Source code for

import io
import re

import torch

__all__ = [

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 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 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( 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 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 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 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 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 import simple_space_split >>> from 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"&amp;", "&"), (r"&lt;", "<"), (r"&gt;", ">"), (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 args: text_iterator: An iterator type object that yields strings. Examples include string list, text io, generators etc. Examples: >>> from 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 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( def __init__(self, iter_data) -> None: # 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)


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