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README.md

@@ -1,53 +1,93 @@
 ---
-dataset_info:
-  features:
-  - name: image_id
-    dtype: int64
-  - name: image
-    dtype: image
-  - name: width
-    dtype: int32
-  - name: height
-    dtype: int32
-  - name: objects
-    list:
-    - name: bw_id
-      dtype: string
-    - name: category_id
-      dtype:
-        class_label:
-          names:
-            '0': Photograph
-            '1': Illustration
-            '2': Map
-            '3': Comics/Cartoon
-            '4': Editorial Cartoon
-            '5': Headline
-            '6': Advertisement
-    - name: image_id
-      dtype: string
-    - name: id
-      dtype: int64
-    - name: area
-      dtype: int64
-    - name: bbox
-      list: float32
-      length: 4
-    - name: iscrowd
-      dtype: bool
-  - name: markdown
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 43226365
-    num_examples: 128
-  download_size: 41020193
-  dataset_size: 43226365
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+viewer: false
+tags:
+- ocr
+- document-processing
+- dots-ocr
+- multilingual
+- markdown
+- uv-script
+- generated
 ---
+
+# Document OCR using dots.ocr
+
+This dataset contains OCR results from images in [biglam/loc_beyond_words](https://huggingface.co/datasets/biglam/loc_beyond_words) using DoTS.ocr, a compact 1.7B multilingual model.
+
+## Processing Details
+
+- **Source Dataset**: [biglam/loc_beyond_words](https://huggingface.co/datasets/biglam/loc_beyond_words)
+- **Model**: [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr)
+- **Number of Samples**: 128
+- **Processing Time**: 5.2 min
+- **Processing Date**: 2025-10-07 14:37 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Batch Size**: 128
+- **Prompt Mode**: layout-all
+- **Max Model Length**: 8,192 tokens
+- **Max Output Tokens**: 8,192
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+DoTS.ocr is a compact multilingual document parsing model that excels at:
+- 🌍 **100+ Languages** - Multilingual document support
+- 📊 **Table extraction** - Structured data recognition
+- 📐 **Formulas** - Mathematical notation preservation
+- 📝 **Layout-aware** - Reading order and structure preservation
+- ⚡ **Fast inference** - 2-3x faster than native HF with vLLM
+- 🎯 **Compact** - Only 1.7B parameters
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `markdown`: The extracted text in markdown format
+- `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) DoTS OCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
+    biglam/loc_beyond_words \
+    <output-dataset> \
+    --image-column image \
+    --batch-size 128 \
+    --prompt-mode layout-all \
+    --max-model-len 8192 \
+    --max-tokens 8192 \
+    --gpu-memory-utilization 0.8
+```
+
+## Performance
+
+- **Processing Speed**: ~0.4 images/second
+- **GPU Configuration**: vLLM with 80% GPU memory utilization
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)