bi-encoder_msmarco_bert-base_german

Model overview

This model can be used for **semantic search** and **documents retrieval** to find relevant passages based on a query. It was trained on a machine translated **MSMARCO dataset** for _german_ with **hard negatives** and **Margin MSE loss**. Combining these elements results in a SOTA transformer for asymmetric search. Details are presented below. The model is based on training with samples from **MSMARCO Passage Ranking** dataset. It contains about 500.000 questions and 8.8 million passages. The training objective is to identify the relevant passages or answers for an input question. In terms of content, the texts deal with diverse domains. Questions are available as sentences but also keyword-based variants can be found. Consequently, models trained on MSMARCO can be used in a variety of domains. The dataset was originally published in English, but has been translated into other languages by researchers with the help of machine translation. To be more specific, **"mMARCO: A Multilingual Version of the MS MARCO Passage Ranking Dataset"** is used, which contains 13 Google based translations, German is one of them.

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。