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README.md

@@ -1,27 +1,64 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: source
-    dtype: string
-  - name: page
-    dtype: int64
-  - name: markdown_glm_ocr
-    dtype: string
-  - name: inference_info
-    dtype: string
-  - name: markdown
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2446240.0
-    num_examples: 6
-  download_size: 2444021
-  dataset_size: 2446240.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+tags:
+- ocr
+- document-processing
+- glm-ocr
+- markdown
+- uv-script
+- generated
+- hf-jobs
 ---
+
+# Document OCR using GLM-OCR
+
+This dataset contains OCR results from images in [divinity-library/thg-test-01](https://huggingface.co/datasets/divinity-library/thg-test-01) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
+
+## Processing Details
+
+- **Source Dataset**: [divinity-library/thg-test-01](https://huggingface.co/datasets/divinity-library/thg-test-01)
+- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
+- **Task**: text recognition
+- **Number of Samples**: 6
+- **Processing Time**: 1.9 min
+- **Processing Date**: 2026-10-28 02:23 UTC
+
+### Configuration
+
+- **Image Column**: `image`
+- **Output Column**: `markdown`
+- **Dataset Split**: `train`
+- **Batch Size**: 16
+- **Max Model Length**: 8,192 tokens
+- **Max Output Tokens**: 8,192
+- **Temperature**: 0.01
+- **Top P**: 1e-05
+- **GPU Memory Utilization**: 80.0%
+
+## Model Information
+
+GLM-OCR is a compact, high-performance OCR model:
+- 0.9B parameters
+- 94.62% on OmniDocBench V1.5
+- CogViT visual encoder + GLM-0.5B language decoder
+- Multi-Token Prediction (MTP) loss for efficiency
+- Multilingual: zh, en, fr, es, ru, de, ja, ko
+- MIT licensed
+
+## 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
+
+## Reproduction
+
+Produced on [Hugging Face Jobs](https://huggingface.co/docs/huggingface_hub/guides/jobs) (`a10g-small`) with the [`glm-ocr.py`](https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py) recipe from [uv-scripts](https://huggingface.co/uv-scripts). Run it yourself:
+
+```bash
+hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
+    divinity-library/thg-test-01 \
+    <output-dataset> \
+    --image-column image \
+    --batch-size 16 \
+    --task ocr
+```