Text Classification
Transformers
Safetensors
English
distilbert
ai-security
prompt-injection
safety
guardrail
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use sapirrior/octopus-26.0.4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sapirrior/octopus-26.0.4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="sapirrior/octopus-26.0.4")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("sapirrior/octopus-26.0.4") model = AutoModelForSequenceClassification.from_pretrained("sapirrior/octopus-26.0.4") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bd4bfd7f9ae2870a5a0412a8175483f29eaae6091d75ac3d6e8234d6a190e4f
- Size of remote file:
- 268 MB
- SHA256:
- 4494ed968c1dce506e68131ca0a9581a09f370c3089a1642dd26b7a4431aee3a
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