Upload paddleocr-vl-1.5.py with huggingface_hub

paddleocr-vl-1.5.py

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+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets",
+#     "huggingface-hub",
+#     "pillow",
+#     "torch",
+#     "transformers>=4.45.0",
+#     "accelerate",
+#     "tqdm",
+#     "toolz",
+# ]
+# ///
+
+"""
+Convert document images to text/tables/formulas using PaddleOCR-VL-1.5 with transformers.
+
+PaddleOCR-VL-1.5 is a compact 0.9B OCR model that achieves 94.5% SOTA accuracy on
+OmniDocBench v1.5. It supports multiple task modes including OCR, table recognition,
+formula extraction, chart analysis, text spotting, and seal recognition.
+
+NOTE: This script uses transformers batch inference (not vLLM) because PaddleOCR-VL
+only supports vLLM in server mode, which doesn't fit the UV script pattern.
+Transformers batch inference via apply_chat_template(padding=True) provides
+efficient batching while keeping the single-command interface.
+
+Features:
+- 🎯 SOTA Performance: 94.5% on OmniDocBench v1.5
+- 🧩 Ultra-compact: Only 0.9B parameters
+- 📝 OCR mode: General text extraction to markdown
+- 📊 Table mode: HTML table recognition
+- 📐 Formula mode: LaTeX mathematical notation
+- 📈 Chart mode: Structured chart analysis
+- 🔍 Spotting mode: Text spotting with localization
+- 🔖 Seal mode: Seal/stamp recognition
+- 🌍 Multilingual support
+- ⚡ Fast inference with batch processing
+
+Model: PaddlePaddle/PaddleOCR-VL-1.5
+Backend: Transformers (batch inference)
+Performance: 94.5% SOTA on OmniDocBench v1.5
+"""
+
+import argparse
+import io
+import json
+import logging
+import math
+import os
+import sys
+from datetime import datetime
+from typing import Any, Dict, List, Union
+
+import torch
+from datasets import load_dataset
+from huggingface_hub import DatasetCard, login
+from PIL import Image
+from toolz import partition_all
+from tqdm.auto import tqdm
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+# Model configuration
+MODEL_ID = "PaddlePaddle/PaddleOCR-VL-1.5"
+
+# Task mode configurations from official PaddleOCR-VL-1.5 documentation
+TASK_MODES = {
+    "ocr": "OCR:",
+    "table": "Table Recognition:",
+    "formula": "Formula Recognition:",
+    "chart": "Chart Recognition:",
+    "spotting": "Spotting:",
+    "seal": "Seal Recognition:",
+}
+
+# Task descriptions for dataset card
+TASK_DESCRIPTIONS = {
+    "ocr": "General text extraction to markdown format",
+    "table": "Table extraction to HTML format",
+    "formula": "Mathematical formula recognition to LaTeX",
+    "chart": "Chart and diagram analysis",
+    "spotting": "Text spotting with localization",
+    "seal": "Seal and stamp recognition",
+}
+
+# Max pixels configuration - spotting uses higher resolution
+MAX_PIXELS = {
+    "spotting": 2048 * 28 * 28,  # Higher resolution for spotting
+    "default": 1280 * 28 * 28,
+}
+
+
+def check_cuda_availability():
+    """Check if CUDA is available and exit if not."""
+    if not torch.cuda.is_available():
+        logger.error("CUDA is not available. This script requires a GPU.")
+        logger.error("Please run on a machine with a CUDA-capable GPU.")
+        sys.exit(1)
+    else:
+        logger.info(f"CUDA is available. GPU: {torch.cuda.get_device_name(0)}")
+
+
+def smart_resize(
+    height: int,
+    width: int,
+    factor: int = 28,
+    min_pixels: int = 28 * 28 * 130,
+    max_pixels: int = 28 * 28 * 1280,
+) -> tuple[int, int]:
+    """
+    PaddleOCR-VL's intelligent resize logic.
+
+    Rescales the image so that:
+    1. Both dimensions are divisible by 'factor' (28)
+    2. Total pixels are within [min_pixels, max_pixels]
+    3. Aspect ratio is maintained as closely as possible
+
+    Args:
+        height: Original image height
+        width: Original image width
+        factor: Dimension divisibility factor (default: 28)
+        min_pixels: Minimum total pixels (default: 100,880)
+        max_pixels: Maximum total pixels (default: 1,003,520)
+
+    Returns:
+        Tuple of (new_height, new_width)
+    """
+    if height < factor:
+        width = round((width * factor) / height)
+        height = factor
+
+    if width < factor:
+        height = round((height * factor) / width)
+        width = factor
+
+    if max(height, width) / min(height, width) > 200:
+        logger.warning(
+            f"Extreme aspect ratio detected: {max(height, width) / min(height, width):.1f}"
+        )
+
+    h_bar = round(height / factor) * factor
+    w_bar = round(width / factor) * factor
+
+    if h_bar * w_bar > max_pixels:
+        beta = math.sqrt((height * width) / max_pixels)
+        h_bar = math.floor(height / beta / factor) * factor
+        w_bar = math.floor(width / beta / factor) * factor
+    elif h_bar * w_bar < min_pixels:
+        beta = math.sqrt(min_pixels / (height * width))
+        h_bar = math.ceil(height * beta / factor) * factor
+        w_bar = math.ceil(width * beta / factor) * factor
+
+    return h_bar, w_bar
+
+
+def prepare_image(
+    image: Union[Image.Image, Dict[str, Any], str],
+    task_mode: str = "ocr",
+    apply_smart_resize: bool = True,
+) -> Image.Image:
+    """
+    Prepare image for PaddleOCR-VL-1.5 processing.
+
+    Args:
+        image: PIL Image, dict with bytes, or file path
+        task_mode: Task mode (affects max_pixels for spotting)
+        apply_smart_resize: Whether to apply smart resize
+
+    Returns:
+        Processed PIL Image
+    """
+    # Convert to PIL Image if needed
+    if isinstance(image, Image.Image):
+        pil_img = image
+    elif isinstance(image, dict) and "bytes" in image:
+        pil_img = Image.open(io.BytesIO(image["bytes"]))
+    elif isinstance(image, str):
+        pil_img = Image.open(image)
+    else:
+        raise ValueError(f"Unsupported image type: {type(image)}")
+
+    # Convert to RGB
+    pil_img = pil_img.convert("RGB")
+
+    # Apply smart resize if requested
+    if apply_smart_resize:
+        max_pixels = MAX_PIXELS.get(task_mode, MAX_PIXELS["default"])
+        original_size = pil_img.size
+        new_height, new_width = smart_resize(
+            pil_img.height, pil_img.width, max_pixels=max_pixels
+        )
+        if (new_width, new_height) != (pil_img.width, pil_img.height):
+            pil_img = pil_img.resize((new_width, new_height), Image.Resampling.LANCZOS)
+            logger.debug(f"Resized image from {original_size} to {pil_img.size}")
+
+    return pil_img
+
+
+def create_message(image: Image.Image, task_mode: str) -> List[Dict]:
+    """
+    Create chat message for PaddleOCR-VL-1.5 processing.
+
+    Args:
+        image: Prepared PIL Image
+        task_mode: Task mode (ocr, table, formula, chart, spotting, seal)
+
+    Returns:
+        Message in chat format
+    """
+    return [
+        {
+            "role": "user",
+            "content": [
+                {"type": "image", "image": image},
+                {"type": "text", "text": TASK_MODES[task_mode]},
+            ],
+        }
+    ]
+
+
+def create_dataset_card(
+    source_dataset: str,
+    model: str,
+    task_mode: str,
+    num_samples: int,
+    processing_time: str,
+    batch_size: int,
+    max_tokens: int,
+    apply_smart_resize: bool,
+    image_column: str = "image",
+    split: str = "train",
+) -> str:
+    """Create a dataset card documenting the OCR process."""
+    task_description = TASK_DESCRIPTIONS[task_mode]
+
+    return f"""---
+tags:
+- ocr
+- document-processing
+- paddleocr-vl-1.5
+- {task_mode}
+- uv-script
+- generated
+---
+
+# Document Processing using PaddleOCR-VL-1.5 ({task_mode.upper()} mode)
+
+This dataset contains {task_mode.upper()} results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using PaddleOCR-VL-1.5, an ultra-compact 0.9B SOTA OCR model.
+
+## Processing Details
+
+- **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
+- **Model**: [{model}](https://huggingface.co/{model})
+- **Task Mode**: `{task_mode}` - {task_description}
+- **Number of Samples**: {num_samples:,}
+- **Processing Time**: {processing_time}
+- **Processing Date**: {datetime.now().strftime("%Y-%m-%d %H:%M UTC")}
+
+### Configuration
+
+- **Image Column**: `{image_column}`
+- **Output Column**: `paddleocr_1.5_{task_mode}`
+- **Dataset Split**: `{split}`
+- **Batch Size**: {batch_size}
+- **Smart Resize**: {"Enabled" if apply_smart_resize else "Disabled"}
+- **Max Output Tokens**: {max_tokens:,}
+- **Backend**: Transformers (batch inference)
+
+## Model Information
+
+PaddleOCR-VL-1.5 is a state-of-the-art, resource-efficient model for document parsing:
+- 🎯 **SOTA Performance** - 94.5% on OmniDocBench v1.5
+- 🧩 **Ultra-compact** - Only 0.9B parameters
+- 📝 **OCR mode** - General text extraction
+- 📊 **Table mode** - HTML table recognition
+- 📐 **Formula mode** - LaTeX mathematical notation
+- 📈 **Chart mode** - Structured chart analysis
+- 🔍 **Spotting mode** - Text spotting with localization
+- 🔖 **Seal mode** - Seal and stamp recognition
+- 🌍 **Multilingual** - Support for multiple languages
+- ⚡ **Fast** - Efficient batch inference
+
+### Task Modes
+
+- **OCR**: Extract text content to markdown format
+- **Table Recognition**: Extract tables to HTML format
+- **Formula Recognition**: Extract mathematical formulas to LaTeX
+- **Chart Recognition**: Analyze and describe charts/diagrams
+- **Spotting**: Text spotting with location information
+- **Seal Recognition**: Extract text from seals and stamps
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `paddleocr_1.5_{task_mode}`: The extracted content based on task mode
+- `inference_info`: JSON list tracking all OCR models applied to this dataset
+
+## Usage
+
+```python
+from datasets import load_dataset
+import json
+
+# Load the dataset
+dataset = load_dataset("{{output_dataset_id}}", split="{split}")
+
+# Access the extracted content
+for example in dataset:
+    print(example["paddleocr_1.5_{task_mode}"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Task: {{info['task_mode']}} - Model: {{info['model_id']}}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) PaddleOCR-VL-1.5 script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \\
+    {source_dataset} \\
+    <output-dataset> \\
+    --task-mode {task_mode} \\
+    --image-column {image_column} \\
+    --batch-size {batch_size}
+```
+
+## Performance
+
+- **Model Size**: 0.9B parameters
+- **Benchmark Score**: 94.5% SOTA on OmniDocBench v1.5
+- **Processing Speed**: ~{num_samples / (float(processing_time.split()[0]) * 60):.2f} images/second
+- **Backend**: Transformers batch inference
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+"""
+
+
+def main(
+    input_dataset: str,
+    output_dataset: str,
+    image_column: str = "image",
+    batch_size: int = 8,
+    task_mode: str = "ocr",
+    max_tokens: int = 512,
+    apply_smart_resize: bool = True,
+    hf_token: str = None,
+    split: str = "train",
+    max_samples: int = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+    output_column: str = None,
+):
+    """Process images from HF dataset through PaddleOCR-VL-1.5 model."""
+
+    # Check CUDA availability first
+    check_cuda_availability()
+
+    # Track processing start time
+    start_time = datetime.now()
+
+    # Login to HF if token provided
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+
+    # Validate task mode
+    if task_mode not in TASK_MODES:
+        raise ValueError(
+            f"Invalid task_mode '{task_mode}'. Choose from: {list(TASK_MODES.keys())}"
+        )
+
+    # Auto-generate output column name based on task mode
+    if output_column is None:
+        output_column = f"paddleocr_1.5_{task_mode}"
+
+    logger.info(f"Using task mode: {task_mode} - {TASK_DESCRIPTIONS[task_mode]}")
+    logger.info(f"Output will be written to column: {output_column}")
+
+    # Load dataset
+    logger.info(f"Loading dataset: {input_dataset}")
+    dataset = load_dataset(input_dataset, split=split)
+
+    # Validate image column
+    if image_column not in dataset.column_names:
+        raise ValueError(
+            f"Column '{image_column}' not found. Available: {dataset.column_names}"
+        )
+
+    # Shuffle if requested
+    if shuffle:
+        logger.info(f"Shuffling dataset with seed {seed}")
+        dataset = dataset.shuffle(seed=seed)
+
+    # Limit samples if requested
+    if max_samples:
+        dataset = dataset.select(range(min(max_samples, len(dataset))))
+        logger.info(f"Limited to {len(dataset)} samples")
+
+    # Import transformers components
+    logger.info(f"Loading model: {MODEL_ID}")
+    logger.info("This may take a minute on first run...")
+
+    from transformers import AutoModelForImageTextToText, AutoProcessor
+
+    # Load processor and model
+    processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = (
+        AutoModelForImageTextToText.from_pretrained(
+            MODEL_ID,
+            torch_dtype=torch.bfloat16,
+            trust_remote_code=True,
+        )
+        .to("cuda")
+        .eval()
+    )
+
+    logger.info(f"Model loaded on {next(model.parameters()).device}")
+    logger.info(f"Processing {len(dataset)} images in batches of {batch_size}")
+    if apply_smart_resize:
+        max_pixels = MAX_PIXELS.get(task_mode, MAX_PIXELS["default"])
+        logger.info(f"Smart resize enabled (max_pixels={max_pixels:,})")
+
+    # Process images in batches
+    all_outputs = []
+
+    for batch_indices in tqdm(
+        partition_all(batch_size, range(len(dataset))),
+        total=(len(dataset) + batch_size - 1) // batch_size,
+        desc=f"PaddleOCR-VL-1.5 {task_mode.upper()}",
+    ):
+        batch_indices = list(batch_indices)
+        batch_images = [dataset[i][image_column] for i in batch_indices]
+
+        try:
+            # Prepare images and create messages
+            processed_images = [
+                prepare_image(
+                    img, task_mode=task_mode, apply_smart_resize=apply_smart_resize
+                )
+                for img in batch_images
+            ]
+
+            # Create messages for batch
+            batch_messages = [
+                create_message(img, task_mode) for img in processed_images
+            ]
+
+            # Process with transformers batch inference
+            inputs = processor.apply_chat_template(
+                batch_messages,
+                padding=True,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt",
+            ).to(model.device)
+
+            # Generate outputs
+            with torch.no_grad():
+                outputs = model.generate(
+                    **inputs,
+                    max_new_tokens=max_tokens,
+                    do_sample=False,
+                )
+
+            # Decode outputs - skip input tokens
+            input_len = inputs["input_ids"].shape[1]
+            generated_ids = outputs[:, input_len:]
+            results = processor.batch_decode(generated_ids, skip_special_tokens=True)
+
+            # Add to outputs
+            for text in results:
+                all_outputs.append(text.strip())
+
+        except Exception as e:
+            logger.error(f"Error processing batch: {e}")
+            # Add error placeholders for failed batch
+            all_outputs.extend(
+                [f"[{task_mode.upper()} ERROR: {str(e)[:100]}]"] * len(batch_images)
+            )
+
+    # Calculate processing time
+    processing_duration = datetime.now() - start_time
+    processing_time_str = f"{processing_duration.total_seconds() / 60:.1f} min"
+
+    # Add output column to dataset
+    logger.info(f"Adding '{output_column}' column to dataset")
+    dataset = dataset.add_column(output_column, all_outputs)
+
+    # Handle inference_info tracking (for multi-model comparisons)
+    inference_entry = {
+        "model_id": MODEL_ID,
+        "model_name": "PaddleOCR-VL-1.5",
+        "model_size": "0.9B",
+        "task_mode": task_mode,
+        "column_name": output_column,
+        "timestamp": datetime.now().isoformat(),
+        "max_tokens": max_tokens,
+        "smart_resize": apply_smart_resize,
+        "backend": "transformers",
+    }
+
+    if "inference_info" in dataset.column_names:
+        # Append to existing inference info
+        logger.info("Updating existing inference_info column")
+
+        def update_inference_info(example):
+            try:
+                existing_info = (
+                    json.loads(example["inference_info"])
+                    if example["inference_info"]
+                    else []
+                )
+            except (json.JSONDecodeError, TypeError):
+                existing_info = []
+
+            existing_info.append(inference_entry)
+            return {"inference_info": json.dumps(existing_info)}
+
+        dataset = dataset.map(update_inference_info)
+    else:
+        # Create new inference_info column
+        logger.info("Creating new inference_info column")
+        inference_list = [json.dumps([inference_entry])] * len(dataset)
+        dataset = dataset.add_column("inference_info", inference_list)
+
+    # Push to hub
+    logger.info(f"Pushing to {output_dataset}")
+    dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
+
+    # Create and push dataset card
+    logger.info("Creating dataset card")
+    card_content = create_dataset_card(
+        source_dataset=input_dataset,
+        model=MODEL_ID,
+        task_mode=task_mode,
+        num_samples=len(dataset),
+        processing_time=processing_time_str,
+        batch_size=batch_size,
+        max_tokens=max_tokens,
+        apply_smart_resize=apply_smart_resize,
+        image_column=image_column,
+        split=split,
+    )
+
+    card = DatasetCard(card_content)
+    card.push_to_hub(output_dataset, token=HF_TOKEN)
+
+    logger.info("PaddleOCR-VL-1.5 processing complete!")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
+    )
+    logger.info(f"Processing time: {processing_time_str}")
+    logger.info(
+        f"Processing speed: {len(dataset) / processing_duration.total_seconds():.2f} images/sec"
+    )
+    logger.info(f"Task mode: {task_mode} - {TASK_DESCRIPTIONS[task_mode]}")
+
+
+if __name__ == "__main__":
+    # Show example usage if no arguments
+    if len(sys.argv) == 1:
+        print("=" * 80)
+        print("PaddleOCR-VL-1.5 Document Processing")
+        print("=" * 80)
+        print("\nSOTA 0.9B OCR model (94.5% on OmniDocBench v1.5)")
+        print("\nFeatures:")
+        print("- 🎯 SOTA Performance - 94.5% on OmniDocBench v1.5")
+        print("- 🧩 Ultra-compact - Only 0.9B parameters")
+        print("- 📝 OCR mode - General text extraction")
+        print("- 📊 Table mode - HTML table recognition")
+        print("- 📐 Formula mode - LaTeX mathematical notation")
+        print("- 📈 Chart mode - Structured chart analysis")
+        print("- 🔍 Spotting mode - Text spotting with localization")
+        print("- 🔖 Seal mode - Seal and stamp recognition")
+        print("- 🌍 Multilingual support")
+        print("- ⚡ Fast batch inference with transformers")
+        print("\nTask Modes:")
+        for mode, description in TASK_DESCRIPTIONS.items():
+            print(f"  {mode:10} - {description}")
+        print("\nExample usage:")
+        print("\n1. Basic OCR (default mode):")
+        print("   uv run paddleocr-vl-1.5.py input-dataset output-dataset")
+        print("\n2. Table extraction:")
+        print("   uv run paddleocr-vl-1.5.py docs tables-extracted --task-mode table")
+        print("\n3. Formula recognition:")
+        print(
+            "   uv run paddleocr-vl-1.5.py papers formulas --task-mode formula --batch-size 16"
+        )
+        print("\n4. Text spotting (higher resolution):")
+        print("   uv run paddleocr-vl-1.5.py images spotted --task-mode spotting")
+        print("\n5. Seal recognition:")
+        print("   uv run paddleocr-vl-1.5.py seals recognized --task-mode seal")
+        print("\n6. Test with small sample:")
+        print("   uv run paddleocr-vl-1.5.py dataset test --max-samples 10 --shuffle")
+        print("\n7. Running on HF Jobs:")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print("     -s HF_TOKEN \\")
+        print(
+            "     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/paddleocr-vl-1.5.py \\"
+        )
+        print("       input-dataset output-dataset --task-mode ocr")
+        print("\n" + "=" * 80)
+        print("\nBackend: Transformers (batch inference)")
+        print(
+            "Note: Uses transformers instead of vLLM for single-command UV script compatibility"
+        )
+        print("\nFor full help, run: uv run paddleocr-vl-1.5.py --help")
+        sys.exit(0)
+
+    parser = argparse.ArgumentParser(
+        description="Document processing using PaddleOCR-VL-1.5 (0.9B SOTA model, transformers backend)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Task Modes:
+  ocr       General text extraction to markdown (default)
+  table     Table extraction to HTML format
+  formula   Mathematical formula recognition to LaTeX
+  chart     Chart and diagram analysis
+  spotting  Text spotting with localization (higher resolution)
+  seal      Seal and stamp recognition
+
+Examples:
+  # Basic text OCR
+  uv run paddleocr-vl-1.5.py my-docs analyzed-docs
+
+  # Extract tables from documents
+  uv run paddleocr-vl-1.5.py papers tables --task-mode table
+
+  # Recognize mathematical formulas
+  uv run paddleocr-vl-1.5.py textbooks formulas --task-mode formula
+
+  # Text spotting (uses higher resolution)
+  uv run paddleocr-vl-1.5.py images spotted --task-mode spotting
+
+  # Seal recognition
+  uv run paddleocr-vl-1.5.py documents seals --task-mode seal
+
+  # Test with random sampling
+  uv run paddleocr-vl-1.5.py large-dataset test --max-samples 50 --shuffle --task-mode ocr
+
+  # Disable smart resize for original resolution
+  uv run paddleocr-vl-1.5.py images output --no-smart-resize
+
+Backend: Transformers batch inference (not vLLM)
+        """,
+    )
+
+    parser.add_argument("input_dataset", help="Input dataset ID from Hugging Face Hub")
+    parser.add_argument("output_dataset", help="Output dataset ID for Hugging Face Hub")
+    parser.add_argument(
+        "--image-column",
+        default="image",
+        help="Column containing images (default: image)",
+    )
+    parser.add_argument(
+        "--batch-size",
+        type=int,
+        default=8,
+        help="Batch size for processing (default: 8)",
+    )
+    parser.add_argument(
+        "--task-mode",
+        choices=list(TASK_MODES.keys()),
+        default="ocr",
+        help="Task type: ocr (default), table, formula, chart, spotting, or seal",
+    )
+    parser.add_argument(
+        "--max-tokens",
+        type=int,
+        default=512,
+        help="Maximum tokens to generate (default: 512)",
+    )
+    parser.add_argument(
+        "--no-smart-resize",
+        action="store_true",
+        help="Disable smart resize, use original image size",
+    )
+    parser.add_argument("--hf-token", help="Hugging Face API token")
+    parser.add_argument(
+        "--split", default="train", help="Dataset split to use (default: train)"
+    )
+    parser.add_argument(
+        "--max-samples",
+        type=int,
+        help="Maximum number of samples to process (for testing)",
+    )
+    parser.add_argument(
+        "--private", action="store_true", help="Make output dataset private"
+    )
+    parser.add_argument(
+        "--shuffle", action="store_true", help="Shuffle dataset before processing"
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for shuffling (default: 42)",
+    )
+    parser.add_argument(
+        "--output-column",
+        help="Column name for output (default: paddleocr_1.5_[task_mode])",
+    )
+
+    args = parser.parse_args()
+
+    main(
+        input_dataset=args.input_dataset,
+        output_dataset=args.output_dataset,
+        image_column=args.image_column,
+        batch_size=args.batch_size,
+        task_mode=args.task_mode,
+        max_tokens=args.max_tokens,
+        apply_smart_resize=not args.no_smart_resize,
+        hf_token=args.hf_token,
+        split=args.split,
+        max_samples=args.max_samples,
+        private=args.private,
+        shuffle=args.shuffle,
+        seed=args.seed,
+        output_column=args.output_column,
+    )