File size: 1,966 Bytes
e4b9a7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | from transformers import M2M100ForConditionalGeneration
from .tokenization_small100 import SMALL100Tokenizer
class Small100Translator:
"""
Machine Translation using small100 model
- Huggingface https://huggingface.co/alirezamsh/small100
:param bool use_gpu : load model using GPU (Default is False)
"""
def __init__(
self,
use_gpu: bool = False,
pretrained: str = "alirezamsh/small100",
) -> None:
self.pretrained = pretrained
self.model = M2M100ForConditionalGeneration.from_pretrained(self.pretrained)
self.tgt_lang = None
if use_gpu:
self.model = self.model.cuda()
def translate(self, text: str, tgt_lang: str="en") -> str:
"""
Translate text from X to X
:param str text: input text in source language
:param str tgt_lang: target language
:return: translated text in target language
:rtype: str
:Example:
::
from pythainlp.translate.small100 import Small100Translator
mt = Small100Translator()
# Translate text from Thai to English
mt.translate("ทดสอบระบบ", tgt_lang="en")
# output: 'Testing system'
# Translate text from Thai to Chinese
mt.translate("ทดสอบระบบ", tgt_lang="zh")
# output: '系统测试'
# Translate text from Thai to French
mt.translate("ทดสอบระบบ", tgt_lang="fr")
# output: 'Test du système'
"""
if tgt_lang!=self.tgt_lang:
self.tokenizer = SMALL100Tokenizer.from_pretrained(self.pretrained, tgt_lang=tgt_lang)
self.tgt_lang = tgt_lang
self.translated = self.model.generate(
**self.tokenizer(text, return_tensors="pt")
)
return self.tokenizer.batch_decode(self.translated, skip_special_tokens=True)[0]
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