Tapping Into XLM RoBERTa’s Hidden Potential

Hey there! I’ve got something super exciting to share about the language understanding, especially in the area of pre-trained language models.

Researchers found that the “hidden layers” of these pre-trained models contain a treasure trove of valuable information about language. By tapping into these hidden layers, models can learn so much more about the language!

There are a couple of neat ways researchers are accessing these hidden layers. One method is called LSTM pooling. Another method is weighted pooling.

I trained these models using transfer learning, focusing on just 5 epochs to save time and resources. To get a clearer picture of how these pooling methods work, I saved the hidden layer weights for each epoch.

Architecture for Sentiment Analysis using XLM-RoBERTa and LSTM Pooling

The results are really fascinating, showing how different pooling methods like LSTM and weighted pooling can process information in unique ways. You can check out the visualizations of the hidden layer weights (I’ve included them in the GitHub repo) — it’s really cool to see how the sentiment classes are clustered differently based on the pooling method.

LSTM pooling : Comparing How Model Weights Change from the First to the Last Epoch

Weighted Pooling : Comparing How Model Weights Change from the First to the Last Epoch

If you’re curious to dive deeper, I’ve shared both the training and testing code on GitHub. Feel free to check it out and experiment with different pooling methods yourself! It’s definitely worth exploring these different techniques to see how they impact the performance of your analysis models.

Have fun, and let’s keep learning together. Thanks for reading!