Update README.md

README.md

@@ -14,4 +14,65 @@ base_model:
 tags:
 - NeuralWave
 - Hackathon
+---
+## Overview
+
+This model serves to enhance the precision and accuracy of personal information detection by utilizing a reduced label set compared to its base model. Through this refinement, it aims to provide superior labeling precision for identifying personal information across multiple languages.
+
+---
+
+## Features
+
+- **Improved Precision**: By reducing the label set size from the base model, the model enhances the precision of the labeling procedure, ensuring more reliable identification of sensitive information.
+
+- **Model Versions**:
+- **Maximum Accuracy Focus**: This version aims to achieve the highest possible accuracy in the detection process, making it suitable for applications where minimizing errors is crucial.
+- **Maximum Precision Focus**: This variant is designed to maximize the precision of the detection, ideal for scenarios where false positives are particularly undesirable.
+
+---
+
+## Installation
+
+To run this model, you will need to install the dependencies:
+
+```bash
+pip install torch transformers safetensors
+```
+
+---
+
+## Usage
+
+Load and run the model using PyTorch and transformers:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+from safetensors.torch import load_file
+
+# Load the tokenizer
+tokenizer = BertTokenizerFast.from_pretrained("google-bert/bert-base-multilingual-cased")
+
+# Load the model
+model = AutoModel.from_pretrained('model-path/miniagent.pt', device_map='auto')
+# Alternatively, for the precision-focused model
+# model = AutoModel.from_pretrained('model-path/miniagent_precision', device_map='auto')
+
+# Example input
+text = "Your sensitive information string"
+
+# Tokenize and run the model
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+
+# Process outputs for analysis...
+```
+
+---
+
+## Evaluation
+
+- **Accuracy Model**: Focused on minimizing errors, evaluates to achieve the highest accuracy metrics.
+- **Precision Model**: Designed to minimize false positives, optimizing for precision-driven applications.
+
 ---