Source code for pythainlp.translate.zh_th

# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 2016-2024 PyThaiNLP Project
# SPDX-License-Identifier: Apache-2.0
"""
Lalita Chinese-Thai Machine Translation

from AI builder

- GitHub: https://github.com/LalitaDeelert/lalita-mt-zhth
- Facebook post https://web.facebook.com/aibuildersx/posts/166736255494822
"""
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM


[docs]class ThZhTranslator: """ Thai-Chinese Machine Translation from Lalita @ AI builder - GitHub: https://github.com/LalitaDeelert/lalita-mt-zhth - Facebook post https://web.facebook.com/aibuildersx/posts/166736255494822 :param bool use_gpu : load model using GPU (Default is False) """
[docs] def __init__( self, use_gpu: bool = False, pretrained: str = "Lalita/marianmt-th-zh_cn", ) -> None: self.tokenizer_thzh = AutoTokenizer.from_pretrained(pretrained) self.model_thzh = AutoModelForSeq2SeqLM.from_pretrained(pretrained) if use_gpu: self.model_thzh = self.model_thzh.cuda()
[docs] def translate(self, text: str) -> str: """ Translate text from Thai to Chinese :param str text: input text in source language :return: translated text in target language :rtype: str :Example: Translate text from Thai to Chinese:: from pythainlp.translate import ThZhTranslator thzh = ThZhTranslator() thzh.translate("ผมรักคุณ") # output: 我爱你 """ self.translated = self.model_thzh.generate( **self.tokenizer_thzh(text, return_tensors="pt", padding=True) ) return [ self.tokenizer_thzh.decode(t, skip_special_tokens=True) for t in self.translated ][0]
[docs]class ZhThTranslator: """ Chinese-Thai Machine Translation from Lalita @ AI builder - GitHub: https://github.com/LalitaDeelert/lalita-mt-zhth - Facebook post https://web.facebook.com/aibuildersx/posts/166736255494822 :param bool use_gpu : load model using GPU (Default is False) """
[docs] def __init__( self, use_gpu: bool = False, pretrained: str = "Lalita/marianmt-zh_cn-th", ) -> None: self.tokenizer_zhth = AutoTokenizer.from_pretrained(pretrained) self.model_zhth = AutoModelForSeq2SeqLM.from_pretrained(pretrained) if use_gpu: self.model_zhth.cuda()
[docs] def translate(self, text: str) -> str: """ Translate text from Chinese to Thai :param str text: input text in source language :return: translated text in target language :rtype: str :Example: Translate text from Chinese to Thai:: from pythainlp.translate import ZhThTranslator zhth = ZhThTranslator() zhth.translate("我爱你") # output: ผมรักคุณนะ """ self.translated = self.model_zhth.generate( **self.tokenizer_zhth(text, return_tensors="pt", padding=True) ) return [ self.tokenizer_zhth.decode(t, skip_special_tokens=True) for t in self.translated ][0]