Create prepare.py

prepare.py

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+import random
+from glob import glob
+
+from datasets import load_dataset, Dataset, DatasetDict
+
+# Get all the token files
+token_files = glob('tokenized/*.tokens')
+total_files = len(token_files)
+
+print(f"Found {total_files} token files")
+
+# Set the split sizes
+train_size = 23
+dev_size = 8
+test_size = 8
+
+# Ensure we have enough files
+if total_files < (train_size + dev_size + test_size):
+    print(f"Warning: Not enough files ({total_files}) for the requested split sizes.")
+    # Adjust sizes proportionally if needed
+    total_requested = train_size + dev_size + test_size
+    train_size = int(total_files * (train_size / total_requested))
+    dev_size = int(total_files * (dev_size / total_requested))
+    test_size = total_files - train_size - dev_size
+
+# Randomly shuffle the files
+random.seed(42)  # For reproducibility
+random.shuffle(token_files)
+
+# Split the files
+train_files = token_files[:train_size]
+dev_files = token_files[train_size:train_size + dev_size]
+test_files = token_files[train_size + dev_size:train_size + dev_size + test_size]
+
+# Function to process a list of files and return data points
+def process_files(file_list):
+    result = []
+    for file in file_list:
+        tokens = []
+        ner_tags = []
+        
+        with open(file, 'r') as f:
+            for line in f:
+                line = line.strip()
+                
+                # If empty line, append current document and reset
+                if not line:
+                    if tokens:  # Only append if there are tokens (avoid empty entries)
+                        result.append({
+                            "tokens": tokens,
+                            "ner_tags": ner_tags,
+                            "file_name": file  # Include file name for reference
+                        })
+                        tokens = []
+                        ner_tags = []
+                    continue
+                
+                # Split line into components
+                parts = line.split()
+                
+                # Ensure we have at least 3 parts (token, POS, NER)
+                if len(parts) >= 3:
+                    token = parts[0]
+                    ner_tag = parts[2]  # NER tag is the 3rd column
+                    
+                    tokens.append(token)
+                    ner_tags.append(ner_tag)
+            
+            # Don't forget to add the last document if it exists
+            if tokens:
+                result.append({
+                    "tokens": tokens,
+                    "ner_tags": ner_tags,
+                    "file_name": file  # Include file name for reference
+                })
+    
+    return result
+
+# Process each split
+train_data = process_files(train_files)
+dev_data = process_files(dev_files)
+test_data = process_files(test_files)
+
+# Create datasets for each split
+train_dataset = Dataset.from_list(train_data)
+dev_dataset = Dataset.from_list(dev_data)
+test_dataset = Dataset.from_list(test_data)
+
+# Create a DatasetDict with train, dev, and test splits
+dataset_dict = DatasetDict({
+    "train": train_dataset,
+    "validation": dev_dataset,  # Using 'validation' as it's more standard
+    "test": test_dataset
+})
+
+# Print some statistics
+print(f"Train split: {len(train_data)} files")
+print(f"Validation split: {len(dev_data)} files")
+print(f"Test split: {len(test_data)} files")
+print(f"Dataset features: {train_dataset.features}")
+
+# Uncomment to push to Hub
+dataset_dict.push_to_hub('extraordinarylab/malware-text-db')