Solving the Cross-Encoder Model Convergence Issue with PyTorch and Huggingface Transformers

Discover how to troubleshoot and resolve convergence problems in your `Cross-Encoder Model` using PyTorch and Huggingface Transformers.
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Troubleshooting the Cross-Encoder Model Convergence Issue with PyTorch

Creating a cross-encoder model can sometimes lead to unexpected learning behaviors. One common issue developers encounter is when the model converges to the same CLS (classification) embedding for every input. In this guide, we'll explore the problem, examine its causes, and provide a solution that helped a user overcome this challenge using PyTorch and Huggingface Transformers.

The Problem Defined

When a cross-encoder model is trained, it is supposed to derive distinct representations for different input sentences. However, in this case, the model quickly learned to collapse all input embeddings to the same value, leading to improper learning and classification. The user reported that even when using a simplified dummy dataset, which consisted of only three repeated sentence pairs, the behavior persisted and resulted in identical embeddings for all inputs.

Symptoms of the Issue

CLS embeddings converging to the same value for all inputs.

The model failing to learn correctly even on a simple dataset.

Freezing the transformer parameters correctly overfitting the data point.

Here is the model architecture the user developed:

[[See Video to Reveal this Text or Code Snippet]]

Diagnosing the Issue

After extensive debugging, the user tried several strategies without success:

Tweaking learning rates and batch sizes had no effect.

Testing different loss functions did not resolve the issue either.

No evident problems in the architecture could be identified, which left the user puzzled about what could be causing the convergence to the same embeddings.

The Solution

After exhausting several troubleshooting methods, the user decided to change the optimizer from Adam to SGD (Stochastic Gradient Descent). This switch proved to be the breakthrough solution.

Why the Optimizer Change Helped

Learning Behavior: Different optimizers have distinct ways of adjusting weights during training. Adam adapts learning rates based on both the momentum of gradients and past squared gradients. In contrast, SGD uses a more straightforward approach that can sometimes help in avoiding convergence issues, especially in cases of malfunctioning representations.

Model Learning: By using SGD, the model can better navigate the optimization landscape, resulting in improving learning dynamics and ultimately leading to correct classification.

Conclusion

The challenge of a cross-encoder model converging to the same CLS embedding can be frustrating, but it is often solvable with relatively straightforward adjustments. For many users, switching from Adam to SGD may provide significant improvements in learning behaviors and convergence issues.

If you encounter similar issues, consider experimenting with different optimizers, as they can entirely change the results of your training process. If you find a solution that works for you, don't hesitate to share your findings, as the machine-learning community grows stronger through shared knowledge and experiences.

For those seeking further assistance, we encourage asking questions in community forums or exploring documentation for more insights on transformer models with Huggingface and PyTorch.