Text Classification
Transformers
Safetensors
distilbert
security
secret-detection
credentials
dlp
code
text-embeddings-inference
Instructions to use Podric/prowl-secret-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Podric/prowl-secret-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Podric/prowl-secret-encoder")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Podric/prowl-secret-encoder") model = AutoModelForSequenceClassification.from_pretrained("Podric/prowl-secret-encoder", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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docs: replace typographic AI-tells with plain ASCII
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docs: add Citation to the model card
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Reframe card on the model's own terms (recall lift + multilingual reach)
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add example output to card
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transparent logo (fixes white box on dark theme)
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plain prose + noncommercial license
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add logo + card
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add logo + card
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Prowl stage-3 multilingual secret encoder + card
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initial commit
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