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KRISHNAPURI
/
q-trust-codebert

Text Classification
Transformers
ONNX
Safetensors
roberta
code
cryptography
post-quantum
static-analysis
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use KRISHNAPURI/q-trust-codebert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use KRISHNAPURI/q-trust-codebert with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="KRISHNAPURI/q-trust-codebert")
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("KRISHNAPURI/q-trust-codebert")
    model = AutoModelForSequenceClassification.from_pretrained("KRISHNAPURI/q-trust-codebert", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
q-trust-codebert / reports
27 kB
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  • 1 contributor
History: 3 commits
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KRISHNAPURI
add reports/models.sha256
e71ed72 verified 3 days ago
  • benchmark_comparison.json
    4.06 kB
    add reports/benchmark_comparison.json 3 days ago
  • models.sha256
    1.05 kB
    add reports/models.sha256 3 days ago
  • training_report_real.json
    21.9 kB
    add reports/training_report_real.json 3 days ago