Update README.md

README.md

@@ -43,10 +43,10 @@ base_model:
 
 - **Model Name**: Finlytic-Categorize
 - **Model Type**: Expense Categorization
-- **Framework**: TensorFlow, Scikit-learn, Keras
+- **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**: Huggingface model repository (currently used in a locally hosted setup)
+- **Hosting**: Hugging Face model repository (currently used in a locally hosted setup)
 
 ## Objective
 
@@ -58,9 +58,11 @@ The model is based on a pre-trained transformer architecture, fine-tuned specifi
 
 ## How to Use
 
+### Local Usage
+
 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.
+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
@@ -71,14 +73,34 @@ To use the **Finlytic-Categorize** model locally, follow these steps:
    ```python
    from transformers import AutoTokenizer, AutoModel
 
-   tokenizer = AutoTokenizer.from_pretrained("path_to/finlytic-categorize")
-   model = AutoModel.from_pretrained("path_to/finlytic-categorize")
+   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:
@@ -108,4 +130,9 @@ The model was evaluated using a hold-out validation set and achieved high accura
 
 ## Contact
 
-For queries or contributions, reach out to the Finlytic development team at [[email protected])](mailto:[email protected]).
+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!