Update README.md

README.md

@@ -131,6 +131,55 @@ The Claude-augmented version demonstrates the highest correlation with human jud
 
 ComeTH-Augmented outperforms direct evaluations from state-of-the-art LLMs, while being more computationally efficient for large-scale translation quality assessments.
 
+## Advanced Usage Examples
+
+### Basic Evaluation
+
+```python
+from comet import download_model, load_from_checkpoint
+model_path = download_model("wasanx/ComeTH")
+model = load_from_checkpoint(model_path)
+
+translations = [
+    {
+        "src": "This is an English source text.",
+        "mt": "นี่คือข้อความภาษาอังกฤษ",
+    }
+]
+results = model.predict(translations, batch_size=8, gpus=1)
+scores = results['scores']
+```
+
+### Batch Processing With Progress Tracking
+
+```python
+import pandas as pd
+from tqdm import tqdm
+
+df = pd.read_csv("translations.csv")
+input_data = df[['src', 'mt']].to_dict('records')
+
+batch_size = 32
+all_scores = []
+
+for i in tqdm(range(0, len(input_data), batch_size)):
+    batch = input_data[i:i+batch_size]
+    results = model.predict(batch, batch_size=len(batch), gpus=1)
+    all_scores.extend(results['scores'])
+
+df['quality_score'] = all_scores
+```
+
+### System-Level Evaluation
+
+```python
+import numpy as np
+
+systems = df.groupby('system_name')['quality_score'].agg(['mean', 'std', 'count']).reset_index()
+systems = systems.sort_values('mean', ascending=False)
+print(systems)
+```
+
 ## License
 
 ```