Dataset Viewer
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191927103
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Encyclopaedia Britannica - Third edition, Volume 18, STR-ZYM - EB.5
| true |
Third edition
|
Third edition, Volume 18, STR-ZYM
|
1797
|
Encyclopaedia Britannica
|
EB.5
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144850377
| 768 |
188279784
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Encyclopaedia Britannica - Second edition, Volume 6, K-Medicine - EB.4
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Second edition
|
Second edition, Volume 6, K-Medicine
|
1778-83
|
Encyclopaedia Britannica
|
EB.4
| "4590\n\n**MEDICINE**\n\nnot found, or of a neutral and indifferent nature. These are the body itsel(...TRUNCATED) | "[{\"column_name\": \"markdown\", \"model_id\": \"deepseek-ai/DeepSeek-OCR\", \"processing_date\": \(...TRUNCATED) |
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190285942
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Encyclopaedia Britannica - Second edition, Volume 9, POI-SCU - EB.4
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Second edition
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Second edition, Volume 9, POI-SCU
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1778-83
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Encyclopaedia Britannica
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EB.4
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0.0
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191253807
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Volume 1, ABE-IMP
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1801
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Gleig, George
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EB.7
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192200901
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192726003
| "MAN\n[ 545 1\nMAN\nMan. heads arife from the compreffion they undergo in in-\nl —' fancy. This ra(...TRUNCATED) | "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<alto xmlns=\"http://www.loc.gov/standards/alto/v3/alto(...TRUNCATED) | true | true | "Encyclopaedia Britannica, or, a Dictionary of arts, sciences, and miscellaneous literature : enlarg(...TRUNCATED) | true |
Fifth edition
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Fifth edition, Volume 12, LIE-Materia medica
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1815
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EB.10
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193108326
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193251551
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Encyclopaedia Britannica - Eighth edition, Volume 18, PLA-REI - EB.16
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Eighth edition
|
Eighth edition, Volume 18, PLA-REI
|
1853-1860
|
Stewart, Dugald
|
Encyclopaedia Britannica
|
EB.16
| "351\n\n**Pupilation**\n\nThe whole of Sweden, where the annual mortality, at the time referred to b(...TRUNCATED) | "[{\"column_name\": \"markdown\", \"model_id\": \"deepseek-ai/DeepSeek-OCR\", \"processing_date\": \(...TRUNCATED) |
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191253803
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Encyclopaedia Britannica - Third edition, Volume 6, DIA-ETH - EB.6
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Third edition
|
Third edition, Volume 6, DIA-ETH
|
1797
|
Encyclopaedia Britannica
|
EB.6
|
1
E.B.6.
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192866681
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Volume 5
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1824
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Stewart, Dugald
|
Supplement to the fourth, fifth and sixth editions of the Encyclopaedia Britannica
|
EB.13
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149981670
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188743743
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Encyclopaedia Britannica - Third edition, Volume 8, GOB-HYD - EB.5
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Third edition
|
Third edition, Volume 8, GOB-HYD
|
1797
|
Encyclopaedia Britannica
|
EB.5
| "466\n\nRules of Heraldry.\n\nOf the Rules or Laws of HERALDRY.\n\nTHE several cefutcheons, tinétur(...TRUNCATED) | "[{\"column_name\": \"markdown\", \"model_id\": \"deepseek-ai/DeepSeek-OCR\", \"processing_date\": \(...TRUNCATED) |
|||
144133903
| 413 |
144809503
| "M\nU\no\nTheorem. The interrals of the notes of all (harp\nkeys and flat keys refpeclively, are pro(...TRUNCATED) | "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<alto xmlns=\"http://www.loc.gov/standards/alto/v3/alto(...TRUNCATED) | true | true | "Encyclopaedia Britannica; or, A dictionary of arts and sciences, compiled upon a new plan … - Fir(...TRUNCATED) | true |
First edition
|
First edition, 1771, Volume 3, M-Z
|
1771
|
Smellie, William
|
Encyclopaedia Britannica; or, A dictionary of arts and sciences, compiled upon a new plan
|
EB.1
| "353\n\n**M U S**\n\nThe intervals of the notes of all sharp keys and flat keys repeatedly, are prop(...TRUNCATED) | "[{\"column_name\": \"markdown\", \"model_id\": \"deepseek-ai/DeepSeek-OCR\", \"processing_date\": \(...TRUNCATED) |
End of preview. Expand
in Data Studio
Document OCR using DeepSeek-OCR
This dataset contains markdown-formatted OCR results from images in davanstrien/ency-test using DeepSeek-OCR.
Processing Details
- Source Dataset: davanstrien/ency-test
- Model: deepseek-ai/DeepSeek-OCR
- Number of Samples: 100
- Processing Time: 8.5 min
- Processing Date: 2025-10-22 17:48 UTC
Configuration
- Image Column:
image - Output Column:
markdown - Dataset Split:
train - Batch Size: 512
- Resolution Mode: large
- Base Size: 1280
- Image Size: 1280
- Crop Mode: False
- Max Model Length: 8,192 tokens
- Max Output Tokens: 8,192
- GPU Memory Utilization: 80.0%
Model Information
DeepSeek-OCR is a state-of-the-art document OCR model that excels at:
- 📐 LaTeX equations - Mathematical formulas preserved in LaTeX format
- 📊 Tables - Extracted and formatted as HTML/markdown
- 📝 Document structure - Headers, lists, and formatting maintained
- 🖼️ Image grounding - Spatial layout and bounding box information
- 🔍 Complex layouts - Multi-column and hierarchical structures
- 🌍 Multilingual - Supports multiple languages
Resolution Modes
- Tiny (512×512): Fast processing, 64 vision tokens
- Small (640×640): Balanced speed/quality, 100 vision tokens
- Base (1024×1024): High quality, 256 vision tokens
- Large (1280×1280): Maximum quality, 400 vision tokens
- Gundam (dynamic): Adaptive multi-tile processing for large documents
Dataset Structure
The dataset contains all original columns plus:
markdown: The extracted text in markdown format with preserved structureinference_info: JSON list tracking all OCR models applied to this dataset
Usage
from datasets import load_dataset
import json
# Load the dataset
dataset = load_dataset("{{output_dataset_id}}", split="train")
# Access the markdown text
for example in dataset:
print(example["markdown"])
break
# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
print(f"Column: {{info['column_name']}} - Model: {{info['model_id']}}")
Reproduction
This dataset was generated using the uv-scripts/ocr DeepSeek OCR vLLM script:
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \\
davanstrien/ency-test \\
<output-dataset> \\
--resolution-mode large \\
--image-column image
Performance
- Processing Speed: ~0.2 images/second
- Processing Method: Batch processing with vLLM (2-3x speedup over sequential)
Generated with 🤖 UV Scripts
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