A reasonably quick and reasonably accurate neural machine translation toolkit. Models are trained using eole and inference using ctranslate2 with sentencepiece for tokenization.
git clone https://github.com/quickmt/quickmt.git
pip install ./quickmt/# List available models
quickmt-list
quickmt-model-download quickmt/quickmt-zh-en ./quickmt-zh-enInference with quickmt:
from quickmt import Translator
# Auto-detects GPU, set to "cpu" to force CPU inference
t = Translator("./quickmt-zh-en/", device="auto")
# Translate - set beam size to 5 for higher quality (but slower speed)
t(["他补充道：“我们现在有 4 个月大没有糖尿病的老鼠，但它们曾经得过该病。”"], beam_size=1)
# Get alternative translations by sampling
# You can pass any cTranslate2 `translate_batch` arguments
t(["他补充道：“我们现在有 4 个月大没有糖尿病的老鼠，但它们曾经得过该病。”"], sampling_temperature=1.2, beam_size=1, sampling_topk=50, sampling_topp=0.9)The model is in ctranslate2 format, and the tokenizers are sentencepiece, so you can use the model files directly if you want. It would be fairly easy to get them to work with e.g. LibreTranslate which also uses ctranslate2 and sentencepiece.