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README.md

@@ -1,21 +1,91 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: markdown
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 246058.0
-    num_examples: 1
-  download_size: 216875
-  dataset_size: 246058.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+viewer: false
+tags:
+- ocr
+- document-processing
+- nanonets
+- markdown
+- uv-script
+- generated
 ---
+
+# Document OCR using Nanonets-OCR-s
+
+This dataset contains markdown-formatted OCR results from images in [/content/my_dataset](https://huggingface.co/datasets//content/my_dataset) using Nanonets-OCR-s.
+
+## Processing Details
+
+- **Source Dataset**: [/content/my_dataset](https://huggingface.co/datasets//content/my_dataset)
+- **Model**: [nanonets/Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s)
+- **Number of Samples**: 1
+- **Processing Time**: 4.6 minutes
+- **Processing Date**: 2025-08-11 09:33 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Batch Size**: 1
+- **Max Model Length**: 8,192 tokens
+- **Max Output Tokens**: 4,096
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+Nanonets-OCR-s 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
+
+## 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 OCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
+    /content/my_dataset \
+    <output-dataset> \
+    --image-column image \
+    --batch-size 1 \
+    --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)