The difference between fine-tuning a transformer model for multi-class classification and using it with a classification header, versus fine-tuning and then extracting last hidden layer embeddings for clustering, lies in the objectives and methods of these approaches.
Fine-tuning with a classification header: In this approach, you train the transformer model with a classification head on your labeled data, where the model learns to directly predict the classes you have labeled. The final layer(s) of the model are adjusted during fine-tuning to adapt to your specific classification task. Once the model is trained, you can use it to classify new data into the known classes based on the learned representations.
Fine-tuning and extracting embeddings for clustering: Here, you also fine-tune the transformer model on your labeled data as in the previous approach. However, instead of using the model for direct classification, you extract the last hidden layer embeddings of the fine-tuned model for each input. These embeddings capture the learned representations of the data. Then, you apply a clustering algorithm (such as k-means or hierarchical clustering) on these embeddings to group similar instances together into clusters. This approach allows for discovering potential new categories or patterns in the data.
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