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Text Classifier

Learn about our text classifier model type

Input: Text

Output: Concepts

Text classifier is a type of deep fine-tuned model designed to automatically categorize or classify text data into predefined categories or concepts. This is a common task in natural language processing (NLP) and has a wide range of applications, including sentiment analysis, spam detection, topic categorization, and more.


The text classifier model type also comes with various templates that give you the control to choose the specific architecture used by your neural network, as well as define a set of hyperparameters you can use to fine-tune the way your model learns.

You may choose a text classifier model type in cases where:

  • You need an automated way to process and categorize large amounts of textual data, enabling applications that require efficient and accurate text categorization.
  • You need a text classification model to learn new features not recognized by the existing Clarifai models. In that case, you may need to "deep fine-tune" your custom model and integrate it directly within your workflows.
  • You have a custom-tailored dataset, accurate labels, and the expertise and time to fine-tune models.

Example use case

A company wants to monitor customer sentiment towards its products by analyzing online reviews. They receive a large number of product reviews on their website and social media platforms. To efficiently understand customer opinions, they can employ a text classifier model to automatically classify these reviews as positive, negative, or neutral.


You can explore the step-by-step tutorial on fine-tuning the GPT-Neo LoRA template for text classification tasks here.