welcome scidcc

.gitattributes

@@ -57,3 +57,4 @@ 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
+*.csv filter=lfs diff=lfs merge=lfs -text

SciDCC/prepare_scidcc.py

@@ -0,0 +1,61 @@
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# Load the CSV file
+df = pd.read_csv("SciDCC.csv")
+
+# Drop unnecessary columns
+df = df.drop(["Date", "Link", "Year"], axis=1)
+
+# Perform a single stratified split on the full dataset (ensuring order is maintained)
+train_idx, temp_idx = train_test_split(df.index, test_size=0.2, stratify=df["Category"], random_state=42)
+dev_idx, test_idx = train_test_split(temp_idx, test_size=0.5, stratify=df.loc[temp_idx, "Category"], random_state=42)
+
+
+# Function to compute min, max, and median word counts for Summary and Body
+def compute_word_stats(df, column):
+    word_counts = df[column].dropna().apply(lambda x: len(str(x).split()))
+    return {
+        "min": word_counts.min(),
+        "max": word_counts.max(),
+        "median": word_counts.median()
+    }
+
+
+# Function to save the split datasets and print category distributions + word count stats
+def save_splits(dataset_df, dataset_name):
+    """Saves train/dev/test splits into separate folders while maintaining consistent ordering."""
+    output_folder = f"{dataset_name}"
+    os.makedirs(output_folder, exist_ok=True)
+
+    train_df, dev_df, test_df = dataset_df.loc[train_idx], dataset_df.loc[dev_idx], dataset_df.loc[test_idx]
+
+    train_df.to_csv(os.path.join(output_folder, "train.csv"), index=False)
+    dev_df.to_csv(os.path.join(output_folder, "dev.csv"), index=False)
+    test_df.to_csv(os.path.join(output_folder, "test.csv"), index=False)
+
+    print(f"\nCategory Distribution for {output_folder}:")
+    print("Train Distribution:\n", train_df["Category"].value_counts())
+    print("Dev Distribution:\n", dev_df["Category"].value_counts())
+    print("Test Distribution:\n", test_df["Category"].value_counts())
+
+    # Compute word stats for Summary and Body (only for the "full" dataset)
+    if "Body" in dataset_df.columns and "Summary" in dataset_df.columns:
+        print("\nWord Count Statistics:")
+        for split_name, split_df in [("Train", train_df), ("Dev", dev_df), ("Test", test_df)]:
+            summary_stats = compute_word_stats(split_df, "Summary")
+            body_stats = compute_word_stats(split_df, "Body")
+            print(f"  {split_name} Split:")
+            print(
+                f"    Summary - Min: {summary_stats['min']}, Max: {summary_stats['max']}, Median: {summary_stats['median']}")
+            print(f"    Body - Min: {body_stats['min']}, Max: {body_stats['max']}, Median: {body_stats['median']}")
+        print("-" * 50)
+
+
+# Create dataset variations while keeping ordering the same
+save_splits(df[["Title", "Category"]], "title")  # Title only
+save_splits(df[["Title", "Summary",  "Category"]], "title_summary")  # Title, Summary, and Body
+save_splits(df[["Title", "Summary", "Body", "Category"]], "title_summary_body")  # Title, Summary, and Body
+
+print("All datasets successfully created with consistent ordering and word count stats printed!")

SciDCC/title/dev.csv

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SciDCC/title/test.csv

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SciDCC/title/train.csv

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SciDCC/title_summary/dev.csv

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SciDCC/title_summary/test.csv

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SciDCC/title_summary/train.csv

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SciDCC/title_summary_body/dev.csv

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SciDCC/title_summary_body/test.csv

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SciDCC/title_summary_body/train.csv

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