| |
| |
| |
| |
| from typing import List, Tuple |
|
|
| from pythainlp.augment.word2vec.core import Word2VecAug |
| from pythainlp.corpus import get_corpus_path |
| from pythainlp.tokenize import THAI2FIT_TOKENIZER |
|
|
|
|
| class Thai2fitAug: |
| """ |
| Text Augment using word2vec from Thai2Fit |
| |
| Thai2Fit: |
| `github.com/cstorm125/thai2fit <https://github.com/cstorm125/thai2fit>`_ |
| """ |
|
|
| def __init__(self): |
| self.thai2fit_wv = get_corpus_path("thai2fit_wv") |
| self.load_w2v() |
|
|
| def tokenizer(self, text: str) -> List[str]: |
| """ |
| :param str text: Thai text |
| :rtype: List[str] |
| """ |
| return THAI2FIT_TOKENIZER.word_tokenize(text) |
|
|
| def load_w2v(self): |
| """ |
| Load Thai2Fit's word2vec model |
| """ |
| self.aug = Word2VecAug(self.thai2fit_wv, self.tokenizer, type="binary") |
|
|
| def augment( |
| self, sentence: str, n_sent: int = 1, p: float = 0.7 |
| ) -> List[Tuple[str]]: |
| """ |
| Text Augment using word2vec from Thai2Fit |
| |
| :param str sentence: Thai sentence |
| :param int n_sent: number of sentence |
| :param float p: probability of word |
| |
| :return: list of text augmented |
| :rtype: List[Tuple[str]] |
| |
| :Example: |
| :: |
| |
| from pythainlp.augment.word2vec import Thai2fitAug |
| |
| aug = Thai2fitAug() |
| aug.augment("ผมเรียน", n_sent=2, p=0.5) |
| # output: [('พวกเรา', 'เรียน'), ('ฉัน', 'เรียน')] |
| """ |
| return self.aug.augment(sentence, n_sent, p) |
|
|
