Impact of data size on Transformer training The fine-tuning process of models based on the Transformer architecture depends heavily on the number of tokens available during training. When comparing datasets with 9M and 90M tokens, the dynamics of cross-entropy loss show distinct behaviors that can lead to overfitting situations or efficient convergence on test sets
Overfitting with small datasets When training with a subset of only 9M tokens, the cross-entropy loss drops rapidly on the training set but remains high on the test set. This gap indicates that the model memorizes patterns instead of generalizing concepts. The risk of overfitting grows and hinders application in real-world scenarios
Optimized convergence with larger volume In contrast, when increasing the data volume to 90M tokens, the loss curves for training and testing converge more harmoniously. Cross-entropy decreases steadily and the model learns robust representations. This behavior favors production applications that require reliability and accuracy
Fine-tuning and evaluation recommendations To achieve a balance between training speed and result quality, it is advisable to perform periodic validations with test sets and adjust hyperparameters following regularization techniques such as dropout and early stopping. Analyzing loss metrics and adjusting batch size helps control model generalization
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