Translation
Transformers
PyTorch
TensorFlow
Safetensors
English
Somali
cus
marian
text2text-generation
Instructions to use Helsinki-NLP/opus-mt-en-cus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-en-cus 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-cus")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-cus") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-cus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 81fb11584c7c0e6d7f3a202d37db2a691ae15a0e8fe7ad69e2f2e4e4a88ec319
- Size of remote file:
- 227 MB
- SHA256:
- 30a147df0083729d493e58d7023e644c0b7d1c1b6cd507c0cad26743f4ae32c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.