Skip to main content

Text Classification Models

Knowledgator provides a collection of powerful models for text classification tasks, supporting both zero-shot and few-shot learning scenarios. These models are designed to handle a variety of use cases such as sentiment analysis, topic detection, intent classification, and more.

Available Models​

🔹 ComprehendIt​

A DeBERTaV3-based model fine-tuned on NLI and classification datasets. Suitable for general-purpose zero-shot classification across multiple domains.

🔹 GLiClass​

A lightweight model optimized for high-speed inference in low-resource environments, suitable for real-time applications and edge devices.

Key Features​

  • Support for multi-label and multi-class classification.
  • Optimized for zero-shot inference using NLI-style input formulation.
  • Fine-tuning capabilities with the LiqFit framework.
  • Usable with Hugging Face's pipeline API or custom training setups.

Use Cases​

  • Sentiment Analysis
  • Intent Recognition
  • Topic Classification
  • Content Moderation
  • Customer Feedback Analysis

For detailed documentation and usage examples, visit Knowledgator Docs.