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Hezam
/
ArabicT5_Classification

Text Classification
Transformers
PyTorch
Arabic
t5
text2text-generation
Classification
ArabicT5
Text Classification
Model card Files Files and versions
xet
Community
1

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
ArabicT5_Classification
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  • 1 contributor
History: 35 commits
Hezam's picture
Hezam
Update README.md
3e6a5d5 verified over 2 years ago
  • .gitattributes
    1.52 kB
    initial commit over 2 years ago
  • README.md
    3.57 kB
    Update README.md over 2 years ago
  • config.json
    761 Bytes
    basic training over 2 years ago
  • generation_config.json
    142 Bytes
    basic training over 2 years ago
  • pytorch_model.bin

    Detected Pickle imports (3)

    • "collections.OrderedDict",
    • "torch._utils._rebuild_tensor_v2",
    • "torch.FloatStorage"

    What is a pickle import?

    653 MB
    xet
    basic training over 2 years ago
  • special_tokens_map.json
    2.2 kB
    Upload 3 files over 2 years ago
  • spiece.model
    924 kB
    xet
    Upload 3 files over 2 years ago
  • tokenizer_config.json
    2.41 kB
    Upload 3 files over 2 years ago