Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+tags:
+- injection
+- security
+- llm
+- prompt-injection
+---
+
+# Model Card for Vijil Prompt Injection
+
+## Model Details
+
+### Model Description
+
+This model is a fine-tuned version of ModernBert to classify prompt-injection prompts which can manipulate language models into producing unintended outputs.
+
+- **Developed by:** Vijil AI
+- **License:** apache-2.0
+- **Finetuned version of [ModernBERT](https://huggingface.co/docs/transformers/en/model_doc/modernbert)**
+
+## Uses
+
+Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. 
+The vijil/mbert-prompt-injection model is designed to enhance security in language model applications by detecting prompt-injection attacks.
+
+## How to Get Started with the Model
+
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("answerdotai/ModernBERT-base") 
+model = AutoModelForSequenceClassification.from_pretrained("vijil/mbert-prompt-injection")
+
+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("this is a prompt-injection prompt"))
+
+```
+
+## Training Details
+
+### Training Data
+
+The dataset used for training the model was taken from 
+
+[wildguardmix/train](https://huggingface.co/datasets/allenai/wildguardmix)
+and
+[safe-guard-prompt-injection/train](https://huggingface.co/datasets/xTRam1/safe-guard-prompt-injection)
+
+### Training Procedure
+
+Supervised finetuning with above dataset 
+
+#### Training Hyperparameters
+
+* learning_rate: 5e-05
+
+* train_batch_size: 32
+
+* eval_batch_size: 32
+
+* optimizer: adamw_torch_fused
+
+* lr_scheduler_type: cosine_with_restarts 
+
+* warmup_ratio: 0.1       
+
+* num_epochs: 3          
+
+## Evaluation
+
+* Training Loss: 0.0036
+
+* Validation Loss: 0.209392
+
+* Accuracy: 0.961538
+
+* Precision: 0.958362 
+
+* Recall: 0.957055 
+
+* Fl: 0.957708
+
+#### Testing Data
+
+The dataset used for training the model was taken from 
+
+[wildguardmix/test](https://huggingface.co/datasets/allenai/wildguardmix)
+and
+[safe-guard-prompt-injection/test](https://huggingface.co/datasets/xTRam1/safe-guard-prompt-injection)
+
+### Results
+
+
+
+## Model Card Contact
+https://vijil.ai