Instructions to use Helsinki-NLP/opus-mt-en-mul with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-mul 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-mul")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-mul") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-mul") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6d1ff498b900c7e97a1a4a2a46943595f00c02f97b74c47506266953e7c12d2
- Size of remote file:
- 310 MB
- SHA256:
- b7d0589b00fb32025948af833778fbbfc6d069683fed3e43c4a4f4f192bd77a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.