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README.md

@@ -37,6 +37,8 @@ You can try out the deployed model here: [Named Entity Recognition Demo](https:/
 
 Both models were fine-tuned on a premium A100 GPU in Google Colab for optimized training performance.
 
+**Note**: Due to its superior performance, the XLM-RoBERTa model was selected for deployment.
+
 ## Model Performance Metrics
 
 ### mBERT Model
@@ -67,17 +69,23 @@ Both models were fine-tuned on a premium A100 GPU in Google Colab for optimized
    cd named-entity-recognition
    ```
 
-2. **Install dependencies**:
+2. **Create and activate a virtual environment**:
+   ```bash
+   python3 -m venv .venv
+   source .venv/bin/activate  # On Windows use: .venv\Scripts\activate
+   ```
+
+3. **Install dependencies**:
    ```bash
    pip install -r requirements.txt
    ```
 
-3. **Run the FastAPI app**:
+4. **Run the FastAPI app**:
    ```bash
    uvicorn main:app --host 0.0.0.0 --port 8080
    ```
 
-4. **Deploy on Fly.io**:
+5. **Deploy on Fly.io**:
    Use the following steps to deploy the app on Fly.io.
 
 ## Fly.io Deployment
@@ -130,4 +138,4 @@ Access your deployed app at the Fly.io-provided URL (e.g., `https://your-app-nam
 
 Access the web interface through the Fly.io URL or `http://localhost:8080` (if running locally) to test the NER model and view recognized entities.
 
-This application leverages both mBERT and XLM-RoBERTa models fine-tuned on Azerbaijani language data for high-accuracy named entity recognition.
+This application leverages both mBERT and XLM-RoBERTa models fine-tuned on Azerbaijani language data for high-accuracy named entity recognition.