Translation
Transformers
Safetensors
Arabic
t5
text2text-generation
saudi
arabic
dialects
text-generation-inference
Instructions to use NAMAA-Space/NAMAA-MT-Saudi2English with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NAMAA-Space/NAMAA-MT-Saudi2English 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="NAMAA-Space/NAMAA-MT-Saudi2English")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("NAMAA-Space/NAMAA-MT-Saudi2English") model = AutoModelForSeq2SeqLM.from_pretrained("NAMAA-Space/NAMAA-MT-Saudi2English") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1a23d7a236df007a4db1c008be5d3388461fcd19d566893bea8f700959a42ad3
- Size of remote file:
- 5.97 kB
- SHA256:
- ff904833470037b5c8045d1c7d75ad32c7fa2bd20f66a8da72871bd521b88bec
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.