Instructions to use Helsinki-NLP/opus-mt-ar-en with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-ar-en 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-ar-en")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-ar-en") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-ar-en", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 54a60cdfda57a438ed6459fb40f36635bd7b30ad9f128c4e226179a95c6a768e
- Size of remote file:
- 308 MB
- SHA256:
- e937a8dd33fa352c278029f190b1d077deb8c637152ff4039c202b72683935fb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.