Source code for torchtext.data.functional
import sentencepiece as spm
import re
__all__ = [
"generate_sp_model", "load_sp_model",
"sentencepiece_numericalizer", "sentencepiece_tokenizer",
"numericalize_tokens_from_iterator"
]
"""
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.
Arguments:
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')
"""
spm_training_string = "--input={} \
--vocab_size={} \
--model_prefix={} \
--model_type={}".format(filename,
vocab_size,
model_prefix,
model_type)
spm.SentencePieceTrainer.train(spm_training_string)
return None
[docs]def load_sp_model(spm_path):
r"""Load a sentencepiece model for file.
Arguments:
spm_path: the file path 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 = spm.SentencePieceProcessor()
sp_model.Load(spm_path)
return sp_model
[docs]def sentencepiece_numericalizer(sp_model):
r"""A sentencepiece model to numericalize a text sentence into
a generator over the ids.
Arguments:
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.
Arguments:
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.
Arguments:
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)))
