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semantic-dedupe.py

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+# /// script
+# requires-python = ">=3.9"
+# dependencies = [
+#     "semhash",
+#     "datasets",
+#     "huggingface-hub",
+#     "hf-transfer",
+# ]
+# ///
+
+"""
+Semantic deduplication for Hugging Face datasets using SemHash.
+
+This script removes duplicate or near-duplicate text samples from datasets based on
+semantic similarity, helping to clean training data and prevent train/test leakage.
+
+Example usage:
+    # Basic deduplication
+    uv run semantic-dedupe.py username/dataset text username/dataset-deduped
+
+    # With custom threshold and max samples for testing
+    uv run semantic-dedupe.py username/dataset text username/dataset-deduped \\
+        --threshold 0.85 --max-samples 1000
+
+    # Using HF Jobs with GPU
+    hf jobs uv run --flavor a10 \\
+        -e HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \\
+        https://huggingface.co/datasets/uv-scripts/deduplication/raw/main/semantic-dedupe.py \\
+        username/dataset text username/dataset-deduped
+"""
+
+import argparse
+import os
+import sys
+from datetime import datetime
+from typing import Optional
+
+from datasets import Dataset, load_dataset
+from huggingface_hub import DatasetCard, login
+from semhash import SemHash
+
+# Enable fast transfers
+os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Deduplicate a dataset using semantic similarity",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Basic usage
+  uv run semantic-dedupe.py imdb text imdb-deduped
+
+  # With options
+  uv run semantic-dedupe.py squad question squad-deduped --threshold 0.85 --method duplicates
+
+  # Test with small sample
+  uv run semantic-dedupe.py large-dataset text test-dedup --max-samples 100
+        """,
+    )
+    
+    parser.add_argument("dataset", help="Input dataset ID (e.g., 'imdb' or 'username/dataset')")
+    parser.add_argument("column", help="Text column to deduplicate on")
+    parser.add_argument("output_repo", help="Output dataset repository name")
+    
+    parser.add_argument(
+        "--split",
+        default="train",
+        help="Dataset split to process (default: train)",
+    )
+    parser.add_argument(
+        "--method",
+        choices=["duplicates", "outliers", "representatives"],
+        default="duplicates",
+        help="Deduplication method (default: duplicates)",
+    )
+    parser.add_argument(
+        "--threshold",
+        type=float,
+        default=0.9,
+        help="Similarity threshold for duplicates (default: 0.9)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=64,
+        help="Batch size for processing (default: 64)",
+    )
+    parser.add_argument(
+        "--max-samples",
+        type=int,
+        help="Maximum number of samples to process (for testing)",
+    )
+    parser.add_argument(
+        "--private",
+        action="store_true",
+        help="Create private dataset repository",
+    )
+    parser.add_argument(
+        "--hf-token",
+        default=os.environ.get("HF_TOKEN"),
+        help="Hugging Face API token (defaults to HF_TOKEN env var)",
+    )
+    
+    return parser.parse_args()
+
+
+def create_dataset_card(
+    original_dataset: str,
+    column: str,
+    method: str,
+    threshold: float,
+    original_size: int,
+    deduped_size: int,
+) -> str:
+    """Create a dataset card with deduplication information."""
+    reduction_pct = ((original_size - deduped_size) / original_size) * 100
+    
+    return f"""---
+viewer: false
+tags:
+- deduplication
+- semhash
+- uv-script
+---
+
+# Deduplicated {original_dataset}
+
+This dataset is a deduplicated version of [{original_dataset}](https://huggingface.co/datasets/{original_dataset}).
+
+## Deduplication Details
+
+- **Method**: {method}
+- **Column**: `{column}`
+- **Threshold**: {threshold}
+- **Original size**: {original_size:,} samples
+- **Deduplicated size**: {deduped_size:,} samples
+- **Reduction**: {reduction_pct:.1f}%
+- **Date**: {datetime.now().strftime("%Y-%m-%d %H:%M UTC")}
+
+## Method Description
+
+{get_method_description(method)}
+
+## How to Use
+
+```python
+from datasets import load_dataset
+
+# Load the deduplicated dataset
+dataset = load_dataset("{os.environ.get('HF_USERNAME', 'username')}/{os.path.basename(original_dataset)}-deduped")
+```
+
+## Reproduce
+
+This dataset was created using the [uv-scripts/deduplication](https://huggingface.co/datasets/uv-scripts/deduplication) tool:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/deduplication/raw/main/semantic-dedupe.py \\
+    {original_dataset} {column} {os.path.basename(original_dataset)}-deduped \\
+    --method {method} --threshold {threshold}
+```
+
+Generated with 🤖 UV Scripts
+"""
+
+
+def get_method_description(method: str) -> str:
+    """Get description for deduplication method."""
+    descriptions = {
+        "duplicates": "Removes semantic duplicates by finding samples with high similarity scores above the threshold.",
+        "outliers": "Removes outlier samples that have low similarity to other samples in the dataset.",
+        "representatives": "Keeps only representative samples, removing both duplicates and outliers.",
+    }
+    return descriptions.get(method, "Unknown method")
+
+
+def main():
+    args = parse_args()
+    
+    # Authenticate
+    if args.hf_token:
+        login(args.hf_token)
+    else:
+        print("Warning: No HF token provided. Using cached credentials or anonymous mode.")
+    
+    # Load dataset
+    print(f"Loading dataset: {args.dataset}")
+    dataset = load_dataset(args.dataset, split=args.split)
+    
+    # Apply max samples if specified
+    if args.max_samples:
+        dataset = dataset.select(range(min(args.max_samples, len(dataset))))
+        print(f"Limited to {len(dataset)} samples for testing")
+    
+    original_size = len(dataset)
+    
+    # Check if column exists
+    if args.column not in dataset.column_names:
+        print(f"Error: Column '{args.column}' not found in dataset")
+        print(f"Available columns: {', '.join(dataset.column_names)}")
+        sys.exit(1)
+    
+    # Initialize SemHash
+    print("Initializing SemHash...")
+    semhash = SemHash(
+        batch_size=args.batch_size,
+        show_progress=True,
+    )
+    
+    # Perform deduplication
+    print(f"Performing {args.method} deduplication on '{args.column}' column...")
+    
+    if args.method == "duplicates":
+        result = semhash.deduplicate(
+            dataset,
+            text_column=args.column,
+            threshold=args.threshold,
+        )
+    elif args.method == "outliers":
+        result = semhash.filter_outliers(
+            dataset,
+            text_column=args.column,
+            threshold=args.threshold,
+        )
+    elif args.method == "representatives":
+        result = semhash.get_representatives(
+            dataset,
+            text_column=args.column,
+            threshold=args.threshold,
+        )
+    else:
+        raise ValueError(f"Unknown method: {args.method}")
+    
+    deduped_size = len(result)
+    
+    # Print statistics
+    print(f"\nDeduplication complete!")
+    print(f"Original size: {original_size:,}")
+    print(f"Deduplicated size: {deduped_size:,}")
+    print(f"Removed: {original_size - deduped_size:,} ({((original_size - deduped_size) / original_size) * 100:.1f}%)")
+    
+    # Create dataset card
+    card = create_dataset_card(
+        args.dataset,
+        args.column,
+        args.method,
+        args.threshold,
+        original_size,
+        deduped_size,
+    )
+    
+    # Push to hub
+    print(f"\nPushing to hub: {args.output_repo}")
+    result.push_to_hub(
+        args.output_repo,
+        private=args.private,
+        commit_message=f"Deduplicated using {args.method} method",
+    )
+    
+    # Create and push dataset card
+    dataset_card = DatasetCard(card)
+    dataset_card.push_to_hub(args.output_repo)
+    
+    print(f"✅ Dataset successfully pushed to: https://huggingface.co/datasets/{args.output_repo}")
+
+
+if __name__ == "__main__":
+    main()