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🎉Today, the 5000th Sentence Transformer model was uploaded to Hugging Face! Embedding models are extremely versatile, so it's no wonder that they're still being trained.
🎉Today, the 5000th Sentence Transformer model was uploaded to Hugging Face! Embedding models are extremely versatile, so it's no wonder that they're still being trained.
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Here's a few resources to get you started with them:
Here's a few resources to get you started with them:
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- All Sentence Transformer models: 
- All Sentence Transformer models: 
link
https://huggingface.co/models?library=sentence-transformers&sort=trending
https://huggingface.co/models?library=sentence-transformers&sort=trending
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- Sentence Transformer documentation: 
- Sentence Transformer documentation: 
link
https://sbert.net/
https://sbert.net/
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- Massive Text Embedding Benchmark (MTEB) Leaderboard: 
- Massive Text Embedding Benchmark (MTEB) Leaderboard: 
resource
https://huggingface.co/spaces/mteb/leaderboard
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mteb/leaderboard
https://huggingface.co/spaces/mteb/leaderboard
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The embedding space is extremely active right now, so if you're using an embedding model for your retrieval, semantic similarity, reranking, classification, clustering, etc., then be sure to keep an eye out on the trending Sentence Transformer models & new models on MTEB.
The embedding space is extremely active right now, so if you're using an embedding model for your retrieval, semantic similarity, reranking, classification, clustering, etc., then be sure to keep an eye out on the trending Sentence Transformer models & new models on MTEB.
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Also, I'm curious if you've ever used Sentence Transformers via a third party library, like a RAG framework or vector database. I'm quite interested in more integrations to bring everyone free, efficient & powerful embedding models!
Also, I'm curious if you've ever used Sentence Transformers via a third party library, like a RAG framework or vector database. I'm quite interested in more integrations to bring everyone free, efficient & powerful embedding models!
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🎉Today, the 5000th Sentence Transformer model was uploaded to Hugging Face! Embedding models are extremely versatile, so it's no wonder that they're still being trained.

Here's a few resources to get you started with them:
- All Sentence Transformer models: https://huggingface.co/models?library=sentence-transformers&sort=trending
- Sentence Transformer documentation: https://sbert.net/
- Massive Text Embedding Benchmark (MTEB) Leaderboard: https://huggingface.co/spaces/mteb/leaderboard

The embedding space is extremely active right now, so if you're using an embedding model for your retrieval, semantic similarity, reranking, classification, clustering, etc., then be sure to keep an eye out on the trending Sentence Transformer models & new models on MTEB.

Also, I'm curious if you've ever used Sentence Transformers via a third party library, like a RAG framework or vector database. I'm quite interested in more integrations to bring everyone free, efficient & powerful embedding models!

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