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
- Xet hash:
- a16085932bfb599f5d7148b768c77e476f0d0187fd100cc1e7dae72dda68d8a7
- Size of remote file:
- 5.5 kB
- SHA256:
- ce2256d8b11c3a407590fecf4bb0de08109db7179fb0942d6ef7f650c1a9821f
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