Translation
Transformers
Safetensors
Arabic
t5
text2text-generation
dialect
msa
syrian_dialect
MSA
Shami_dialect
text-generation-inference
Instructions to use JadwalAlmaa/Shami_to_Fasih with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JadwalAlmaa/Shami_to_Fasih 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="JadwalAlmaa/Shami_to_Fasih")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("JadwalAlmaa/Shami_to_Fasih") model = AutoModelForSeq2SeqLM.from_pretrained("JadwalAlmaa/Shami_to_Fasih") - Notebooks
- Google Colab
- Kaggle
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Model tree for JadwalAlmaa/Shami_to_Fasih
Base model
UBC-NLP/AraT5v2-base-1024