harrier-oss-v1-27b

Model overview

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. 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. Have a look at config_sentence_transformers.json for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `bitext_query`. You can also use a custom instruction directly via e.g. `model.encode(queries, prompt="Instruct: Retrieve semantically similar text\nQuery: ")`.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。