Text Classification
Transformers
PyTorch
Arabic
t5
text2text-generation
Classification
ArabicT5
Text Classification
Instructions to use Hezam/ArabicT5_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hezam/ArabicT5_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Hezam/ArabicT5_Classification")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Hezam/ArabicT5_Classification") model = AutoModelForSeq2SeqLM.from_pretrained("Hezam/ArabicT5_Classification") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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# # Our experiment
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- The category mapping
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'Politics':1,
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'Finance':2,
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'Culture':5,
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'Religion':7
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- Training parameters
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# # Our experiment
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- The category mapping:
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category_mapping = {
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'Politics':1,
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'Finance':2,
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'Culture':5,
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'Tech':6,
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'Religion':7
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}
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- Training parameters
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