---
title: GeoReranker
canonical_url: "https://www.modelscope.cn/models/GeoGPT/GeoReranker"
md_url: "https://www.modelscope.cn/models/GeoGPT/GeoReranker.md"
repository: GeoGPT/GeoReranker
last_updated: 2025-06-05
model_type:
  - xlm-roberta
architectures:
  - XLMRobertaForSequenceClassification
parameters: 567.8M
tensor_type:
  - F32
library_name:
  - safetensors
inference_backends:
  - "deploy_task emb"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 78
stars: 1
---

# GeoReranker

> GeoReranker - GeoGPT 在 ModelScope 开源的模型。Model Card for GeoReranker

GeoGPT/GeoReranker 是 ModelScope 魔搭社区上的 567.8M 参数机器学习模型，可用 deploy_task emb、sglang 0.5.2、vllm 0.9.2 部署。

- **Repository**: GeoGPT/GeoReranker
- **Parameters**: 567.8M
- **Inference backends**: deploy_task emb, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 78
- **Stars**: 1
- **Last updated**: 2025-06-05

Source: https://www.modelscope.cn/models/GeoGPT/GeoReranker

---

# Model Card for GeoReranker

The reranker is a critical component in Retrieval-Augmented Generation (RAG) systems, designed to refine the initial retrieval results by reordering candidate documents based on their semantic relevance to the query. Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. You can get a relevance score by inputting query and passage to the reranker. And the score can be mapped to a float value in [0,1] by sigmoid function.

## Quick Start

To load the GeoReranker model with HuggingFace, use the following snippet:
```python
from FlagEmbedding import FlagReranker

model_name_or_path = 'GeoGPT/GeoReranker'
reranker = FlagReranker(model_name_or_path, use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation

score = reranker.compute_score(['query', 'passage'])
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
score = reranker.compute_score(['query', 'passage'], normalize=True)
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)
```


## License and Uses
GeoReranker is licensed under the MIT License Agreement. The primary use of GeoGPT models is to support geoscience research, providing geoscientists with innovative tools and capabilities enhanced by large language models. It is specifically designed for non-commercial research and educational purposes.

The model is not intended for use in any manner that violates applicable laws or regulations, nor for any activities prohibited by the license agreement. Additionally, it should not be used in languages other than those explicitly supported, as outlined in this model card.

## Limitations
GeoReranker is trained on English datasets, and performance may be suboptimal for other languages.