# SPDX-FileCopyrightText: 2016-2026 PyThaiNLP Project
# SPDX-FileType: SOURCE
# 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 __future__ import annotations
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
import torch
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
[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:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
self.tokenizer_thzh: AutoTokenizer = AutoTokenizer.from_pretrained(
pretrained
)
self.model_thzh: AutoModelForSeq2SeqLM = (
AutoModelForSeq2SeqLM.from_pretrained(pretrained)
)
if use_gpu:
self.model_thzh = self.model_thzh.cuda()
[docs]
def translate(
self, text: str, exclude_words: Optional[list[str]] = None
) -> str:
"""Translate text from Thai to Chinese
:param str text: input text in source language
:param list[str] exclude_words: words to exclude from translation
(optional)
: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
Translate text from Thai to Chinese with excluded words::
thzh.translate("ผมรักคุณ", exclude_words=["ผม"])
# output: ผม爱你
"""
from pythainlp.translate.core import (
_prepare_text_with_exclusions,
_restore_excluded_words,
)
prepared_text, placeholder_map = _prepare_text_with_exclusions(
text, exclude_words
)
self.translated: torch.Tensor = self.model_thzh.generate(
**self.tokenizer_thzh(
prepared_text, return_tensors="pt", padding=True
)
)
translated_text = [
self.tokenizer_thzh.decode(t, skip_special_tokens=True)
for t in self.translated
][0]
return _restore_excluded_words(translated_text, placeholder_map)
[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:
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
self.tokenizer_zhth: AutoTokenizer = AutoTokenizer.from_pretrained(
pretrained
)
self.model_zhth: AutoModelForSeq2SeqLM = (
AutoModelForSeq2SeqLM.from_pretrained(pretrained)
)
if use_gpu:
self.model_zhth = self.model_zhth.cuda()
[docs]
def translate(
self, text: str, exclude_words: Optional[list[str]] = None
) -> str:
"""Translate text from Chinese to Thai
:param str text: input text in source language
:param list[str] exclude_words: words to exclude from translation
(optional)
: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
Translate text from Chinese to Thai with excluded words::
zhth.translate("我爱你", exclude_words=["我"])
# output: 我รักคุณนะ
"""
from pythainlp.translate.core import (
_prepare_text_with_exclusions,
_restore_excluded_words,
)
prepared_text, placeholder_map = _prepare_text_with_exclusions(
text, exclude_words
)
self.translated: torch.Tensor = self.model_zhth.generate(
**self.tokenizer_zhth(
prepared_text, return_tensors="pt", padding=True
)
)
translated_text = [
self.tokenizer_zhth.decode(t, skip_special_tokens=True)
for t in self.translated
][0]
return _restore_excluded_words(translated_text, placeholder_map)