image imagewidth (px) 1.11k 1.2k | markdown stringclasses 10
values | inference_info stringclasses 1
value |
|---|---|---|
<|ref|>sub_title<|/ref|><|det|>[[97, 123, 328, 147]]<|/det|>
## Application Question
<|ref|>text<|/ref|><|det|>[[99, 153, 789, 312]]<|/det|>
10- 5. Joan is a risk management professional working for the parent company of a multinational corporation. She is trying to decide whether she should buy admitted or nonadmit... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>image<|/ref|><|det|>[[90, 120, 930, 595]]<|/det|>
<|ref|>text<|/ref|><|det|>[[300, 625, 911, 697]]<|/det|>
The most frequently used categories are industry- or process- specific and are known by names that signify the first letter of each of the categories within the group. For example, the "6 Ms" group of ca... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>text<|/ref|><|det|>[[100, 105, 577, 127]]<|/det|>
1- 3. List the general categories of risk treatment options.
<|ref|>text<|/ref|><|det|>[[100, 264, 768, 305]]<|/det|>
1- 4. Describe risk treatment techniques for events that appear to have primarily positive potential outcomes.
<|ref|>sub_title<|/ref|><|de... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>text<|/ref|><|det|>[[631, 720, 797, 740]]<|/det|>
5502 AI54243
<|ref|>text<|/ref|><|det|>[[656, 750, 916, 769]]<|/det|>
ISBN 978-0-89463-991-3
<|ref|>text<|/ref|><|det|>[[830, 770, 907, 783]]<|/det|>
90000
<|ref|>text<|/ref|><|det|>[[585, 804, 650, 813]]<|/det|>
0806117500
<|ref|>text<|/ref|><|det|>[[585... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>sub_title<|/ref|><|det|>[[22, 108, 201, 130]]<|/det|>
## Review Questions
<|ref|>text<|/ref|><|det|>[[27, 139, 591, 177]]<|/det|>
6- 1. List five widely used techniques of alternative dispute resolution (ADR). (p. 7.22)
<|ref|>text<|/ref|><|det|>[[27, 308, 515, 329]]<|/det|>
6- 2. Identify the steps in the... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>sub_title<|/ref|><|det|>[[82, 110, 339, 137]]<|/det|>
## Educational Objective 2
<|ref|>text<|/ref|><|det|>[[80, 139, 723, 160]]<|/det|>
Describe and illustrate the following theories and approaches of accident causation:
<|ref|>text<|/ref|><|det|>[[80, 163, 496, 277]]<|/det|>
a. Domino theory
b. General... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>text<|/ref|><|det|>[[45, 116, 875, 155]]<|/det|>
7- 3. These are two reasons why the distinction between excess and umbrella liability coverage is often unclear:
<|ref|>text<|/ref|><|det|>[[90, 160, 870, 225]]<|/det|>
Courts and many in the insurance profession use the terms interchangeably. Many insurers pro... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>text<|/ref|><|det|>[[33, 108, 675, 147]]<|/det|>
specified types of losses. In general, consistent and effective control of noninsurance transfers requires the following:
<|ref|>text<|/ref|><|det|>[[36, 154, 635, 230]]<|/det|>
An analysis of factors affecting appropriate use of risk transfers A clearly writte... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>text<|/ref|><|det|>[[33, 100, 485, 122]]<|/det|>
5- 5. Contrast diversifiable with nondiversifiable risk.
<|ref|>text<|/ref|><|det|>[[33, 259, 339, 280]]<|/det|>
5- 6. Describe the quadrants of risk.
<|ref|>sub_title<|/ref|><|det|>[[24, 429, 267, 454]]<|/det|>
## Application Question
<|ref|>text<|/ref|>... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... | |
<|ref|>text<|/ref|><|det|>[[52, 100, 684, 209]]<|/det|>
- The claims-made coverage form covers bodily injury or property damage that occurs after the retroactive date stated in the policy. The retroactive date stated in the policy can be the same as the policy inception date or can be earlier than the policy inception ... | [{"model_id": "deepseek-ai/DeepSeek-OCR", "model_name": "DeepSeek-OCR", "column_name": "markdown", "timestamp": "2026-02-21T19:32:27.962928", "prompt_mode": "document", "batch_size": 8, "max_tokens": 8192, "gpu_memory_utilization": 0.8, "max_model_len": 8192, "script": "deepseek-ocr-vllm.py", "script_url": "https://hug... |
Document OCR using DeepSeek-OCR
This dataset contains markdown-formatted OCR results from images in andesco/risk-management-resources-images-sample using DeepSeek-OCR.
Processing Details
- Source Dataset: andesco/risk-management-resources-images-sample
- Model: deepseek-ai/DeepSeek-OCR
- Number of Samples: 10
- Processing Time: 1.5 min
- Processing Date: 2026-02-21 19:32 UTC
Configuration
- Image Column:
image - Output Column:
markdown - Dataset Split:
train - Batch Size: 8
- 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
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 \\
andesco/risk-management-resources-images-sample \\
<output-dataset> \\
--image-column image
Performance
- Processing Speed: ~0.1 images/second
- Processing Method: Batch processing with vLLM (2-3x speedup over sequential)
Generated with UV Scripts
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