Text Classification
Transformers
Safetensors
modernbert
prompt-injection
jailbreak-detection
guardrail
text-embeddings-inference
Instructions to use subin/mmbert32k-attack-guard-candidate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subin/mmbert32k-attack-guard-candidate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="subin/mmbert32k-attack-guard-candidate")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("subin/mmbert32k-attack-guard-candidate") model = AutoModelForSequenceClassification.from_pretrained("subin/mmbert32k-attack-guard-candidate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from subin/mmbert32k-attack-guard-candidate: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/subin/mmbert32k-attack-guard-candidate/resolve/main/tokenizer.json
- Command line
-
hf download hf://subin/mmbert32k-attack-guard-candidate/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/subin/mmbert32k-attack-guard-candidate/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- daf46a52874fb9b6191b9ae46a0f471904405b611f8e1d580f76c7424d343f89
- Size of remote file:
- 34.4 MB
- SHA256:
- 17f7d8b9518c403d7429ad9eeeabb6eed49c8d3311de8ef4ed5ad811381a2ced
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