willwhim/anne-steele-steele-vol_1-3-1780

Document OCR using GLM-OCR

This dataset contains OCR results from images in willwhim/steele_vol_1-3 using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.

Processing Details

• —Source Dataset: willwhim/steele_vol_1-3 (DELETED)
• —Model: zai-org/GLM-OCR
• —Task: text recognition
• —Number of Samples: 820
• —Processing Time: 88.6 min
• —Processing Date: 2026-04-03 01:25 UTC

Results

• —See steele-poetry

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

uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr-v2.py \
    willwhim/steele_vol_1-3 \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --task ocr

Generated with UV Scripts (glm-ocr-v2.py)