Create transform.py

transform.py

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+from datasets import load_dataset
+import pandas as pd
+import re
+import json
+
+# Load SQuAD dataset from Hugging Face
+dataset = load_dataset('squad')
+
+# Define a function to find the previous sentence-ending punctuation before a given index
+def find_previous_punctuation(text, start_index):
+    # Regex pattern for sentence-ending punctuation
+    punctuation_pattern = r"[.?!;]"
+    # Find all matching punctuation positions
+    matches = list(re.finditer(punctuation_pattern, text[:start_index]))
+    if matches:
+        # Return the position just after the last match before start_index
+        return matches[-1].end()
+    return 0  # Return the start of the string if no punctuation is found
+
+# Define a function to find the next sentence-ending punctuation after a given index
+def find_next_punctuation(text, start_index):
+    # Regex pattern for sentence-ending punctuation
+    punctuation_pattern = r"[.?!;]"
+    # Search for the next punctuation after start_index
+    match = re.search(punctuation_pattern, text[start_index:])
+    if match:
+        # Return the index relative to the original string
+        return start_index + match.end()
+    return len(text)  # Return end of the string if no punctuation is found
+
+# Extract the specific row's context from the dataset
+def get_row_context(row):
+    # Get the starting index of the answer
+    answer_idx = row.get('answers').get('answer_start')[0]  # Assuming the first answer
+    # Find the previous sentence-ending punctuation before the answer
+    start_idx = find_previous_punctuation(row['context'], answer_idx)
+    # Find the next sentence-ending punctuation after the answer
+    end_idx = find_next_punctuation(row['context'], answer_idx + len(row.get('answers').get('text')[0]))
+    # Return the substring of context containing the answer and its surrounding context
+    return row['context'][start_idx:end_idx].strip()
+
+# Function to join and process the dataset into a Pandas DataFrame
+def join_squad_dataset(dataset):
+    # Convert the dataset into a Pandas DataFrame
+    df = pd.DataFrame(dataset)  # Use 'train' split
+    # Apply `get_row_context` to generate the context sentence
+    df['context'] = df.apply(get_row_context, axis=1)
+    df['answer'] = df['answers'].apply(lambda x: x['text'][0])
+    # Return the processed DataFrame with required columns
+    return df[['context', 'question', 'answer']]
+
+# Process the dataset
+train_df = join_squad_dataset(dataset['train'])
+test_df = join_squad_dataset(dataset['validation'])
+
+with open("train.json", "w", encoding="utf-8") as f:
+        json.dump(train_df.to_dict(orient='records'), f, indent=4, ensure_ascii=False)
+
+with open("validation.json", "w", encoding="utf-8") as f:
+        json.dump(test_df.to_dict(orient='records'), f, indent=4, ensure_ascii=False)