How to Implement a contrastive loss function for fine-tuning a text-generation model

Question

Can you tell me How to Implement a contrastive loss function for fine-tuning a text-generation model

Answer 1

You can implement a contrastive loss function for fine-tuning a text-generation model by encouraging similar embeddings to stay close and dissimilar ones to diverge using cosine similarity.

Here is the code snippet below:

In the above code, we are using the following key points:

• Triplet-based setup: anchor, positive, and negative embeddings

• Cosine similarity via dot product and normalization

• Temperature scaling for contrastive sharpening

Hence, this approach strengthens semantic distinctions and improves representation quality in text-generation models.