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
- Xet hash:
- ac627522de95fc1c46355a4370faeaa460eeb0c1dfe416dbae438c6e9cbf699d
- Size of remote file:
- 924 kB
- SHA256:
- 9e8dbab83dcb62b598d6165273a4b7139dc6f758c718a6a1337c1ef7e63fc6dd
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