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README.md

@@ -1,29 +1,83 @@
 ---
-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: rolmocr_text
-    dtype: string
-  - name: inference_info
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 235659213
-    num_examples: 21
-  download_size: 16686725
-  dataset_size: 235659213
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+viewer: false
+tags:
+- ocr
+- text-extraction
+- rolmocr
+- uv-script
+- generated
 ---
+
+# OCR Text Extraction using RolmOCR
+
+This dataset contains extracted text from images in [stckmn/ocr-input-Directive017-1761353484](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761353484) using RolmOCR.
+
+## Processing Details
+
+- **Source Dataset**: [stckmn/ocr-input-Directive017-1761353484](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761353484)
+- **Model**: [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR)
+- **Number of Samples**: 21
+- **Processing Time**: 2.2 minutes
+- **Processing Date**: 2025-10-25 00:54 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `rolmocr_text`
+- **Dataset Split**: `train`
+- **Batch Size**: 16
+- **Max Model Length**: 16,384 tokens
+- **Max Output Tokens**: 8,192
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+RolmOCR is a fast, general-purpose OCR model based on Qwen2.5-VL-7B architecture. It extracts plain text from document images with high accuracy and efficiency.
+
+## Dataset Structure
+
+The dataset contains all original columns plus:
+- `rolmocr_text`: The extracted text from each image
+- `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 extracted text
+for example in dataset:
+    print(example["rolmocr_text"])
+    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) RolmOCR script:
+
+```bash
+uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/rolm-ocr.py \
+    stckmn/ocr-input-Directive017-1761353484 \
+    <output-dataset> \
+    --image-column image \
+    --batch-size 16 \
+    --max-model-len 16384 \
+    --max-tokens 8192 \
+    --gpu-memory-utilization 0.8
+```
+
+## Performance
+
+- **Processing Speed**: ~0.2 images/second
+- **GPU Configuration**: vLLM with 80% GPU memory utilization
+
+Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)