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
Download pytorch_model.bin from Hezam/ArabicT5_Classification: direct link, hf CLI and curl.
- Browser
- Download file 653 MB
-
https://huggingface.co/Hezam/ArabicT5_Classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
- Command line
-
hf download hf://Hezam/ArabicT5_Classification@refs/pr/1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Hezam/ArabicT5_Classification/resolve/refs%2Fpr%2F1/pytorch_model.bin
653 MB
- Xet hash:
- f03d455066edff45e9a0845cfd2a29bb7199cdbe010ebd8ee029cccf5ec49f8b
- Size of remote file:
- 653 MB
- SHA256:
- d63c70258885e3cf1acd91a540f505c6a8d1f988591d17bf47a850f361af2ba8
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