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README.md

@@ -1,29 +1,99 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: page_num
-    dtype: int64
-  - name: source_file
-    dtype: string
-  - name: source_path
-    dtype: string
-  - name: total_pages
-    dtype: int64
-  - name: markdown
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 235658705
-    num_examples: 21
-  download_size: 16684734
-  dataset_size: 235658705
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- ocr
+- document-processing
+- lighton-ocr
+- markdown
+- uv-script
+- generated
 ---
+
+# Document OCR using LightOnOCR-0.9B-32k-1025
+
+This dataset contains OCR results from images in [stckmn/ocr-input-Directive017-1761353279](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761353279) using LightOnOCR, a fast and compact 1B OCR model.
+
+## Processing Details
+
+- **Source Dataset**: [stckmn/ocr-input-Directive017-1761353279](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761353279)
+- **Model**: [lightonai/LightOnOCR-0.9B-32k-1025](https://huggingface.co/lightonai/LightOnOCR-0.9B-32k-1025)
+- **Vocabulary Size**: 32k tokens
+- **Number of Samples**: 21
+- **Processing Time**: 1.2 min
+- **Processing Date**: 2025-10-25 00:50 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Batch Size**: 32
+- **Target Image Size**: 1288px (longest dimension)
+- **Max Model Length**: 8,192 tokens
+- **Max Output Tokens**: 6,500
+- **Temperature**: 0.2
+- **Top P**: 0.9
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+LightOnOCR is a fast, compact OCR model that excels at:
+- ⚡ **Production Speed** - 5.71 pages/second on H100 GPU
+- 🎯 **Compact Size** - Only 1B parameters
+- 📐 **LaTeX formulas** - Mathematical notation in LaTeX format
+- 📊 **Tables** - Extracted and formatted as markdown
+- 📝 **Document structure** - Hierarchy and layout preservation
+- 🌍 **Multilingual** - Optimized for European languages
+- 🔤 **Flexible vocabulary** - 151k/32k/16k token variants
+
+### Vocabulary Variants
+
+- **151k tokens**: Full vocabulary, supports all languages
+- **32k tokens**: European languages optimized (~12% faster decoding)
+- **16k tokens**: European languages optimized (~12% faster decoding)
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format with LaTeX formulas
+- `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) LightOnOCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \
+    stckmn/ocr-input-Directive017-1761353279 \
+    <output-dataset> \
+    --vocab-size 32k \
+    --image-column image \
+    --batch-size 32
+```
+
+## Performance
+
+- **Processing Speed**: ~0.29 images/second
+- **Benchmark Score**: 76.1% overall (across diverse document types)
+- **Optimization**: Native resolution ViT + lightweight decoder
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)