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README.md

@@ -1,21 +1,94 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: markdown
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 156551
-    num_examples: 1
-  download_size: 150485
-  dataset_size: 156551
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- ocr
+- document-processing
+- nanonets
+- nanonets-ocr2
+- markdown
+- uv-script
+- generated
 ---
+
+# Document OCR using Nanonets-OCR2-3B
+
+This dataset contains markdown-formatted OCR results from images in [Alysonhower/test](https://huggingface.co/datasets/Alysonhower/test) using Nanonets-OCR2-3B.
+
+## Processing Details
+
+- **Source Dataset**: [Alysonhower/test](https://huggingface.co/datasets/Alysonhower/test)
+- **Model**: [nanonets/Nanonets-OCR2-3B](https://huggingface.co/nanonets/Nanonets-OCR2-3B)
+- **Model Size**: 3.75B parameters
+- **Number of Samples**: 1
+- **Processing Time**: 4.4 minutes
+- **Processing Date**: 2025-10-14 21:10 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Batch Size**: 16
+- **Max Model Length**: 8,192 tokens
+- **Max Output Tokens**: 4,096
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+Nanonets-OCR2-3B 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
+- 📝 **Document structure** - Headers, lists, and formatting maintained
+- 🖼️ **Images** - Captions and descriptions included in `<img>` tags
+- ☑️ **Forms** - Checkboxes rendered as ☐/☑
+- 🔖 **Watermarks** - Wrapped in `<watermark>` tags
+- 📄 **Page numbers** - Wrapped in `<page_number>` tags
+- 🌍 **Multilingual** - Supports multiple languages
+
+## 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) Nanonets OCR2 script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr2.py \
+    Alysonhower/test \
+    <output-dataset> \
+    --model nanonets/Nanonets-OCR2-3B \
+    --image-column image \
+    --batch-size 16 \
+    --max-model-len 8192 \
+    --max-tokens 4096 \
+    --gpu-memory-utilization 0.8
+```
+
+## Performance
+
+- **Processing Speed**: ~0.0 images/second
+- **GPU Configuration**: vLLM with 80% GPU memory utilization
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)