README.md

metadata

tags:
  - ocr
  - document-processing
  - glm-ocr
  - markdown
  - uv-script
  - generated
  - hf-jobs
configs:
  - config_name: deepseek-ocr-2
    data_files:
      - split: train
        path: deepseek-ocr-2/train-*
dataset_info:
  config_name: deepseek-ocr-2
  features:
    - name: image
      dtype: image
    - name: b_number
      dtype: string
    - name: page_index
      dtype: int64
    - name: source_row
      dtype: int64
    - name: markdown
      dtype: string
    - name: inference_info
      dtype: string
  splits:
    - name: train
      num_bytes: 20402919
      num_examples: 50
  download_size: 20341827
  dataset_size: 20402919

Document OCR using GLM-OCR

This dataset contains OCR results from images in davanstrien/moh-bench-sample using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.

Processing Details

• Source Dataset: davanstrien/moh-bench-sample
• Model: zai-org/GLM-OCR
• Task: text recognition
• Number of Samples: 50
• Processing Time: 6.2 min
• Processing Date: 2026-07-08 16:45 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 (gpu) with the glm-ocr.py recipe from uv-scripts. Run it yourself:

hf jobs uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \
    davanstrien/moh-bench-sample \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --task ocr