Update README.md

README.md

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-<h2>Overview<h2>
+---
+license: unknown
+---
+
+# Overview
+
+<!-- This model is obtained by finetuning Pre-Trained RoBERTa on dataset containing several sets of malicious prompts.
+Using this model, we can classify malicious prompts that can lead towards creation of phishing websites and phishing emails.  -->
+This model is obtained by finetuning a Pre-Trained RoBERTa using a dataset encompassing multiple sets of malicious prompts, as detailed in the corresponding arXiv paper. 
+Using this model, we can classify malicious prompts that can lead towards creation of phishing websites and phishing emails. 
+
+- **Paper:** 
+
+## Dataset Details
+
+The dataset utilized for this model is constructed from malicious prompts generated by GPT-4. 
+We have decided not to make it publicly available. However, it will be provided upon request.
+
+## Training Details
+
+The model was trained using RobertaForSequenceClassification.from_pretrained. 
+In this process, both the model and tokenizer pertinent to the RoBERTa-base were employed. 
+We trained this model for 10 epochs, setting a learning rate to 2e-5, and used AdamW Optimizer. 
+
+## Inference
+
+There are multiple ways to use this model. The simplest way to use is with pipeline "text-classification"
+
+```python
+from transformers import pipeline
+classifier = pipeline(task="text-classification", model="phishbot/Isitphish", top_k=None)
+prompt = ["Your Sample Sentence or Prompt...."]
+model_outputs = classifier(prompt)
+print(model_outputs[0])
+```
+
+### Results
+
+Achieved an accuracy of 96% with an F1-score of 0.96, on test sets distribution, explained in the paper. 
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+