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+# More Resources
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+- [The guide to instructor-large | HKU NLP](https://zilliz.com/ai-models/instructor-large): instructor-large: an instruction-finetuned model tailored for text embeddings; better performance than `instructor-base`, but worse than `instructor-xl`.
+- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.
+- [The guide to all-MiniLM-L12-v2 | Hugging Face](https://zilliz.com/ai-models/all-MiniLM-L12-v2): all-MiniLM-L12-v2: a text embedding model ideal for semantic search and RAG and fine-tuned based on Microsoft/MiniLM-L12-H384-uncased
+- [The guide to bge-base-zh-v1.5 | BAAI](https://zilliz.com/ai-models/bge-base-zh-v1.5): bge-base-zh-v1.5: A general embedding (BGE) model introduced by BAAI and tailored for Chinese text.
+- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
+- [Image Embeddings for Enhanced Image Search - Zilliz blog](https://zilliz.com/learn/image-embeddings-for-enhanced-image-search): Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.
+- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar/success): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
+- [Training Text Embeddings with Jina AI  - Zilliz blog](https://zilliz.com/blog/training-text-embeddings-with-jina-ai): In a recent talk by Bo Wang, he discussed the creation of Jina text embeddings for modern vector search and RAG systems. He also shared methodologies for training embedding models that effectively encode extensive information, along with guidance o
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