Translation
Transformers
PyTorch
TensorFlow
Safetensors
Arabic
English
marian
text2text-generation
opus-mt-tc
Eval Results (legacy)
Instructions to use Helsinki-NLP/opus-mt-tc-big-en-ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Helsinki-NLP/opus-mt-tc-big-en-ar 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-tc-big-en-ar")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-tc-big-en-ar") model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-tc-big-en-ar") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60641e57303d847584280fa4a2680d990176c077db911260ae06f8864bfe5d00
- Size of remote file:
- 965 MB
- SHA256:
- 6dbf6a18947b8c22fb28c638eec1d3c1e7930c4ba9cfc149e6be5e18f3f7da5e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.