePiece model.
Examples:
>>> from torchtext.data.functional import sentencepiece_numericalizer
>>> sp_id_generator = sentencepiece_numericalizer(sp_model)
>>> list_a = ["sentencepiece encode as pieces", "examples to try!"]
>>> list(sp_id_generator(list_a))
[[9858, 9249, 1629, 1305, 1809, 53, 842],
[2347, 13, 9, 150, 37]]
"""
def _internal_func(txt_iter):
for line in txt_iter:
yield sp_model.EncodeAsIds(line)
return _internal_func
[docs]def sentencepiece_tokenizer(sp_model):
r"""A sentencepiece model to tokenize a text sentence into
a generator over the tokens.
Args:
sp_model: a SentencePiece model.
Outputs:
output: a generator with the input of text sentence and the output of the
corresponding tokens based on SentencePiece model.
Examples:
>>> from torchtext.data.functional import sentencepiece_tokenizer
>>> sp_tokens_generator = sentencepiece_tokenizer(sp_model)
>>> list_a = ["sentencepiece encode as pieces", "examples to try!"]
>>> list(sp_tokens_generator(list_a))
[['_sentence', 'piece', '_en', 'co', 'de', '_as', '_pieces'],
['_example', 's', '_to', '_try', '!']]
"""
def _internal_func(txt_iter):
for line in txt_iter:
yield sp_model.EncodeAsPieces(line)
return _internal_func
[docs]def custom_replace(replace_pattern):
r"""A transform to convert text string.
Examples:
>>> from torchtext.data.functional import custom_replace
>>> custom_replace_transform = custom_replace([(r'S', 's'), (r'\s+', ' ')])
>>> list_a = ["Sentencepiece encode aS pieces", "exampleS to try!"]
>>> list(custom_replace_transform(list_a))
['sentencepiece encode as pieces', 'examples to try!']
"""
_patterns = list((re.compile(p), r) for (p, r) in replace_pattern)
def _internal_func(txt_iter):
for line in txt_iter:
for pattern_re, replaced_str in _patterns:
line = pattern_re.sub(replaced_str, line)
yield line
return _internal_func
[docs]def simple_space_split(iterator):
r"""A transform to split text string by spaces.
Examples:
>>> from torchtext.data.functional import simple_space_split
>>> list_a = ["Sentencepiece encode as pieces", "example to try!"]
>>> list(simple_space_split(list_a))
[['Sentencepiece', 'encode', 'as', 'pieces'], ['example', 'to', 'try!']]
"""
for line in iterator:
yield line.split()
[docs]def numericalize_tokens_from_iterator(vocab, iterator, removed_tokens=None):
r"""Yield a list of ids from an token iterator with a vocab.
Args:
vocab: the vocabulary convert token into id.
iterator: the iterator yield a list of tokens.
removed_tokens: removed tokens from output dataset (Default: None)
Examples:
>>> from torchtext.data.functional import simple_space_split
>>> from torchtext.data.functional import numericalize_tokens_from_iterator
>>> vocab = {'Sentencepiece' : 0, 'encode' : 1, 'as' : 2, 'pieces' : 3}
>>> ids_iter = numericalize_tokens_from_iterator(vocab,
>>> simple_space_split(["Sentencepiece as pieces",
>>> "as pieces"]))
>>> for ids in ids_iter:
>>> print([num for num in ids])
>>> [0, 2, 3]
>>> [2, 3]
"""
for tokens in iterator:
if removed_tokens is None:
yield iter(vocab[token] for token in tokens)
else:
yield iter(map(lambda x: vocab[x], filter(lambda x: x not in removed_tokens, tokens)))
_patterns = [
(r"<.*>", ""),
(r"&", "&"),
(r"<", "<"),
(r">", ">"),
(r"<ref[^<]*<\/ref>", ""),
(r"<[^>]*>", ""),
(r"\[http:[^] ]*", "["),
(r"\|thumb", ""),
(r"\|left", ""),
(r"\|right", ""),
(r"\|\d+px", ""),
(r"\[\[image:[^\[\]]*\|", ""),
(r"\[\[category:([^|\]]*)[^]]*\]\]", "[[$1]]"),
(r"\[\[[a-z\-]*:[^\]]*\]\]", ""),
(r"\[\[[^\|\]]*\|", "[["),
(r"\{\{[^\}]*\}\}", ""),
(r"\{[^\}]*\}", ""),
(r"\[", ""),
(r"\]", ""),
(r"&[^;]*;", " "),
(r"A", "a"),
(r"B", "b"),
(r"C", "c"),
(r"D", "d"),
(r"E", "e"),
(r"F", "f"),
(r"G", "g"),
(r"H", "h"),
(r"I", "i"),
(r"J", "j"),
(r"K", "k"),
(r"L", "l"),
(r"M", "m"),
(r"N", "n"),
(r"O", "o"),
(r"P", "p"),
(r"Q", "q"),
(r"R", "r"),
(r"S", "s"),
(r"T", "t"),
(r"U", "u"),
(r"V", "v"),
(r"W", "w"),
(r"X", "x"),
(r"Y", "y"),
(r"Z", "z"),
(r"0", " zero "),
(r"1", " one "),
(r"2", " two "),
(r"3", " three "),
(r"4", " four "),
(r"5", " five "),
(r"6", " six "),
(r"7", " seven "),
(r"8", " eight "),
(r"9", " nine "),
(r"[^a-z\n]+", " "),
(r"\n ", ""),
(r"\s+", " "),
(r"\n\s*\n", r"\n"),
]
[docs]def filter_wikipedia_xml(text_iterator):
r"""Filter wikipedia xml lines according to https://github.com/facebookresearch/fastText/blob/master/wikifil.pl
args:
text_iterator: An iterator type object that yields strings. Examples include string list, text io, generators etc.
Examples:
>>> from torchtext.data.functional import filter_wikipedia_xml
>>> from torchtext.datasets import EnWik9
>>> data_iter = EnWik9(split='train')
>>> filter_data_iter = filter_wikipedia_xml(data_iter)
>>> file_name = '.data/EnWik9/enwik9'
>>> filter_data_iter = filter_wikipedia_xml(open(file_name,'r'))
"""
try:
iter(text_iterator)
except:
raise TypeError("Input {} must support iterator semantics".format(text_iterator))
norm_transform = custom_replace(_patterns)
for line in text_iterator:
if "#redirect" in line or "#REDIRECT" in line:
continue
line = list(norm_transform([line]))[0].strip()
if line:
yield line
[docs]def to_map_style_dataset(iter_data):
r"""Convert iterable-style dataset to map-style dataset.
args:
iter_data: An iterator type object. Examples include Iterable datasets, string list, text io, generators etc.
Examples:
>>> from torchtext.datasets import IMDB
>>> from torchtext.data import to_map_style_dataset
>>> train_iter = IMDB(split='train')
>>> train_dataset = to_map_style_dataset(train_iter)
>>> file_name = '.data/EnWik9/enwik9'
>>> data_iter = to_map_style_dataset(open(file_name,'r'))
"""
# Inner class to convert iterable-style to map-style dataset
class _MapStyleDataset(torch.utils.data.Dataset):
def __init__(self, iter_data):
# TODO Avoid list issue #1296
self._data = list(iter_data)
def __len__(self):
return len(self._data)
def __getitem__(self, idx):
return self._data[idx]
return _MapStyleDataset(iter_data)
