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This article introduces ProtSent, a contrastive fine-tuning framework for protein language models that improves embedding quality for downstream tasks like remote homology detection and structural retrieval.
Developer seeks advice on handling English-Hindi code-mixed text classification without heavy LLMs, as sentence transformers fail on Romanized Hindi.
This article provides a technical guide on training and fine-tuning multimodal embedding and reranker models using the Sentence Transformers library, demonstrating performance improvements on Visual Document Retrieval tasks with Qwen3-VL.
Sentence Transformers v5.4 introduces support for multimodal embedding and reranking, allowing users to encode and compare text, images, audio, and video using a unified API.