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@@ -57,3 +57,29 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+_del/dev.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multiclass_train_stratified.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multilabel_dev.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multilabel_dev_cleaned.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multilabel_test.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multilabel_test_cleaned.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multilabel_train.tsv filter=lfs diff=lfs merge=lfs -text
+_del/multilabel_train_cleaned.tsv filter=lfs diff=lfs merge=lfs -text
+_del/test.tsv filter=lfs diff=lfs merge=lfs -text
+_del/train.tsv filter=lfs diff=lfs merge=lfs -text
+multiclass_dev.tsv filter=lfs diff=lfs merge=lfs -text
+multiclass_test.tsv filter=lfs diff=lfs merge=lfs -text
+multiclass_train.tsv filter=lfs diff=lfs merge=lfs -text
+multiclass_train_stratified.tsv filter=lfs diff=lfs merge=lfs -text
+multilabel_dev.tsv filter=lfs diff=lfs merge=lfs -text
+multilabel_dev_cleaned.tsv filter=lfs diff=lfs merge=lfs -text
+multilabel_test.tsv filter=lfs diff=lfs merge=lfs -text
+multilabel_test_cleaned.tsv filter=lfs diff=lfs merge=lfs -text
+multilabel_train.tsv filter=lfs diff=lfs merge=lfs -text
+multilabel_train_cleaned.tsv filter=lfs diff=lfs merge=lfs -text
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+org[[:space:]]files/cleaned_test.tsv filter=lfs diff=lfs merge=lfs -text
+org[[:space:]]files/cleaned_train.tsv filter=lfs diff=lfs merge=lfs -text
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+org[[:space:]]files/test.tsv filter=lfs diff=lfs merge=lfs -text
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prep_labels.py

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+import pandas as pd
+import argparse
+
+def process_to_multiclass(file_path):
+    # Load the TSV file into a pandas DataFrame, treating "NA" as a valid string
+    df = pd.read_csv(file_path, sep='\t', keep_default_na=False)
+
+    # Rename the 'label' column to 'register'
+    if 'label' in df.columns:
+        df.rename(columns={'label': 'register'}, inplace=True)
+
+    # Define the allowed register labels
+    allowed_labels = {"IN", "OP", "NA", "IP", "ID", "HI", "LY", "SP", "OTHER"}
+
+    # Create the new 'label' column by filtering the allowed labels from the 'register' column
+    def extract_labels(register_value, index):
+        if pd.isna(register_value) or register_value == "":
+            print(f"Problematic row {index}: Missing 'register' value")
+            return ""  # Return an empty string or handle missing data as desired
+        try:
+            return ' '.join([label for label in register_value.split() if label in allowed_labels])
+        except Exception as e:
+            print(f"Problematic row {index}: {register_value} caused error: {e}")
+            return ""  # Handle invalid data gracefully
+
+    # Apply the extraction function with index tracking for debugging
+    df['label'] = df.apply(lambda row: extract_labels(row['register'], row.name), axis=1)
+
+    # Save the DataFrame back to a TSV file (overwrites the original)
+    output_file = 'output_' + file_path
+    df.to_csv(output_file, sep='\t', index=False)
+    print(f"File processed successfully. Output saved to {output_file}")
+
+def process_to_multilabel(file_path):
+    # Load the TSV file into a pandas DataFrame, treating "NA" as a valid string
+    df = pd.read_csv(file_path, sep='\t', keep_default_na=False)
+
+    # Rename the 'label' column to 'register'
+    if 'label' in df.columns:
+        df.rename(columns={'label': 'register'}, inplace=True)
+
+    # Create the new 'label' column by ws splitting the 'register' column and saving a list
+    def extract_labels(register_value, index):
+        try:
+            return register_value.split()
+        except Exception as e:
+            print(f"Problematic row {index}: {register_value} caused error: {e}")
+            return ""
+
+    # Apply the extraction function with index tracking for debugging
+    df['full_label'] = df.apply(lambda row: extract_labels(row['register'], row.name), axis=1)
+
+    # only keep major labels for the 'label' column
+    allowed_labels = {"IN", "OP", "NA", "IP", "ID", "HI", "LY", "SP", "OTHER"}
+    df['label'] = df['full_label'].apply(lambda x: [label for label in x if label in allowed_labels])
+
+    # Save the DataFrame back to a TSV file 
+    output_file = 'output_' + file_path
+    df.to_csv(output_file, sep='\t', index=False)
+    print(f"File processed successfully. Output saved to {output_file}")
+
+if __name__ == "__main__":
+    # Set up argument parser to take file name from the command line
+    parser = argparse.ArgumentParser(description="Process a TSV file by renaming the label column and filtering register labels.")
+    parser.add_argument('file_path', type=str, help="The path to the input TSV file")
+
+    # Parse the arguments
+    args = parser.parse_args()
+
+    # Call the function with the provided file path
+    process_to_multilabel(args.file_path)