How to approach training on your own domain? What to do with the general model?

Training a model on your own domain begins with analyzing the specifics of the data and the tasks to be solved. A general model is a good starting point; it can be used as a base for fine-tuning or for feature extraction.

Approaches:

• Data collection and preparation: gather relevant data from your domain, perform cleaning and annotation.
• Using transfer learning: take a general model and fine-tune it on your data to better reflect domain specifics.
• Error analysis: understand where the general model makes mistakes and focus on these cases during training.
• Integration with business logic: consider the specifics of the model's application to improve quality and interpretability.

For example, if there is a general model for text processing and the task is to analyze reviews of medical services, you should gather a corpus of reviews, fine-tune the model on them, and check how it handles terminology and context.

Thus, the general model serves as a foundation, and training on your own domain is an adaptation and improvement for specific tasks.