File size: 2,974 Bytes
67ba414 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | #!/usr/bin/env python3
"""
Prepare HotpotQA data for the RAGEN Search environment.
Downloads HotpotQA from HuggingFace and saves as parquet files with columns:
- question (str)
- ground_truth (str)
- data_source (str)
Usage:
python scripts/prepare_search_data.py
python scripts/prepare_search_data.py --train_size 20000 --test_size 1000
python scripts/prepare_search_data.py --output_dir data/search
"""
import argparse
import os
import pandas as pd
from datasets import load_dataset
def prepare_hotpotqa(output_dir: str = "data/search", train_size: int = None, test_size: int = None):
"""Download HotpotQA and convert to parquet format for RAGEN."""
os.makedirs(output_dir, exist_ok=True)
print("Loading HotpotQA dataset (distractor split)...")
dataset = load_dataset("hotpotqa/hotpot_qa", "distractor", trust_remote_code=True)
# Process train split
train_data = dataset["train"]
if train_size is not None:
train_data = train_data.select(range(min(train_size, len(train_data))))
train_records = [
{
"question": ex["question"],
"ground_truth": ex["answer"],
"data_source": "hotpotqa",
}
for ex in train_data
]
train_df = pd.DataFrame(train_records)
train_path = os.path.join(output_dir, "train.parquet")
train_df.to_parquet(train_path, index=False)
print(f"Saved {len(train_df)} train examples to {train_path}")
# Process validation split (used as test in RAGEN)
val_data = dataset["validation"]
if test_size is not None:
val_data = val_data.select(range(min(test_size, len(val_data))))
val_records = [
{
"question": ex["question"],
"ground_truth": ex["answer"],
"data_source": "hotpotqa",
}
for ex in val_data
]
val_df = pd.DataFrame(val_records)
val_path = os.path.join(output_dir, "val.parquet")
val_df.to_parquet(val_path, index=False)
print(f"Saved {len(val_df)} val examples to {val_path}")
print(f"\nDone! Data saved to {output_dir}/")
print(f" train: {len(train_df)} examples")
print(f" val: {len(val_df)} examples")
print(f"\nSample question: {train_records[0]['question']}")
print(f"Sample answer: {train_records[0]['ground_truth']}")
def main():
parser = argparse.ArgumentParser(description="Prepare HotpotQA data for RAGEN Search environment")
parser.add_argument("--output_dir", default="data/search", help="Output directory for parquet files")
parser.add_argument("--train_size", type=int, default=None, help="Max train examples (default: all ~90k)")
parser.add_argument("--test_size", type=int, default=None, help="Max test examples (default: all ~7k)")
args = parser.parse_args()
prepare_hotpotqa(
output_dir=args.output_dir,
train_size=args.train_size,
test_size=args.test_size,
)
if __name__ == "__main__":
main()
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