Summarization
Transformers
TensorBoard
Safetensors
Arabic
m2m_100
text2text-generation
Dialects Conversion
Text Correction
Punctiating
Diacretization
En-Ar Transtaltion
Instructions to use HamzaNaser/Dialects-to-MSA-Transformer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HamzaNaser/Dialects-to-MSA-Transformer with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="HamzaNaser/Dialects-to-MSA-Transformer")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("HamzaNaser/Dialects-to-MSA-Transformer") model = AutoModelForSeq2SeqLM.from_pretrained("HamzaNaser/Dialects-to-MSA-Transformer") - Notebooks
- Google Colab
- Kaggle
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