FastText文本分类器及通用文本分类器的标签数量上限咨询

Hey there! Let's break down your questions about text classifier category limits clearly:

FastText itself doesn't have a hard, fixed upper limit on the number of categories you can use for text classification. The real constraints here come down to your hardware resources (like available RAM and computing power):

• Each category corresponds to a neuron in the model's output layer. The more categories you have, the larger the output layer's weight matrix becomes, which eats up more memory during both training and inference.
• In practice, many real-world use cases (like large-scale news categorization or product catalog classification) use FastText with hundreds or even thousands of categories—so long as the machine has enough memory to handle the model size, it works perfectly fine.

For custom-built general text classifiers (like those based on BERT, CNNs, or other transformer architectures), there's also no theoretical upper limit. Just like FastText, the only constraints are your hardware and the practicality of managing large numbers of categories (like collecting labeled data for each category).

However, when it comes to commercial text analysis APIs, things are different. After checking multiple services, most of them cap the number of supported categories at 20 or fewer. This is mostly due to:

• Performance optimization: More categories can increase inference latency, which APIs need to keep low for real-time use.
• User experience: Managing dozens or hundreds of categories would make setup, data labeling, and model tuning far more complex for end users.
• Cost control: Hosting and maintaining models with huge numbers of categories increases operational costs for API providers, so they set reasonable limits to balance service quality and affordability.

内容的提问来源于stack exchange，提问作者saran_atv