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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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output = [tokenizer.decode(ids, skip_special_tokens=True,clean_up_tokenization_spaces=True)for ids in output]
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output
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```bash
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['5']
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```
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output = [tokenizer.decode(ids, skip_special_tokens=True,clean_up_tokenization_spaces=True)for ids in output]
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output
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```
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```bash
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['5']
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```
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