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
Download added_tokens.json from JHC04567/spid-deberta-base: direct link, hf CLI and curl.
- Browser
- Download file 23 Bytes
-
https://huggingface.co/JHC04567/spid-deberta-base/resolve/main/added_tokens.json
- Command line
-
hf download hf://JHC04567/spid-deberta-base/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/JHC04567/spid-deberta-base/resolve/main/added_tokens.json
23 Bytes
| { | |
| "[MASK]": 128000 | |
| } | |