File size: 4,489 Bytes
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import argparse
import random
from pathlib import Path
from tqdm import tqdm
import datasets
from huggingface_hub import HfApi, RepoCard
from transformers import HfArgumentParser
random.seed(0)
def generate_unique_multiplication_data(a_max, b_max, n_train, n_test):
"""Generate train and test datasets for each multiplication range ensuring no overlap."""
datasets = {}
for a in range(1, a_max + 1):
for b in range(1, b_max + 1):
all_pairs = [(x, y) for x in range(1, a + 1) for y in range(1, b + 1)]
# Convert to list before sampling to avoid Python 3.9+ warning
test_data = set(random.sample(list(all_pairs), min(n_test, len(all_pairs))))
train_data = set(random.sample(list(set(all_pairs) - test_data), min(n_train, len(set(all_pairs) - test_data))))
datasets[f"{a}x{b}"] = {"train": list(train_data), "test": list(test_data)}
return datasets
def save_to_jsonl(data, file_path):
"""Save dataset to JSONL format."""
with open(file_path, "w") as f:
for a, b in data:
json.dump({"problem": f"What is {a} times {b}?", "answer": str(a * b)}, f)
f.write("\n")
def prepare_datasets(output_dir):
"""Prepare train and test datasets ensuring no overlap for all 1x1 to 15x15 combinations."""
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
all_datasets = generate_unique_multiplication_data(a_max=15, b_max=15, n_train=1000, n_test=100)
train_files, test_files = [], []
for name, data in all_datasets.items():
train_file = output_dir / f"multiplication_train_{name}.jsonl"
test_file = output_dir / f"multiplication_test_{name}.jsonl"
save_to_jsonl(data["train"], train_file)
save_to_jsonl(data["test"], test_file)
train_files.append(train_file)
test_files.append(test_file)
print(f"\n✅ Datasets saved to {output_dir}")
return train_files, test_files
def process_file(file_path):
"""Convert JSONL data into Hugging Face dataset format."""
with open(file_path, "r") as f:
data = [json.loads(line.strip()) for line in f if line.strip()]
dataset = {
"messages": [[
{"role": "user", "content": item["problem"]},
{"role": "assistant", "content": item["answer"]},
] for item in data],
"ground_truth": [item["answer"] for item in data],
"dataset": ["multiplication"] * len(data),
}
return datasets.Dataset.from_dict(dataset)
def push_to_huggingface(train_files, test_files, hf_entity):
"""Push datasets to Hugging Face Hub and print the dataset link."""
api = HfApi()
hf_entity = hf_entity or api.whoami()["name"]
print("\n📤 Uploading datasets to Hugging Face...\n")
for file in train_files + test_files:
dataset = process_file(file)
dataset_name = file.stem
repo_id = f"{hf_entity}/{dataset_name}"
hf_url = f"https://huggingface.co/datasets/{repo_id}"
print(f"✅ Dataset uploaded: {dataset_name}")
# print(f"🔗 Click to view: {hf_url}\n") # 👈 PRINTS THE LINK
dataset.push_to_hub(repo_id)
api.upload_file(
path_or_fileobj=__file__,
path_in_repo="create_dataset.py",
repo_type="dataset",
repo_id=repo_id,
)
# Add RepoCard with Hugging Face link
repo_card = RepoCard(
content=f"""\
# Multiplication Dataset - {dataset_name}
This dataset contains multiplication problems for numbers up to 15x15.
## Dataset Format
- `messages`: User question and assistant answer.
- `ground_truth`: Correct multiplication result.
- `dataset`: "multiplication"
## Hugging Face Dataset Link
➡️ [View dataset on Hugging Face]({hf_url})
"""
)
repo_card.push_to_hub(repo_id, repo_type="dataset")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--output_dir", type=str, default="math_data", help="Output directory")
parser.add_argument("--push_to_hub", action="store_true", help="Upload to Hugging Face")
parser.add_argument("--hf_entity", type=str, default=None, help="Hugging Face entity")
args = parser.parse_args()
train_files, test_files = prepare_datasets(args.output_dir)
if args.push_to_hub:
push_to_huggingface(train_files, test_files, args.hf_entity)
if __name__ == "__main__":
main()
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