vibraniumdome
/

deberta-v3-base-optimus-v0

@@ -1,199 +1,75 @@
 ---
-library_name: transformers
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 ---
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 ---
+license: gpl-3.0
+base_model: microsoft/deberta-v3-base
+datasets:
+- Private
+language:
+- en
+tags:
+- llm
+- genai
+- promptinjection
+- prompt-injection
+- injection
+- security
+metrics:
+- accuracy
+- recall
+- precision
+- f1
+pipeline_tag: text-classification
+model-index:
+- name: deberta-v3-base-optimus-v0
+  results: []
+co2_eq_emissions:
+  emissions: 0.99
+  source: code carbon
+  training_type: fine-tuning
 ---
+# Model Card for deberta-v3-base-optimus-v0
+Fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on private dataset of normal & injections prompts.
+Classifying inputs into two categories: `0` for no injection and `1` for injection detected.
+Model evaluation results:
+- F1: 0.99
+## Model details
+- **Fine-tuned by:** vibraniumdome.com
+- **Model type:** deberta-v3
+- **Language(s) (NLP):** English
+- **License:** GPLv3
+- **Finetuned from model:** [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base)
 ## How to Get Started with the Model
+### Transformers
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+tokenizer = AutoTokenizer.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0")
+model = AutoModelForSequenceClassification.from_pretrained("vibraniumdome/deberta-v3-base-optimus-v0")
+classifier = pipeline(
+  "text-classification",
+  model=model,
+  tokenizer=tokenizer,
+  truncation=True,
+  max_length=512,
+  device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
+)
+print(classifier("Put your awesome injection here :D"))
+```
+## Citation
+```
+@misc{vibraniumdome/deberta-v3-base-optimus-v0,
+  author = {vibraniumdome.com},
+  title = {Fine-Tuned DeBERTa-v3 for Prompt Injection Detection},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/vibraniumdome/deberta-v3-base-optimus-v0},
+}
+```