JobBERT-v2

Model overview

This is a sentence-transformers model specifically trained for job title matching and similarity. It's finetuned from sentence-transformers/all-mpnet-base-v2 on a large dataset of job titles and their associated skills/requirements. The model maps job titles and descriptions to a 1024-dimensional dense vector space and can be used for semantic job title matching, job similarity search, and related HR/recruitment tasks. - **Model Type:** Sentence Transformer - **Base model:** sentence-transformers/all-mpnet-base-v2 - **Maximum Sequence Length:** 64 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** 5.5M+ job title - skills pairs - **Primary Use Case:** Job title matching and similarity - **Performance:** Achieves 0.6457 MAP on TalentCLEF benchmark - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face

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。