Instructions to use Helsinki-NLP/opus-mt-afa-afa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-afa-afa 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-afa-afa")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-afa-afa") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-afa-afa") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d09e3fede6a3a9b6810b362f1da3af7dce4e534b5da393bd0e3ef8bf56370cd
- Size of remote file:
- 248 MB
- SHA256:
- 53c1d632238d267d9cbcbec67b9421d830464ce75f5d4543e99d41991af30089
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.