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davanstrien
/

glm-ocr-test-v3

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document-processing

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Document OCR using GLM-OCR

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

Processing Details

Source Dataset: davanstrien/ufo-ColPali
Model: zai-org/GLM-OCR
Task: text recognition
Number of Samples: 10
Processing Time: 2.1 min
Processing Date: 2026-02-05 14:35 UTC

Configuration

Image Column: image
Output Column: markdown
Dataset Split: train
Batch Size: 16
Max Model Length: 8,192 tokens
Max Output Tokens: 16,384
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.py \
    davanstrien/ufo-ColPali \
    <output-dataset> \
    --image-column image \
    --batch-size 16 \
    --task ocr

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