harrier oss v1 harrier oss v1 is a family of multilingual text embedding models developed by Microsoft. The models use decoder only architectures with last token pooling and L2 normalization to produce dense text embeddings. They can be applied to a wide range of tasks, including but not limited to retrieval , clustering , semantic similarity , classification , bitext mining , and reranking . The models achieve state of the art results on the Multilingual MTEB v2 benchmark as of the release date. Model Parameters Embedding Dimension Max Tokens MTEB v2 Score harrier oss v1 270m 270M 640 32,768 66.5 harrier oss v1 0.6b 0.6B 1,024 32,768 69.0 harrier oss v1 27b 27B 5,376 32,768 74.3 Training All models are trained with contrastive learning objectives on a large scale mixture of multilingual datasets covering diverse tasks. The 270m and 0.6b variants are additionally trained with knowledge distillation from larger embedding models. Usage Below is an example to encode queries and passages from the MS MARCO passage ranking dataset. Sentence Transformers Have a look at config sentence transformers.json for the prompts that are pre configured, such as web search query , sts query , and bit…
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