Source code for pythainlp.augment.word2vec.bpemb_wv

# -*- coding: utf-8 -*-
from pythainlp.augment.word2vec.core import Word2VecAug
from bpemb import BPEmb
from typing import List, Tuple


[docs]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): self.bpemb_temp = BPEmb(lang=lang, dim=dim, vs=vs) self.model = self.bpemb_temp.emb self.load_w2v()
[docs] def tokenizer(self, text: str) -> List[str]: """ :param str text: thai text :rtype: List[str] """ return self.bpemb_temp.encode(text)
[docs] def load_w2v(self): """ Load BPEmb model """ self.aug = Word2VecAug( self.model, tokenize=self.tokenizer, type="model" )
[docs] 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 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