Text Classification
Transformers
Safetensors
distilbert
spam-detection
multilingual
security
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use IntegralBender/distilbert-spam-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IntegralBender/distilbert-spam-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IntegralBender/distilbert-spam-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IntegralBender/distilbert-spam-detector") model = AutoModelForSequenceClassification.from_pretrained("IntegralBender/distilbert-spam-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
Yesterday
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Update model card
Verified signature53a85e3 -
Update comprehensive model card documentation
Verified signature- Add multilingual widget examples (EN, TR, DE, FR) for HAM/SPAM classification. - Document the 5-dataset aggregation, strict deduplication, and data leakage prevention pipeline. - Expand Model Overview and Intended Uses & Limitations sections. - Add reference link to the CPU-optimized INT8 quantized ONNX variant. - Structure training hyperparameters, hardware specs, and evaluation metrics tables.45e3876
6 days ago
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Model sync via MLOps pipeline - 2026-10-23 01:02.
Verified signature9f43ca2 -
initial commit
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