Update README.md

README.md

@@ -51,6 +51,121 @@ out, attentions = model(**tokenized_text, output_attentions=True)
 print(attentions[0].size())     # torch.Size([48, 12, 32, 32])
 ```
 
+## Usage (Batch)
+
+Here's a more fleshed out example showing how to run LUAR across many batches of data:
+
+```python
+
+import numpy as np
+import torch
+from termcolor import cprint
+from transformers import AutoModel, AutoTokenizer
+from tqdm import tqdm
+
+def generate_data(num_batches: int = 100, batch_size: int = 32, num_samples_per_author: int = 16):
+    """
+    Generator that produces dummy data for testing.
+    
+    Args:
+        num_batches (int): Total number of batches to yield.
+        batch_size (int): Number of authors per batch.
+        num_samples_per_author (int): Number of text samples per author.
+        
+    Yields:
+        list: A batch of data structured as a list of lists of strings.
+              Shape: (batch_size, num_samples_per_author)
+    """
+    s = "This is an example string."
+    for batch in tqdm(range(num_batches)):
+        # Create a batch where each element is a list of 's' repeated 'num_samples_per_author' times
+        yield [[s] * num_samples_per_author for _ in range(batch_size)]
+
+def flatten(l):
+    """
+    Helper function to flatten a 2D list into a 1D list.
+    
+    Args:
+        l (list): List of lists.
+        
+    Returns:
+        list: Flattened list.
+    """
+    return [item for sublist in l for item in sublist]
+
+def main():
+    cprint("Starting LUAR-MUD example script...", 'magenta')
+
+    # --- Model Loading ---
+    cprint("Loading model 'rrivera1849/LUAR-MUD'...", 'blue')
+    # trust_remote_code=True is required for custom model architectures like LUAR-MUD
+    model = AutoModel.from_pretrained("rrivera1849/LUAR-MUD", trust_remote_code=True)
+    
+    model.eval()
+    
+    # Check for CUDA availability and move model to appropriate device
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    cprint(f"Moving model to device: {device}", 'yellow')
+    model.to(device)
+
+    # --- Tokenizer Loading ---
+    cprint("Loading tokenizer...", 'blue')
+    tokenizer = AutoTokenizer.from_pretrained("rrivera1849/LUAR-MUD", trust_remote_code=True)
+
+    # --- Configuration ---
+    num_batches = 100
+    batch_size = 32
+    num_samples_per_author = 16
+    max_length = 512
+
+    cprint("\nConfiguration:", 'cyan')
+    print(f"  Batch Size: {batch_size}")
+    print(f"  Samples per Author: {num_samples_per_author}")
+    print(f"  Max Length: {max_length}")
+    print(f"  Device: {device}\n")
+
+    all_outputs = []
+    
+    cprint("Starting inference loop...", 'green')
+    
+    # context manager for disabling gradient calculation to save memory/compute
+    with torch.inference_mode():
+        for i, batch in enumerate(generate_data(num_batches=num_batches, batch_size=batch_size, num_samples_per_author=num_samples_per_author)):
+            if (i + 1) % 10 == 0:
+                print(f"  Processing batch {i + 1}...")
+
+            # Flatten the batch structure for tokenization: 
+            # (batch_size, num_samples) -> (batch_size * num_samples)
+            batch = flatten(batch)
+            
+            # Tokenize the flattened batch
+            inputs = tokenizer(batch, return_tensors="pt", padding=True, max_length=max_length, truncation=True)
+            
+            # Move inputs to the same device as the model
+            inputs = inputs.to(device)
+            
+            # Reshape input_ids and attention_mask to match the model's expected 3D input:
+            # (batch_size, num_samples_per_author, sequence_length)
+            inputs["input_ids"] = inputs["input_ids"].reshape(batch_size, num_samples_per_author, -1)
+            inputs["attention_mask"] = inputs["attention_mask"].reshape(batch_size, num_samples_per_author, -1)
+            
+            # Forward pass through the model
+            outputs = model(**inputs)
+            
+            # Move outputs back to CPU and convert to numpy for storage
+            all_outputs.append(outputs.cpu().numpy())
+
+    # Concatenate all batch results into a single array
+    # axis=0 corresponds to the batch dimension
+    all_outputs = np.concatenate(all_outputs, axis=0)
+    
+    cprint("\nInference complete!", 'green')
+    cprint(f"Final output shape: {all_outputs.shape}", attrs=['bold'])
+
+if __name__ == "__main__":
+    main()
+```
+
 ## Citing & Authors
 
 If you find this model helpful, feel free to cite our [publication](https://aclanthology.org/2021.emnlp-main.70.pdf).