| |
| |
| |
| |
| import itertools |
| from typing import List, Tuple |
|
|
| from gensim.models.fasttext import FastText as FastText_gensim |
| from gensim.models.keyedvectors import KeyedVectors |
|
|
| from pythainlp.tokenize import word_tokenize |
|
|
|
|
| class FastTextAug: |
| """ |
| Text Augment from fastText |
| |
| :param str model_path: path of model file |
| """ |
|
|
| def __init__(self, model_path: str): |
| """ |
| :param str model_path: path of model file |
| """ |
| if model_path.endswith(".bin"): |
| self.model = FastText_gensim.load_facebook_vectors(model_path) |
| elif model_path.endswith(".vec"): |
| self.model = KeyedVectors.load_word2vec_format(model_path) |
| else: |
| self.model = FastText_gensim.load(model_path) |
| self.dict_wv = list(self.model.key_to_index.keys()) |
|
|
| def tokenize(self, text: str) -> List[str]: |
| """ |
| Thai text tokenization for fastText |
| |
| :param str text: Thai text |
| |
| :return: list of words |
| :rtype: List[str] |
| """ |
| return word_tokenize(text, engine="icu") |
|
|
| def modify_sent(self, sent: str, p: float = 0.7) -> List[List[str]]: |
| """ |
| :param str sent: text of sentence |
| :param float p: probability |
| :rtype: List[List[str]] |
| """ |
| list_sent_new = [] |
| for i in sent: |
| if i in self.dict_wv: |
| w = [j for j, v in self.model.most_similar(i) if v >= p] |
| if w == []: |
| list_sent_new.append([i]) |
| else: |
| list_sent_new.append(w) |
| else: |
| list_sent_new.append([i]) |
| return list_sent_new |
|
|
| def augment( |
| self, sentence: str, n_sent: int = 1, p: float = 0.7 |
| ) -> List[Tuple[str]]: |
| """ |
| Text Augment from fastText |
| |
| You may want to download the Thai model |
| from https://fasttext.cc/docs/en/crawl-vectors.html. |
| |
| :param str sentence: Thai sentence |
| :param int n_sent: number of sentences |
| :param float p: probability of word |
| |
| :return: list of synonyms |
| :rtype: List[Tuple[str]] |
| """ |
| self.sentence = self.tokenize(sentence) |
| self.list_synonym = self.modify_sent(self.sentence, p=p) |
| new_sentences = [] |
| for x in list(itertools.product(*self.list_synonym))[0:n_sent]: |
| new_sentences.append(x) |
| return new_sentences |
|
|
