The goal of the text classification tasks is to assign labels or classes to text.
Applications of text classification:
- Sentiment analysis.
- Entities extraction.
- ...
Text classification can be used with:
- Representation models: task-sepecific models and embedding models
- Generative models
There are two types of representation models for text classification:
- Task-specific models: they are trained for specific tasks (sentiment analysis, ...).
- Embedding models: they generate embeddings that can be used for text classification tasks.
These models are created by fine-tuning a base representation models (like BERT).