#!/usr/bin/env python3 """ BitTransformerLM Dataset Creation Script Usage: python create_dataset.py --token YOUR_HF_TOKEN --repo-id YOUR_REPO_NAME This script creates a comprehensive dataset for BitTransformerLM training and uploads it to HuggingFace Hub with proper metadata and organization. """ import argparse import sys from pathlib import Path # Add the bit_transformer module to path sys.path.insert(0, str(Path(__file__).parent)) from bit_transformer.dataset_builder import create_bittransformerlm_dataset def main(): parser = argparse.ArgumentParser(description="Create BitTransformerLM Dataset") parser.add_argument("--token", required=True, help="HuggingFace access token") parser.add_argument("--repo-id", default="BitTransformerLM", help="Dataset repository ID") parser.add_argument("--private", action="store_true", default=True, help="Make dataset private") parser.add_argument("--samples", type=int, default=25000, help="Total number of samples") args = parser.parse_args() print("šŸš€ Starting BitTransformerLM Dataset Creation") print(f"Repository: {args.repo_id}") print(f"Private: {args.private}") print(f"Target samples: {args.samples}") print("-" * 50) try: dataset_url = create_bittransformerlm_dataset( hf_token=args.token, repo_id=args.repo_id ) print("\n" + "=" * 50) print("šŸŽ‰ SUCCESS! Dataset created and uploaded") print(f"šŸ“ URL: {dataset_url}") print("=" * 50) print("\nšŸ“‹ Next Steps:") print("1. View your dataset on HuggingFace Hub") print("2. Test loading with: `from datasets import load_dataset`") print("3. Integrate with BitTransformerLM training pipeline") print("4. Monitor dataset usage and performance metrics") except Exception as e: print(f"\nāŒ ERROR: {e}") print("Please check your token and repository permissions.") sys.exit(1) if __name__ == "__main__": main()