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+ ---
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+ tags:
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+ - ocr
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+ - document-processing
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+ - glm-ocr
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+ - markdown
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+ - uv-script
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+ - generated
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+ ---
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+
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+ # Document OCR using GLM-OCR
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+
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+ This dataset contains OCR results from images in [willwhim/wattsocr](https://huggingface.co/datasets/willwhim/wattsocr) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance.
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+
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+ ## Processing Details
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+
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+ - **Source Dataset**: [willwhim/wattsocr](https://huggingface.co/datasets/willwhim/wattsocr)
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+ - **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR)
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+ - **Task**: text recognition
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+ - **Number of Samples**: 20
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+ - **Processing Time**: 3.1 min
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+ - **Processing Date**: 2026-02-20 01:44 UTC
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+
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+ ### Configuration
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+
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+ - **Image Column**: `image`
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+ - **Output Column**: `markdown`
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+ - **Dataset Split**: `train`
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+ - **Batch Size**: 16
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+ - **Max Model Length**: 8,192 tokens
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+ - **Max Output Tokens**: 8,192
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+ - **Temperature**: 0.01
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+ - **Top P**: 1e-05
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+ - **GPU Memory Utilization**: 80.0%
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+
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+ ## Model Information
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+
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+ GLM-OCR is a compact, high-performance OCR model:
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+ - 0.9B parameters
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+ - 94.62% on OmniDocBench V1.5
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+ - CogViT visual encoder + GLM-0.5B language decoder
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+ - Multi-Token Prediction (MTP) loss for efficiency
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+ - Multilingual: zh, en, fr, es, ru, de, ja, ko
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+ - MIT licensed
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+
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+ ## Dataset Structure
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+
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+ The dataset contains all original columns plus:
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+ - `markdown`: The extracted text in markdown format
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+ - `inference_info`: JSON list tracking all OCR models applied to this dataset
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+
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+ ## Reproduction
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+
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+ ```bash
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+ uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr-v2.py \
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+ willwhim/wattsocr \
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+ <output-dataset> \
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+ --image-column image \
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+ --batch-size 16 \
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+ --task ocr
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+ ```
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+
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+ Generated with [UV Scripts](https://huggingface.co/uv-scripts) (glm-ocr-v2.py)