Text Classification
Transformers
Safetensors
English
deberta-v2
prompt-injection
prompt-injection-detection
llm-security
llm-safety
ai-safety
deberta
Eval Results (legacy)
text-embeddings-inference
Instructions to use JHC04567/spid-deberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JHC04567/spid-deberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JHC04567/spid-deberta-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JHC04567/spid-deberta-base") model = AutoModelForSequenceClassification.from_pretrained("JHC04567/spid-deberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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- text-classification
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base_model: microsoft/deberta-v3-base
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datasets:
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- walledai/JailbreakHub
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- walledai/AdvBench
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metrics:
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- precision
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- recall
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- text-classification
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base_model: microsoft/deberta-v3-base
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datasets:
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- walledai/AdvBench
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- walledai/JailbreakHub
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- databricks/databricks-dolly-15k
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- Anthropic/hh-rlhf
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- OpenAssistant/oasst1
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- deepset/prompt-injections
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metrics:
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- precision
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- recall
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