# Interpreting History with AI: How Large Language Models Classify 19th-Century Swedish Patent Documents

> A research project combining the KB-BERT model and generative large language models has successfully automated the classification of 19th-century Swedish historical patents, demonstrating the potential of AI in the digitization and knowledge mining of historical documents.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-27T08:44:14.000Z
- 最近活动: 2026-05-27T08:51:11.454Z
- 热度: 145.9
- 关键词: 大语言模型, 历史文献, 专利分类, BERT, 数字人文, 瑞典语, NLP, 文本分类, KB-BERT, 预训练模型
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## Introduction: Core Achievements and Significance of AI Classification for 19th-Century Swedish Patent Documents

### Core Insights
A research project combining the KB-BERT model and generative large language models has successfully automated the classification of 19th-century Swedish historical patents, demonstrating the potential of AI in the digitization and knowledge mining of historical documents.

### Basic Information
- Original Author/Maintainer: yuntingxie
- Source Platform: GitHub
- Original Title: patent_classification
- Original Link: https://github.com/yuntingxie/patent_classification
- Publication Date: May 27, 2026
- Related Paper: "You have no class! Large Language Model Classification of Nineteenth Century Patents in Sweden, 1852-1914"

## Project Background and Research Significance

The digitization and automatic analysis of historical documents are important topics in the field of digital humanities. The Swedish Historical Patent Infrastructure Project preserves a large number of patent documents from 1852 to 1914, recording the trajectory of technological innovation during the Industrial Revolution. However, manual classification is time-consuming, labor-intensive, and requires professional knowledge. With the development of large language model technology, this project explores the use of AI to automate the classification of historical documents and verifies its effectiveness.

## Technical Solution and Implementation Methods

### Core Models
1. **KB-BERT Fine-tuning Scheme**: Based on the KB-BERT model trained by the National Library of Sweden, using patent titles as input, and performing supervised fine-tuning on the DPK classification system.
2. **Generative Large Language Model Scheme**: Guiding the generative model to output classification results through prompt engineering.

### Data Processing
- Data Source: Swedish Historical Patent Infrastructure (https://svenskahistoriskapatent.se/) patent documents from 1852 to 1914
- Classification System: DPK (Det Preliminära Klassifikationssystemet) historical patent classification standard

### Technical Details
- Environment Requirements: Python 3.10+, dependencies include pandas, numpy, torch, transformers, scikit-learn, tqdm
- Hardware Support: NVIDIA T4 GPU or CPU
- KB-BERT Acquisition: https://huggingface.co/KB/bert-base-swedish-cased

## Research Results and Academic Value

### Key Findings
The fine-tuned KB-BERT model performed excellently in the 19th-century Swedish patent classification task, effectively identifying technical categories and verifying the potential of pre-trained models in historical document processing.

### Academic Contributions
1. Methodological Innovation: Applying modern NLP technology to historical document research
2. Dataset Construction: Providing reusable technical solutions
3. Interdisciplinary Integration: Connecting computer science and history

### Data Openness
The team commits to releasing the complete dataset along with a data paper to facilitate subsequent research.

## Application Prospects and Implications

### Digitization of Historical Documents
It can be extended to large-scale historical document digitization tasks such as ancient book classification, archive organization, and topic modeling of historical newspapers.

### Digital Humanities Paradigm
AI technology greatly improves the efficiency of document organization, allowing researchers to focus on in-depth analysis and knowledge discovery.

### Low-Resource Language Processing
The success of KB-BERT provides a reference for processing medium-resource languages such as Swedish, and domain-specific fine-tuning can achieve practical results.

## Summary of Technical Highlights

1. **Domain Adaptation**: Optimizing the model for the special language style of 19th-century Swedish patents
2. **Multi-Model Comparison**: Systematically comparing the performance differences between discriminative (KB-BERT) and generative models
3. **Reproducibility**: Complete code and data release plans ensure the reproducibility of the research
4. **Cross-Language Application**: Demonstrating the effectiveness of pre-trained models in historical low-resource language processing

### Related Links
- GitHub Repository: https://github.com/yuntingxie/patent_classification
- Swedish Historical Patent Infrastructure: https://svenskahistoriskapatent.se/
- KB-BERT Model: https://huggingface.co/KB/bert-base-swedish-cased