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Medyassino
/
devSECV2

Text Classification
Transformers
Safetensors
French
English
modernbert
devsecops
cybersecurity
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use Medyassino/devSECV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Medyassino/devSECV2 with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="Medyassino/devSECV2")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModelForSequenceClassification
    
    tokenizer = AutoTokenizer.from_pretrained("Medyassino/devSECV2")
    model = AutoModelForSequenceClassification.from_pretrained("Medyassino/devSECV2", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
devSECV2
1.2 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
Medyassino's picture
Medyassino
Create README.md
e5d07de verified about 1 month ago
  • final
    Upload fine-tuned secopv2 (ModernBERT) about 1 month ago
  • .gitattributes
    1.52 kB
    initial commit about 1 month ago
  • README.md
    2.42 kB
    Create README.md about 1 month ago
  • config.json
    2.17 kB
    Upload fine-tuned secopv2 (ModernBERT) about 1 month ago
  • model.safetensors
    598 MB
    xet
    Upload fine-tuned secopv2 (ModernBERT) about 1 month ago
  • tokenizer.json
    3.58 MB
    Upload fine-tuned secopv2 (ModernBERT) about 1 month ago
  • tokenizer_config.json
    488 Bytes
    Upload fine-tuned secopv2 (ModernBERT) about 1 month ago
  • training_args.bin
    5.2 kB
    xet
    Upload fine-tuned secopv2 (ModernBERT) about 1 month ago