Create generate.py

generate.py

@@ -0,0 +1,43 @@
+import pandas as pd
+from datasets import load_dataset
+
+# Load the SQuAD dataset
+squad_dataset = load_dataset("squad")
+
+# Convert the 'train' and 'validation' splits to pandas DataFrames
+train_df = pd.DataFrame(squad_dataset['train'])
+validation_df = pd.DataFrame(squad_dataset['validation'])
+
+
+import re
+import pandas as pd
+
+df = pd.concat([train_df, validation_df], ignore_index=True)
+
+
+def get_closest_sentence_section(text, provided_index):
+    # Regular expression to match any punctuation that ends a sentence
+    sentence_end_punctuation = r'[.!?]'
+    
+    # Find all occurrences of sentence-ending punctuation and their indices
+    punctuation_indices = [match.start() for match in re.finditer(sentence_end_punctuation, text)]
+    
+    # Add the start and end of the string as virtual punctuation indices
+    punctuation_indices = [-1] + punctuation_indices + [len(text)]
+    
+    # Find the closest punctuation index above (<= provided_index)
+    closest_above = max([idx for idx in punctuation_indices if idx < provided_index], default=0)
+    
+    # Find the closest punctuation index below (> provided_index)
+    closest_below = min([idx for idx in punctuation_indices if idx > provided_index], default=len(text))
+    
+    # Trim the string based on closest punctuation above and below
+    trimmed_text = text[closest_above + 1: closest_below + 1].strip()
+    
+    return trimmed_text
+
+# Trim the context to only the relevant sentence
+df['context'] = df.apply(lambda row: get_closest_sentence_section(row.context, row.answers.get('answer_start')[0]) , axis=1)
+df['answer'] = df.apply(lambda row: row.answers.get('text')[0] , axis=1)
+
+df[['title','context','question','answer']].to_parquet('tinysquad.parquet')