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Embeddings

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M2-BERT 80M 32K Retrieval

80M checkpoint of M2-BERT, pretrained with sequence length 32768, and it has been fine-tuned for long-context retrieval.

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BGE-Base-EN v1.5

This model maps any text to a low-dimensional dense vector using FlagEmbedding.

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M2-BERT 80M 8K Retrieval

80M checkpoint of M2-BERT, pretrained with sequence length 8192, and it has been fine-tuned for long-context retrieval.

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M2-BERT 80M 2K Retrieval

80M checkpoint of M2-BERT, pretrained with sequence length 2048, and it has been fine-tuned for long-context retrieval.

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UAE-Large v1

Universal English sentence embedding model by WhereIsAI with 1024-dim embeddings and 512 context length support.

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BGE-Large-EN v1.5

BAAI v1.5 large maps text to dense vectors for retrieval, classification, clustering, semantic search, and LLM databases.

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