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", device_map="auto")# 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
Update README.md
Browse files
README.md
CHANGED
|
@@ -53,8 +53,6 @@ model-index:
|
|
| 53 |
The key innovation is **fragment-based detection**: SPID splits input into fragments and classifies each independently, catching compound attacks where a malicious instruction hides behind a benign prefix.
|
| 54 |
|
| 55 |
> Full pipeline, training code, and demo videos: **[GitHub repository](https://github.com/JHC56/spid)**
|
| 56 |
-
>
|
| 57 |
-
> If you find this useful, a ⭐ on GitHub is appreciated!
|
| 58 |
|
| 59 |
## Demo
|
| 60 |
|
|
|
|
| 53 |
The key innovation is **fragment-based detection**: SPID splits input into fragments and classifies each independently, catching compound attacks where a malicious instruction hides behind a benign prefix.
|
| 54 |
|
| 55 |
> Full pipeline, training code, and demo videos: **[GitHub repository](https://github.com/JHC56/spid)**
|
|
|
|
|
|
|
| 56 |
|
| 57 |
## Demo
|
| 58 |
|