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
Update README.md
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README.md
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library_name: transformers
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license: apache-2.0
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base_model: distilbert-base-uncased
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tags:
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- generated_from_trainer
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model-index:
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- name: octopus-26.0.4
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results:
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---
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-
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**Model Card — Prompt Injection Classifier**
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Developer: Nolan Stark · Architecture: DistilBERT Base Uncased · Version: 26.0.4
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library_name: transformers
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license: apache-2.0
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base_model: distilbert-base-uncased
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pipeline_tag: text-classification
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language:
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- en
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tags:
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- ai-security
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- prompt-injection
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- safety
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- guardrail
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- distilbert
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- generated_from_trainer
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model-index:
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- name: octopus-26.0.4
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results:
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- task:
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type: text-classification
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name: Prompt Injection Detection
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metrics:
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- type: loss
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value: 0.0039
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name: Training Loss
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---
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# Octopus-26.0.4
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**Model Card — Prompt Injection Classifier**
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Developer: Nolan Stark · Architecture: DistilBERT Base Uncased · Version: 26.0.4
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