Text Classification
Transformers
Safetensors
English
modernbert
prompt-injection
jailbreak-detection
guardrails
safety
classification
Eval Results (legacy)
text-embeddings-inference
Instructions to use smcleod/guardrails-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smcleod/guardrails-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smcleod/guardrails-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smcleod/guardrails-v1") model = AutoModelForSequenceClassification.from_pretrained("smcleod/guardrails-v1") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c5dfe77135b042d6a191ff1ae3b1543bd699f9111662b1ddcfb4748281bd925a
- Size of remote file:
- 5.2 kB
- SHA256:
- ae1e8298a30223d178e36ca7450c66080178694a58143c63d02e7f81c7852308
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