---
license: mit
language:
- zh
- en

tags:
- mteb
model-index:
- name: bge-reranker-large-1k
  results:
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv1-reranking
      name: MTEB CMedQAv1
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 82.15039301310942
    - type: mrr
      value: 84.92349206349207
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/CMedQAv2-reranking
      name: MTEB CMedQAv2
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 84.18695515652256
    - type: mrr
      value: 86.96876984126984
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/Mmarco-reranking
      name: MTEB MMarcoReranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 37.641770303833105
    - type: mrr
      value: 36.576587301587304
  - task:
      type: Reranking
    dataset:
      type: C-MTEB/T2Reranking
      name: MTEB T2Reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 67.47867763060952
    - type: mrr
      value: 77.609724774586
---

本模型是在 [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) 上对模型的 `model.base_model.embeddings.position_embeddings.weight` 依赖经验值进行修改，来扩大模型输入长度，并没有进行任何继续supervised fined tuning，作为新手示例。
同时亦附上在`C-MTEB`的分数作为对比。
感谢原作者的工作。