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Source code for torchtext.data.functional

import re
import io
import torch


__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. 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)))

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