torchtext.data.functional

generate_sp_model

torchtext.data.functional.generate_sp_model(filename, vocab_size=20000, model_type='unigram', model_prefix='m_user')

Train a SentencePiece tokenizer.

Parameters:

• 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')

load_sp_model

torchtext.data.functional.load_sp_model(spm)

Load a sentencepiece model for file.

Parameters:

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'))

sentencepiece_numericalizer

torchtext.data.functional.sentencepiece_numericalizer(sp_model)

A sentencepiece model to numericalize a text sentence into

a generator over the ids.

Parameters:

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]]

sentencepiece_tokenizer

torchtext.data.functional.sentencepiece_tokenizer(sp_model)

A sentencepiece model to tokenize a text sentence into

a generator over the tokens.

Parameters:

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', '!']]

custom_replace

torchtext.data.functional.custom_replace(replace_pattern)

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!']

simple_space_split

torchtext.data.functional.simple_space_split(iterator)

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!']]

numericalize_tokens_from_iterator

torchtext.data.functional.numericalize_tokens_from_iterator(vocab, iterator, removed_tokens=None)

Yield a list of ids from an token iterator with a vocab.

Parameters:

• 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]

filter_wikipedia_xml

torchtext.data.functional.filter_wikipedia_xml(text_iterator)

Filter wikipedia xml lines according to https://github.com/facebookresearch/fastText/blob/master/wikifil.pl

Parameters:

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'))

to_map_style_dataset

torchtext.data.functional.to_map_style_dataset(iter_data)

Convert iterable-style dataset to map-style dataset.

Parameters:

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'))