To recognize if a Sentence Transformer model is underfitting or overfitting during fine-tuning, monitor its performance on training and validation data. Underfitting occurs when the model performs poorly on both training and validation sets. For example, if the training loss remains high and doesn’t decrease over epochs, and the validation loss mirrors this trend, the model isn’t learning meaningful patterns. This often happens with overly simple architectures, insufficient training data, or hyperparameters like a learning rate that’s too low. For instance, using a small pre-trained model (e.g., all-MiniLM-L6-v2) on a complex task with limited data may result in high error rates across the board.
Overfitting is characterized by a large gap between training and validation performance. If the training loss drops significantly while validation loss plateaus or increases, the model is memorizing training data instead of generalizing. For example, a model achieving near-zero training loss but a validation Spearman correlation that stagnates or declines (e.g., 0.8 training vs. 0.5 validation) indicates overfitting. This is common when the model is too complex relative to the dataset size, such as fine-tuning a large model like all-mpnet-base-v2 on a small custom dataset of 1,000 examples.
To address underfitting, increase model capacity by using a larger pre-trained model or adding layers. Augment training data with techniques like synonym replacement or back-translation. Adjust hyperparameters: raise the learning rate (e.g., from 2e-5 to 5e-5) or train longer if the loss is still decreasing. For overfitting, apply regularization like dropout (e.g., setting "dropout": 0.2 in the model config) or weight decay (e.g., 0.01). Use early stopping to halt training when validation loss stops improving (e.g., patience=3 epochs). Reduce model complexity by switching to a smaller architecture or pruning layers. Data augmentation and ensuring training/validation data distributions align also help. For example, if overfitting occurs on a domain-specific task, adding unlabeled in-domain data via techniques like masked language modeling can improve generalization.
By systematically adjusting model architecture, data, and training parameters based on these patterns, you can balance the model’s ability to learn without memorizing.