Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- expense-categorization
+- financial-transactions
+- machine-learning
+- tax-compliance
+model-index:
+- name: Finlytic-Categorize
+  results:
+  - task:
+      type: expense-categorization
+    dataset:
+      name: finlytic-financial-data
+      type: financial-transactions
+    metrics:
+    - name: Accuracy
+      type: accuracy
+      value: 94
+    - name: Precision
+      type: precision
+      value: 91
+    - name: Recall
+      type: recall
+      value: 89
+    - name: F1-Score
+      type: f1
+      value: 90
+    source:
+      name: Internal Evaluation
+      url: https://huggingface.co/comethrusws/finlytic-categorize
+    base_model: openai-community/gpt2
+base_model:
+- openai-community/gpt2
+---
+
+# Finlytic-Categorize
+
+**Finlytic-Categorize** is an AI-powered machine learning model developed to automate the categorization of expenses for small and medium-sized enterprises (SMEs). This model is designed to simplify the financial accounting process by classifying business expenses into appropriate tax-related categories, ensuring efficiency, and minimizing errors.
+
+## Model Details
+
+- **Model Name**: Finlytic-Categorize
+- **Model Type**: Expense Categorization
+- **Framework**: TensorFlow, Scikit-learn, Keras
+- **Dataset**: The model is trained on financial transaction data, including diverse business expenses.
+- **Use Case**: Automating the process of categorizing expenses into tax-compliant categories for SMEs in Nepal.
+- **Hosting**: Huggingface model repository (currently used in a locally hosted setup)
+
+## Objective
+
+The model is designed to reduce manual effort and the likelihood of human errors when handling large amounts of financial data. By using **Finlytic-Categorize**, SMEs can easily categorize expenses and maintain accurate records for tax filing.
+
+## Model Architecture
+
+The model is based on a pre-trained transformer architecture, fine-tuned specifically for the task of expense categorization. The dataset used for fine-tuning includes annotated financial records with appropriate tax labels. 
+
+## How to Use
+
+To use the **Finlytic-Categorize** model locally, follow these steps:
+
+1. **Installation**: Clone the model repository from Huggingface or use the local model by loading it with Huggingface’s `transformers` library.
+   
+   ```bash
+   git clone https://huggingface.co/comethrusws/finlytic-categorize
+   ```
+   
+2. **Load the Model**:
+
+   ```python
+   from transformers import AutoTokenizer, AutoModel
+
+   tokenizer = AutoTokenizer.from_pretrained("path_to/finlytic-categorize")
+   model = AutoModel.from_pretrained("path_to/finlytic-categorize")
+   ```
+
+3. **Input**: Feed your financial data (in JSON, CSV, or any structured format). The model expects financial transaction descriptions and amounts.
+
+4. **Output**: The output will be the assigned tax category for each transaction. You can format this into a structured report or integrate it into your financial systems.
+
+## Dataset
+
+The model was trained on financial data with annotations, specifically curated for Nepalese businesses, covering a wide range of common expense types, such as:
+
+- Delivery charges
+- Software licenses
+- Employee training
+- Operational supplies
+
+## Evaluation
+
+The model was evaluated using a hold-out validation set and achieved high accuracy in categorizing business expenses. Specific metrics include:
+
+- **Accuracy**: 94%
+- **Precision**: 91%
+- **Recall**: 89%
+
+## Limitations
+
+- The model is tailored for Nepalese SMEs and may require re-training or fine-tuning for different tax laws or regions.
+- It is best suited for common expense categories and may not generalize well for very niche or rare expenses.
+
+## Future Improvements
+
+- Expand the model's training data to include more diverse financial transactions.
+- Fine-tune for region-specific tax categorization, making it more adaptable globally.
+
+## Contact
+
+For queries or contributions, reach out to the Finlytic development team at [[email protected])](mailto:[email protected]).