| |
| |
| |
| |
| from typing import List, Tuple |
|
|
| from pythainlp.augment.word2vec.core import Word2VecAug |
|
|
|
|
| class BPEmbAug: |
| """ |
| Thai Text Augment using word2vec from BPEmb |
| |
| BPEmb: |
| `github.com/bheinzerling/bpemb <https://github.com/bheinzerling/bpemb>`_ |
| """ |
|
|
| def __init__(self, lang: str = "th", vs: int = 100000, dim: int = 300): |
| from bpemb import BPEmb |
|
|
| self.bpemb_temp = BPEmb(lang=lang, dim=dim, vs=vs) |
| self.model = self.bpemb_temp.emb |
| self.load_w2v() |
|
|
| def tokenizer(self, text: str) -> List[str]: |
| """ |
| :param str text: Thai text |
| :rtype: List[str] |
| """ |
| return self.bpemb_temp.encode(text) |
|
|
| def load_w2v(self): |
| """ |
| Load BPEmb model |
| """ |
| self.aug = Word2VecAug( |
| self.model, tokenize=self.tokenizer, type="model" |
| ) |
|
|
| def augment( |
| self, sentence: str, n_sent: int = 1, p: float = 0.7 |
| ) -> List[Tuple[str]]: |
| """ |
| Text Augment using word2vec from BPEmb |
| |
| :param str sentence: Thai sentence |
| :param int n_sent: number of sentence |
| :param float p: probability of word |
| |
| :return: list of synonyms |
| :rtype: List[str] |
| :Example: |
| :: |
| |
| from pythainlp.augment.word2vec.bpemb_wv import BPEmbAug |
| |
| aug = BPEmbAug() |
| aug.augment("ผมเรียน", n_sent=2, p=0.5) |
| # output: ['ผมสอน', 'ผมเข้าเรียน'] |
| """ |
| self.sentence = sentence.replace(" ", "▁") |
| self.temp = self.aug.augment(self.sentence, n_sent, p=p) |
| self.temp_new = [] |
| for i in self.temp: |
| self.t = "" |
| for j in i: |
| self.t += j.replace("▁", "") |
| self.temp_new.append(self.t) |
| return self.temp_new |
|
|
