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README.md

@@ -1,24 +1,25 @@
 ---
 tags:
 - ocr
+- structured-extraction
 - document-processing
-- deepseek
-- deepseek-ocr-2
+- nuextract3
 - markdown
 - uv-script
 - generated
 ---
 
-# Document OCR using DeepSeek-OCR-2
+# NuExtract3 on davanstrien/moh-bench-sample
 
-This dataset contains markdown-formatted OCR results from images in [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample) using DeepSeek-OCR-2.
+This dataset contains outputs from [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample) processed with [NuExtract3](https://huggingface.co/numind/NuExtract3), a 4B vision-language model for document understanding.
 
 ## Processing Details
 
 - **Source Dataset**: [davanstrien/moh-bench-sample](https://huggingface.co/datasets/davanstrien/moh-bench-sample)
-- **Model**: [deepseek-ai/DeepSeek-OCR-2](https://huggingface.co/deepseek-ai/DeepSeek-OCR-2)
+- **Model**: [numind/NuExtract3](https://huggingface.co/numind/NuExtract3)
+- **Mode**: markdown
 - **Number of Samples**: 50
-- **Processing Time**: 4.4 min
+- **Processing Time**: 9.7 min
 - **Processing Date**: 2026-07-08 16:48 UTC
 
 ### Configuration
@@ -26,66 +27,14 @@ This dataset contains markdown-formatted OCR results from images in [davanstrien
 - **Image Column**: `image`
 - **Output Column**: `markdown`
 - **Dataset Split**: `train`
-- **Batch Size**: 8
-- **Max Model Length**: 8,192 tokens
-- **Max Output Tokens**: 8,192
-- **GPU Memory Utilization**: 80.0%
-
-## Model Information
-
-DeepSeek-OCR-2 is a 3B parameter vision-language model featuring Visual Causal
-Flow architecture for more human-like visual encoding. Building on DeepSeek-OCR v1,
-it offers enhanced document understanding with dynamic resolution up to
-(0-6)x768x768 + 1x1024x1024 patches.
-
-### Capabilities
-- 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
+- **Temperature**: 0.2
+- **Thinking Mode**: disabled
 
 ## 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="train")
-
-# 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-2 vLLM script:
-
-```bash
-uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr2-vllm.py \\
-    davanstrien/moh-bench-sample \\
-    <output-dataset> \\
-    --image-column image
-```
-
-## Performance
+Original columns plus:
+- `markdown`: NuExtract3 output (markdown)
+- `inference_info`: JSON list tracking models applied to this dataset
 
-- **Processing Speed**: ~0.2 images/second
-- **Processing Method**: Batch processing with vLLM (2-3x speedup over sequential)
 
 Generated with [UV Scripts](https://huggingface.co/uv-scripts)