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  ---
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- dataset_info:
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- features:
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- - name: image
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- dtype: image
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- - name: raw_queries
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- list: string
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- - name: broad_topical_query
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- dtype: string
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- - name: broad_topical_explanation
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- dtype: string
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- - name: specific_detail_query
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- dtype: string
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- - name: specific_detail_explanation
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- dtype: string
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- - name: visual_element_query
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- dtype: string
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- - name: visual_element_explanation
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- dtype: string
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- - name: parsed_into_json
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- dtype: bool
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- - name: markdown
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- dtype: string
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- - name: inference_info
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- dtype: string
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- splits:
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- - name: train
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- num_bytes: 1269530.0
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- num_examples: 10
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- download_size: 1240913
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- dataset_size: 1269530.0
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- configs:
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- - config_name: default
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- data_files:
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- - split: train
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- path: data/train-*
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+ tags:
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+ - ocr
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+ - document-processing
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+ - lighton-ocr-2
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+ - markdown
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+ - uv-script
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+ - generated
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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+
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+ # Document OCR using LightOnOCR-2-1B
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+
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+ This dataset contains OCR results from images in [davanstrien/ufo-ColPali](https://huggingface.co/datasets/davanstrien/ufo-ColPali) using LightOnOCR-2, a fast and compact 1B OCR model trained with RLVR.
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+
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+ ## Processing Details
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+
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+ - **Source Dataset**: [davanstrien/ufo-ColPali](https://huggingface.co/datasets/davanstrien/ufo-ColPali)
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+ - **Model**: [lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B)
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+ - **Number of Samples**: 10
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+ - **Processing Time**: 2.9 min
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+ - **Processing Date**: 2026-01-29 14:30 UTC
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+
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+ ### Configuration
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+
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+ - **Image Column**: `image`
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+ - **Output Column**: `markdown`
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+ - **Dataset Split**: `train`
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+ - **Batch Size**: 16
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+ - **Target Image Size**: 1540px (longest dimension)
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+ - **Max Model Length**: 8,192 tokens
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+ - **Max Output Tokens**: 6,144
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+ - **Temperature**: 0.2
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+ - **Top P**: 0.9
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+ - **GPU Memory Utilization**: 80.0%
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+
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+ ## Model Information
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+
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+ LightOnOCR-2 is a next-generation fast, compact OCR model that excels at:
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+ - ⚡ **Fastest Speed** - 42.8 pages/second on H100 GPU (7× faster than v1)
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+ - 🎯 **High Accuracy** - 83.2 ± 0.9% on OlmOCR-Bench (+7.1% vs v1)
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+ - 🧠 **RLVR Training** - Eliminates repetition loops and formatting errors
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+ - 📚 **Better Dataset** - 2.5× larger training data with cleaner annotations
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+ - 📐 **LaTeX formulas** - Mathematical notation in LaTeX format
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+ - 📊 **Tables** - Extracted and formatted as markdown
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+ - 📝 **Document structure** - Hierarchy and layout preservation
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+ - 🌍 **Multilingual** - Optimized for European languages
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+ - 💪 **Production-ready** - Outperforms models 9× larger
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+
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+ ### Key Improvements over v1
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+
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+ - **7.5× faster**: 42.8 vs 5.71 pages/sec on H100
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+ - **+7.1% accuracy**: 83.2% vs 76.1% on benchmarks
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+ - **Better quality**: RLVR training eliminates common OCR errors
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+ - **Cleaner output**: No repetition loops or formatting glitches
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+ - **Simpler**: Single model (no vocabulary variants)
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+
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+ ## Dataset Structure
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+
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+ The dataset contains all original columns plus:
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+ - `markdown`: The extracted text in markdown format with LaTeX formulas
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+ - `inference_info`: JSON list tracking all OCR models applied to this dataset
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+
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+ ## Usage
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+
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+ ```python
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+ from datasets import load_dataset
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+ import json
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+
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+ # Load the dataset
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+ dataset = load_dataset("{output_dataset_id}", split="train")
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+
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+ # Access the markdown text
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+ for example in dataset:
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+ print(example["markdown"])
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+ break
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+
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+ # View all OCR models applied to this dataset
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+ inference_info = json.loads(dataset[0]["inference_info"])
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+ for info in inference_info:
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+ print(f"Column: {info['column_name']} - Model: {info['model_id']}")
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+ ```
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+
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+ ## Reproduction
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+
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+ This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) LightOnOCR-2 script:
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+
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+ ```bash
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+ uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
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+ davanstrien/ufo-ColPali \
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+ <output-dataset> \
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+ --image-column image \
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+ --batch-size 16
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+ ```
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+
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+ ## Performance
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+
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+ - **Processing Speed**: ~0.06 images/second
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+ - **Benchmark Score**: 83.2 ± 0.9% on OlmOCR-Bench
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+ - **Training**: RLVR (Reinforcement Learning with Verifiable Rewards)
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+
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+ Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)