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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Hezam/ArabicT5_Classification") model = AutoModelForSeq2SeqLM.from_pretrained("Hezam/ArabicT5_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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- SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
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- Paper
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[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
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-Dataset
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[https://data.mendeley.com/datasets/57zpx667y9/2]
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# # Their experiment'
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| CGRU | 93.43% |
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| HANGRU | 95.81% |
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[https://www.sciencedirect.com/science/article/abs/pii/S0306457319303413]
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# # Our experiment
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# # The category mapping
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- SANAD: Single-label Arabic News Articles Dataset for automatic text categorization
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- Paper
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[https://www.researchgate.net/publication/333605992_SANAD_Single-Label_Arabic_News_Articles_Dataset_for_Automatic_Text_Categorization]
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-Dataset
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[https://data.mendeley.com/datasets/57zpx667y9/2]
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# # Their experiment'
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| CGRU | 93.43% |
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| HANGRU | 95.81% |
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# # Our experiment
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# # The category mapping
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