Add DeepSeek-OCR script (Transformers-based)

Initial implementation using official transformers API.
- Multiple resolution modes (Tiny/Small/Base/Large/Gundam)
- Sequential processing (no batching)
- Full dataset processing support
- Dataset card generation
- Compatible with HF Jobs

To test before updating README.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

deepseek-ocr.py

@@ -0,0 +1,586 @@
+# /// script
+# requires-python = ">=3.11"
+# dependencies = [
+#     "datasets",
+#     "huggingface-hub[hf_transfer]",
+#     "pillow",
+#     "torch",
+#     "transformers",
+#     "tqdm",
+# ]
+#
+# ///
+
+"""
+Convert document images to markdown using DeepSeek-OCR with Transformers.
+
+This script processes images through the DeepSeek-OCR model to extract
+text and structure as markdown, using the official Transformers API.
+
+Features:
+- Multiple resolution modes (Tiny/Small/Base/Large/Gundam)
+- LaTeX equation recognition
+- Table extraction and formatting
+- Document structure preservation
+- Image grounding and descriptions
+- Multilingual support
+
+Note: This script processes images sequentially (no batching) using the
+official transformers API. It's slower than vLLM-based scripts but uses
+the well-supported official implementation.
+"""
+
+import argparse
+import json
+import logging
+import os
+import sys
+import tempfile
+from datetime import datetime
+from typing import Optional
+
+import torch
+from datasets import load_dataset
+from huggingface_hub import DatasetCard, login
+from PIL import Image
+from tqdm.auto import tqdm
+from transformers import AutoModel, AutoTokenizer
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Resolution mode presets
+RESOLUTION_MODES = {
+    "tiny": {"base_size": 512, "image_size": 512, "crop_mode": False},
+    "small": {"base_size": 640, "image_size": 640, "crop_mode": False},
+    "base": {"base_size": 1024, "image_size": 1024, "crop_mode": False},
+    "large": {"base_size": 1280, "image_size": 1280, "crop_mode": False},
+    "gundam": {"base_size": 1024, "image_size": 640, "crop_mode": True},  # Dynamic resolution
+}
+
+
+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 create_dataset_card(
+    source_dataset: str,
+    model: str,
+    num_samples: int,
+    processing_time: str,
+    resolution_mode: str,
+    base_size: int,
+    image_size: int,
+    crop_mode: bool,
+    image_column: str = "image",
+    split: str = "train",
+) -> str:
+    """Create a dataset card documenting the OCR process."""
+    model_name = model.split("/")[-1]
+
+    return f"""---
+tags:
+- ocr
+- document-processing
+- deepseek
+- deepseek-ocr
+- markdown
+- uv-script
+- generated
+---
+
+# Document OCR using {model_name}
+
+This dataset contains markdown-formatted OCR results from images in [{source_dataset}](https://huggingface.co/datasets/{source_dataset}) using DeepSeek-OCR.
+
+## Processing Details
+
+- **Source Dataset**: [{source_dataset}](https://huggingface.co/datasets/{source_dataset})
+- **Model**: [{model}](https://huggingface.co/{model})
+- **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**: `markdown`
+- **Dataset Split**: `{split}`
+- **Resolution Mode**: {resolution_mode}
+- **Base Size**: {base_size}
+- **Image Size**: {image_size}
+- **Crop Mode**: {crop_mode}
+
+## Model Information
+
+DeepSeek-OCR is a state-of-the-art document OCR model that excels at:
+- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
+- 📊 **Tables** - Extracted and formatted as HTML/markdown
+- 📝 **Document structure** - Headers, lists, and formatting maintained
+- 🖼️ **Image grounding** - Spatial layout and bounding box information
+- 🔍 **Complex layouts** - Multi-column and hierarchical structures
+- 🌍 **Multilingual** - Supports multiple languages
+
+### Resolution Modes
+
+- **Tiny** (512×512): Fast processing, 64 vision tokens
+- **Small** (640×640): Balanced speed/quality, 100 vision tokens
+- **Base** (1024×1024): High quality, 256 vision tokens
+- **Large** (1280×1280): Maximum quality, 400 vision tokens
+- **Gundam** (dynamic): Adaptive multi-tile processing for large documents
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format with preserved structure
+- `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 markdown text
+for example in dataset:
+    print(example["markdown"])
+    break
+
+# View all OCR models applied to this dataset
+inference_info = json.loads(dataset[0]["inference_info"])
+for info in inference_info:
+    print(f"Column: {{{{info['column_name']}}}} - Model: {{{{info['model_id']}}}}")
+```
+
+## Reproduction
+
+This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) DeepSeek OCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr.py \\
+    {source_dataset} \\
+    <output-dataset> \\
+    --resolution-mode {resolution_mode} \\
+    --image-column {image_column}
+```
+
+## Performance
+
+- **Processing Speed**: ~{num_samples / (float(processing_time.split()[0]) * 60):.1f} images/second
+- **Processing Method**: Sequential (Transformers API, no batching)
+
+Note: This uses the official Transformers implementation. For faster batch processing,
+consider using the vLLM version once DeepSeek-OCR is officially supported by vLLM.
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
+"""
+
+
+def process_single_image(
+    model,
+    tokenizer,
+    image: Image.Image,
+    prompt: str,
+    base_size: int,
+    image_size: int,
+    crop_mode: bool,
+) -> str:
+    """Process a single image through DeepSeek-OCR."""
+    # model.infer expects a file path, so save to temp file
+    with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp:
+        try:
+            # Convert to RGB if needed
+            if image.mode != "RGB":
+                image = image.convert("RGB")
+
+            # Save image
+            image.save(tmp.name, format="PNG")
+
+            # Run inference
+            result = model.infer(
+                tokenizer,
+                prompt=prompt,
+                image_file=tmp.name,
+                output_path="",  # Don't save intermediate files
+                base_size=base_size,
+                image_size=image_size,
+                crop_mode=crop_mode,
+                save_results=False,
+                test_compress=False,
+            )
+
+            return result if isinstance(result, str) else str(result)
+
+        finally:
+            # Clean up temp file
+            try:
+                os.unlink(tmp.name)
+            except:
+                pass
+
+
+def main(
+    input_dataset: str,
+    output_dataset: str,
+    image_column: str = "image",
+    model: str = "deepseek-ai/DeepSeek-OCR",
+    resolution_mode: str = "gundam",
+    base_size: Optional[int] = None,
+    image_size: Optional[int] = None,
+    crop_mode: Optional[bool] = None,
+    prompt: str = "<image>\n<|grounding|>Convert the document to markdown.",
+    hf_token: str = None,
+    split: str = "train",
+    max_samples: int = None,
+    private: bool = False,
+    shuffle: bool = False,
+    seed: int = 42,
+):
+    """Process images from HF dataset through DeepSeek-OCR model."""
+
+    # Check CUDA availability first
+    check_cuda_availability()
+
+    # Track processing start time
+    start_time = datetime.now()
+
+    # Enable HF_TRANSFER for faster downloads
+    os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
+
+    # Login to HF if token provided
+    HF_TOKEN = hf_token or os.environ.get("HF_TOKEN")
+    if HF_TOKEN:
+        login(token=HF_TOKEN)
+
+    # Determine resolution settings
+    if resolution_mode in RESOLUTION_MODES:
+        mode_config = RESOLUTION_MODES[resolution_mode]
+        final_base_size = base_size if base_size is not None else mode_config["base_size"]
+        final_image_size = image_size if image_size is not None else mode_config["image_size"]
+        final_crop_mode = crop_mode if crop_mode is not None else mode_config["crop_mode"]
+        logger.info(f"Using resolution mode: {resolution_mode}")
+    else:
+        # Custom mode - require all parameters
+        if base_size is None or image_size is None or crop_mode is None:
+            raise ValueError(
+                f"Invalid resolution mode '{resolution_mode}'. "
+                f"Use one of {list(RESOLUTION_MODES.keys())} or specify "
+                f"--base-size, --image-size, and --crop-mode manually."
+            )
+        final_base_size = base_size
+        final_image_size = image_size
+        final_crop_mode = crop_mode
+        resolution_mode = "custom"
+
+    logger.info(
+        f"Resolution: base_size={final_base_size}, "
+        f"image_size={final_image_size}, crop_mode={final_crop_mode}"
+    )
+
+    # 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")
+
+    # Initialize model
+    logger.info(f"Loading model: {model}")
+    tokenizer = AutoTokenizer.from_pretrained(model, trust_remote_code=True)
+
+    try:
+        model_obj = AutoModel.from_pretrained(
+            model,
+            _attn_implementation="flash_attention_2",
+            trust_remote_code=True,
+            use_safetensors=True,
+        )
+    except Exception as e:
+        logger.warning(f"Failed to load with flash_attention_2: {e}")
+        logger.info("Falling back to standard attention...")
+        model_obj = AutoModel.from_pretrained(
+            model,
+            trust_remote_code=True,
+            use_safetensors=True,
+        )
+
+    model_obj = model_obj.eval().cuda().to(torch.bfloat16)
+    logger.info("Model loaded successfully")
+
+    # Process images sequentially
+    all_markdown = []
+
+    logger.info(f"Processing {len(dataset)} images (sequential, no batching)")
+    logger.info("Note: This may be slower than vLLM-based scripts")
+
+    for i in tqdm(range(len(dataset)), desc="OCR processing"):
+        try:
+            image = dataset[i][image_column]
+
+            # Handle different image formats
+            if isinstance(image, dict) and "bytes" in image:
+                from io import BytesIO
+                image = Image.open(BytesIO(image["bytes"]))
+            elif isinstance(image, str):
+                image = Image.open(image)
+            elif not isinstance(image, Image.Image):
+                raise ValueError(f"Unsupported image type: {type(image)}")
+
+            # Process image
+            result = process_single_image(
+                model_obj,
+                tokenizer,
+                image,
+                prompt,
+                final_base_size,
+                final_image_size,
+                final_crop_mode,
+            )
+
+            all_markdown.append(result)
+
+        except Exception as e:
+            logger.error(f"Error processing image {i}: {e}")
+            all_markdown.append("[OCR FAILED]")
+
+    # Add markdown column to dataset
+    logger.info("Adding markdown column to dataset")
+    dataset = dataset.add_column("markdown", all_markdown)
+
+    # Handle inference_info tracking
+    logger.info("Updating inference_info...")
+
+    # Check for existing inference_info
+    if "inference_info" in dataset.column_names:
+        try:
+            existing_info = json.loads(dataset[0]["inference_info"])
+            if not isinstance(existing_info, list):
+                existing_info = [existing_info]
+        except (json.JSONDecodeError, TypeError):
+            existing_info = []
+        dataset = dataset.remove_columns(["inference_info"])
+    else:
+        existing_info = []
+
+    # Add new inference info
+    new_info = {
+        "column_name": "markdown",
+        "model_id": model,
+        "processing_date": datetime.now().isoformat(),
+        "resolution_mode": resolution_mode,
+        "base_size": final_base_size,
+        "image_size": final_image_size,
+        "crop_mode": final_crop_mode,
+        "prompt": prompt,
+        "script": "deepseek-ocr.py",
+        "script_version": "1.0.0",
+        "script_url": "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr.py",
+        "implementation": "transformers (sequential)",
+    }
+    existing_info.append(new_info)
+
+    # Add updated inference_info column
+    info_json = json.dumps(existing_info, ensure_ascii=False)
+    dataset = dataset.add_column("inference_info", [info_json] * len(dataset))
+
+    # Push to hub
+    logger.info(f"Pushing to {output_dataset}")
+    dataset.push_to_hub(output_dataset, private=private, token=HF_TOKEN)
+
+    # Calculate processing time
+    end_time = datetime.now()
+    processing_duration = end_time - start_time
+    processing_time = f"{processing_duration.total_seconds() / 60:.1f} minutes"
+
+    # Create and push dataset card
+    logger.info("Creating dataset card...")
+    card_content = create_dataset_card(
+        source_dataset=input_dataset,
+        model=model,
+        num_samples=len(dataset),
+        processing_time=processing_time,
+        resolution_mode=resolution_mode,
+        base_size=final_base_size,
+        image_size=final_image_size,
+        crop_mode=final_crop_mode,
+        image_column=image_column,
+        split=split,
+    )
+
+    card = DatasetCard(card_content)
+    card.push_to_hub(output_dataset, token=HF_TOKEN)
+    logger.info("✅ Dataset card created and pushed!")
+
+    logger.info("✅ OCR conversion complete!")
+    logger.info(
+        f"Dataset available at: https://huggingface.co/datasets/{output_dataset}"
+    )
+
+
+if __name__ == "__main__":
+    # Show example usage if no arguments
+    if len(sys.argv) == 1:
+        print("=" * 80)
+        print("DeepSeek-OCR to Markdown Converter (Transformers)")
+        print("=" * 80)
+        print("\nThis script converts document images to markdown using")
+        print("DeepSeek-OCR with the official Transformers API.")
+        print("\nFeatures:")
+        print("- Multiple resolution modes (Tiny/Small/Base/Large/Gundam)")
+        print("- LaTeX equation recognition")
+        print("- Table extraction and formatting")
+        print("- Document structure preservation")
+        print("- Image grounding and spatial layout")
+        print("- Multilingual support")
+        print("\nNote: Sequential processing (no batching). Slower than vLLM scripts.")
+        print("\nExample usage:")
+        print("\n1. Basic OCR conversion (Gundam mode - dynamic resolution):")
+        print("   uv run deepseek-ocr.py document-images markdown-docs")
+        print("\n2. High quality mode (Large - 1280×1280):")
+        print("   uv run deepseek-ocr.py scanned-pdfs extracted-text --resolution-mode large")
+        print("\n3. Fast processing (Tiny - 512×512):")
+        print("   uv run deepseek-ocr.py quick-test output --resolution-mode tiny")
+        print("\n4. Process a subset for testing:")
+        print("   uv run deepseek-ocr.py large-dataset test-output --max-samples 10")
+        print("\n5. Custom resolution:")
+        print("   uv run deepseek-ocr.py dataset output \\")
+        print("       --base-size 1024 --image-size 640 --crop-mode")
+        print("\n6. Running on HF Jobs:")
+        print("   hf jobs uv run --flavor l4x1 \\")
+        print('     --secrets HF_TOKEN \\')
+        print("     https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr.py \\")
+        print("       your-document-dataset \\")
+        print("       your-markdown-output")
+        print("\n" + "=" * 80)
+        print("\nFor full help, run: uv run deepseek-ocr.py --help")
+        sys.exit(0)
+
+    parser = argparse.ArgumentParser(
+        description="OCR images to markdown using DeepSeek-OCR (Transformers)",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Resolution Modes:
+  tiny      512×512 pixels, fast processing (64 vision tokens)
+  small     640×640 pixels, balanced (100 vision tokens)
+  base      1024×1024 pixels, high quality (256 vision tokens)
+  large     1280×1280 pixels, maximum quality (400 vision tokens)
+  gundam    Dynamic multi-tile processing (adaptive)
+
+Examples:
+  # Basic usage with default Gundam mode
+  uv run deepseek-ocr.py my-images-dataset ocr-results
+
+  # High quality processing
+  uv run deepseek-ocr.py documents extracted-text --resolution-mode large
+
+  # Fast processing for testing
+  uv run deepseek-ocr.py dataset output --resolution-mode tiny --max-samples 100
+
+  # Custom resolution settings
+  uv run deepseek-ocr.py dataset output --base-size 1024 --image-size 640 --crop-mode
+        """,
+    )
+
+    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(
+        "--model",
+        default="deepseek-ai/DeepSeek-OCR",
+        help="Model to use (default: deepseek-ai/DeepSeek-OCR)",
+    )
+    parser.add_argument(
+        "--resolution-mode",
+        default="gundam",
+        choices=list(RESOLUTION_MODES.keys()) + ["custom"],
+        help="Resolution mode preset (default: gundam)",
+    )
+    parser.add_argument(
+        "--base-size",
+        type=int,
+        help="Base resolution size (overrides resolution-mode)",
+    )
+    parser.add_argument(
+        "--image-size",
+        type=int,
+        help="Image tile size (overrides resolution-mode)",
+    )
+    parser.add_argument(
+        "--crop-mode",
+        action="store_true",
+        help="Enable dynamic multi-tile cropping (overrides resolution-mode)",
+    )
+    parser.add_argument(
+        "--prompt",
+        default="<image>\n<|grounding|>Convert the document to markdown.",
+        help="Prompt for OCR (default: grounding markdown conversion)",
+    )
+    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 the dataset before processing (useful for random sampling)",
+    )
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="Random seed for shuffling (default: 42)",
+    )
+
+    args = parser.parse_args()
+
+    main(
+        input_dataset=args.input_dataset,
+        output_dataset=args.output_dataset,
+        image_column=args.image_column,
+        model=args.model,
+        resolution_mode=args.resolution_mode,
+        base_size=args.base_size,
+        image_size=args.image_size,
+        crop_mode=args.crop_mode if args.crop_mode else None,
+        prompt=args.prompt,
+        hf_token=args.hf_token,
+        split=args.split,
+        max_samples=args.max_samples,
+        private=args.private,
+        shuffle=args.shuffle,
+        seed=args.seed,
+    )