Datasetpublic

davanstrien/glm-ocr-test-v3

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:… See the full description on the dataset page: https://huggingface.co/datasets/davanstrien/glm-ocr-test-v3.

sourceHugging Faceupdated 8mo agoView on Hugging Face

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Dataset Card

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

bash

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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