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turingevo
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bge-reranker-base-openvino

Text Classification
sentence-transformers
OpenVINO
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xlm-roberta
mteb
text-embeddings-inference
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bge-reranker-base-openvino
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---
license: mit
language:
- en
- zh
tags:
- mteb
- text-embeddings-inference
- openvino
- openvino-export
pipeline_tag: text-classification
library_name: sentence-transformers
base_model: BAAI/bge-reranker-base
model-index:
- name: bge-reranker-base
  results:
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv1
      type: C-MTEB/CMedQAv1-reranking
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 81.27206722525007
    - type: mrr
      value: 84.14238095238095
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv2
      type: C-MTEB/CMedQAv2-reranking
      config: default
      split: test
      revision: None
    metrics:
    - type: map
      value: 84.10369934291236
    - type: mrr
      value: 86.79376984126984
  - task:
      type: Reranking
    dataset:
      name: MTEB MMarcoReranking
      type: C-MTEB/Mmarco-reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 35.4600511272538
    - type: mrr
      value: 34.60238095238095
  - task:
      type: Reranking
    dataset:
      name: MTEB T2Reranking
      type: C-MTEB/T2Reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 67.27728847727172
    - type: mrr
      value: 77.1315192743764
---

This model was converted to OpenVINO from [`BAAI/bge-reranker-base`](https://huggingface.co/BAAI/bge-reranker-base) using [optimum-intel](https://github.com/huggingface/optimum-intel)
via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.

First make sure you have optimum-intel installed:

```bash
pip install optimum[openvino]
```

To load your model you can do as follows:

```python
from optimum.intel import OVModelForSequenceClassification

model_id = "turingevo/bge-reranker-base-openvino"
model = OVModelForSequenceClassification.from_pretrained(model_id)
```