How many sample documents are required to train your model effectively?

To effectively train a model, it is generally recommended to have at least five sample documents. This is because having a more diverse and larger set of samples allows the model to learn from different variations and patterns present in the data. Five samples provide a more balanced representation, which enhances the model’s ability to generalize beyond the specific instances it was trained on.

In a machine learning context, the adequacy of training data is crucial. While more data is typically better, starting with at least five samples allows for a basic understanding of the underlying patterns without being overwhelmed by too much data that could obfuscate key learning points. Fewer than five samples might lead to overfitting, where the model memorizes rather than learns, affecting its performance on unseen data.

Furthermore, as models become more complex and as the variability within the documents increases, the need for more training data becomes apparent. Consequently, five samples strike a balance between quantity and the opportunity to capture the essential features necessary for effective model training.