Instructions to use Helsinki-NLP/opus-mt-en-zh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-zh with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Helsinki-NLP/opus-mt-en-zh", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-zh") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-zh", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
How to convert MT Model (like Helsinki-NLP / opus-mt-en-zh )into onnx model and use it
#2
by corner - opened
I was use Python to enable machine translation as a server, But each translation thread requires 24 cores, so how is the way to convert the model to onnx format
I try it ,but converting the model to onnx format is bad. because it is Marian model,which is build by c++.