Transfer Learning

Transfer learning is a powerful tool in machine learning that allows a model to leverage the knowledge from a previously trained model and apply it to a new task. In Docsumo, this capability can significantly enhance the efficiency and accuracy of your data extraction processes by building on the strengths of existing models.

What is Transfer Learning in Docsumo?

Transfer learning involves using an already trained model as the foundation for a new model. This approach is particularly useful when you have a model that performs well on a specific document type, and you want to adapt it to a similar but slightly different document type. Instead of training a new model from scratch, you can "transfer" the learning from the existing model, thereby saving time and improving performance.

Click here to learn about model training in Docsumo.

Advantages of Transfer Learning in Docsumo:

• Efficiency: Transfer learning reduces the need for large datasets and extensive training time by leveraging existing models.
• Accuracy: By building on the knowledge of a well-trained model, the new model is likely to perform better, especially with limited data.
• Flexibility: You can apply transfer learning to various document types, making it a versatile tool for different document processing tasks.

Should you have any questions or encounter any issues during the process, feel free reach out to us at [email protected], and we'll be more than happy to help you.