Text Classification
Transformers
ONNX
Safetensors
multilingual
English
modernbert
prompt-injection
jailbreak-detection
llm-security
ai-agents
guardrails
onnxruntime
mcp
text-embeddings-inference
Instructions to use usezn/Galvanize-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use usezn/Galvanize-3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="usezn/Galvanize-3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("usezn/Galvanize-3") model = AutoModelForSequenceClassification.from_pretrained("usezn/Galvanize-3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from usezn/Galvanize-3: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/usezn/Galvanize-3/resolve/main/tokenizer.json
- Command line
-
hf download hf://usezn/Galvanize-3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/usezn/Galvanize-3/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- bc7667ea83987ba5c9c8a21bdf2d426e80db83152da005f6484efd7e4d87910e
- Size of remote file:
- 34.4 MB
- SHA256:
- 8ab59b95e06d1d289dba953ef1ab95044067692bf5877f49236ebd1d8a24dcdf
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