多类别文档分类应用开发：求适合的ML模型训练数据集

Hey there! Let me share some solid, accessible dataset options that align perfectly with your project’s needs—all either in .txt format or straightforward to convert to plain text:

• Reuters-21578 Dataset: This classic text classification dataset is packed with news articles labeled into categories that map directly to yours: finance, politics, technology, and more. Most distributions come as plain text files, and even if you grab a structured version (like CSV), a quick Python script can export each document to .txt in no time. It’s widely used and well-curated, making it a great addition to your BBC News data.

• AG News Corpus: This large-scale dataset focuses on news across four core categories—World, Sports, Business, Sci/Tech—that overlap heavily with your required classes. It’s available in raw plain text format, so you can start using it immediately. You’ll just need to map its existing labels to your exact classification schema (e.g., "World" to "Government & Politics", "Sci/Tech" to "Technology & Science") which is a simple manual or scripted task.

• 20 Newsgroups Dataset: While this dataset is built from discussion forum posts, it includes categories like sci.tech, talk.politics.misc, and rec.sport.baseball that fit your tech, government/politics, and sports & entertainment classes. Best of all, each document is stored as an individual .txt file—no conversion needed. You might want to combine a few related subcategories to match your label set, but that’s trivial to do with basic file handling.

• Anonymized Sensitive Content Datasets: For your "sensitive (personal & government)" category, look into the Enron Email Dataset (emails can be extracted to .txt easily) which contains anonymized corporate and personal sensitive content. Additionally, some government open data portals release redacted public documents that fall into this class—just ensure you’re using fully anonymized data to comply with privacy regulations like GDPR.

• Ethically Scraped Custom Data: If you need more tailored data, consider scraping public, non-copyrighted sources: government press releases, reputable sports news outlets, tech blogs, or financial news sites. Always follow each site’s robots.txt rules and ethical guidelines. Use tools like BeautifulSoup in Python to extract text content and save it directly as .txt files, then label them manually or use semi-supervised learning to speed up the process.

Quick Conversion Tip #

If you end up with a dataset in a structured format (like CSV), here’s a simple Python snippet to convert each document to a .txt file:

import pandas as pd

# Load the structured dataset
df = pd.read_csv('your_dataset.csv')

# Write each document to a separate .txt file
for doc_id, row in df.iterrows():
    with open(f'document_{doc_id}.txt', 'w', encoding='utf-8') as txt_file:
        txt_file.write(row['text_column'])

Hope these options help you build out a robust training dataset for your PDF/DOCX classification app—happy coding!

内容的提问来源于stack exchange，提问作者Nuraj Chaminda