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Comparison of Intfloat: E5-Base-v2, all-MiniLM-L6-v2
2 modelsintfloatIntfloat: E5-Base-v2Remove ×sentence-transformersall-MiniLM-L6-v2Remove ×
What’s differentThe e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as…We present a sentence transformation model that achieves state-of-the-art results on various NLP tasks without requiring task-specific architectures or fine-tuning. Our approach leverages contrastive…
DateNov 22, 2023ListedMar 22, 2023Listed
Listed rate$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →1 endpoint →
Context window512512
Max output460460
Parameters——
Open weightsSupportedSupported

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