| |
| |
| |
| |
|
|
| from typing import List |
|
|
| from transformers import ( |
| CamembertTokenizer, |
| pipeline, |
| ) |
|
|
| model_name = "airesearch/wangchanberta-base-att-spm-uncased" |
|
|
|
|
| class Thai2transformersAug: |
| def __init__(self): |
| self.model_name = "airesearch/wangchanberta-base-att-spm-uncased" |
| self.target_tokenizer = CamembertTokenizer |
| self.tokenizer = CamembertTokenizer.from_pretrained( |
| self.model_name, revision="main" |
| ) |
| self.tokenizer.additional_special_tokens = [ |
| "<s>NOTUSED", |
| "</s>NOTUSED", |
| "<_>", |
| ] |
| self.fill_mask = pipeline( |
| task="fill-mask", |
| tokenizer=self.tokenizer, |
| model=f"{self.model_name}", |
| revision="main", |
| ) |
| self.MASK_TOKEN = self.tokenizer.mask_token |
|
|
| def generate(self, sentence: str, num_replace_tokens: int = 3): |
| self.sent2 = [] |
| self.input_text = sentence |
| sent = [ |
| i for i in self.tokenizer.tokenize(self.input_text) if i != "▁" |
| ] |
| if len(sent) < num_replace_tokens: |
| num_replace_tokens = len(sent) |
| masked_text = self.input_text |
| for i in range(num_replace_tokens): |
| masked_text = masked_text + self.MASK_TOKEN |
| self.sent2 += [ |
| str(j["sequence"]).replace("<s> ", "").replace("</s>", "") |
| for j in self.fill_mask(masked_text) |
| if j["sequence"] not in self.sent2 |
| ] |
| masked_text = self.input_text |
| return self.sent2 |
|
|
| def augment(self, sentence: str, num_replace_tokens: int = 3) -> List[str]: |
| """ |
| Text augmentation from WangchanBERTa |
| |
| :param str sentence: Thai sentence |
| :param int num_replace_tokens: number replace tokens |
| |
| :return: list of text augment |
| :rtype: List[str] |
| |
| :Example: |
| :: |
| |
| from pythainlp.augment.lm import Thai2transformersAug |
| |
| aug = Thai2transformersAug() |
| |
| aug.augment("ช้างมีทั้งหมด 50 ตัว บน") |
| # output: ['ช้างมีทั้งหมด 50 ตัว บนโลกใบนี้', |
| 'ช้างมีทั้งหมด 50 ตัว บนสุด', |
| 'ช้างมีทั้งหมด 50 ตัว บนบก', |
| 'ช้างมีทั้งหมด 50 ตัว บนนั้น', |
| 'ช้างมีทั้งหมด 50 ตัว บนหัว'] |
| """ |
| self.sent2 = [] |
| self.sent2 = self.generate(sentence, num_replace_tokens) |
| return self.sent2 |
|
|
