Model overview
LongEmbed: Extending Embedding Models for Long Context Retrieval. Dawei Zhu, Liang Wang, Nan Yang, Yifan Song, Wenhao Wu, Furu Wei, Sujian Li, arxiv 2024. Github Repo for LongEmbed: https://github.com/dwzhu-pku/LongEmbed. Please refer to our paper at https://arxiv.org/abs/2404.12096.pdf. Note that E5-Base-4k simply expands the position embedding matrix to allow for 4,096 position ids. The embedding vectors for the original pids {0,1,2,...,511} is mapped to represent {0,8,16,...,4088}. Embedding vectors for other pids are trained. So for inputs not exceeding 512 tokens, please multiply the position ids by 8 to maintain the original behavior, as shown in the code above.
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.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
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。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
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。