README.md

---
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**: Transformers (PyTorch), GPT-2
- **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**: Hugging Face 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

### Local Usage

To use the **Finlytic-Categorize** model locally, follow these steps:

1. **Installation**: Clone the model repository from Hugging Face or use the local model by loading it with Hugging Face’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("comethrusws/finlytic-categorize")
   model = AutoModel.from_pretrained("comethrusws/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.

### Using Inference API

You can also use the **Finlytic-Categorize** model via the Hugging Face Inference API.

```python
import requests

API_URL = "https://api-inference.huggingface.co/models/comethrusws/finlytic-categorize"
headers = {"Authorization": "Bearer YOUR_API_KEY"}

data = {
    "inputs": "Categorize this expense: 'Software purchase, $200.'"
}

response = requests.post(API_URL, headers=headers, json=data)
print(response.json())
```

Replace `YOUR_API_KEY` with your Hugging Face API key.

## 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]).
```

This version updates the framework section to `Transformers (PyTorch), GPT-2` and includes a working example of how to use the inference API. You can now copy and paste this into your `README.md` file on Hugging Face.

Let me know if you need any further tweaks!