ollama rerank 模型
时间: 2025-03-06 15:40:51 浏览: 202
### Ollama Rerank 模型概述
Ollama Rerank 是一种用于重新排列文档或段落顺序的模型,旨在提高检索系统的精度和效率。该模型通过学习查询与文档之间的关系来优化排序结果[^1]。
### 安装与配置
为了安装并使用 `ollama-rerank` 模型,可以从 Hugging Face 平台获取预训练权重:
```bash
pip install transformers sentence-transformers torch
```
接着可以加载特定版本的模型:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "BAAI/bge-reranker-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
```
### 使用实例
下面是一个简单的例子展示如何利用此模型对候选列表进行重排:
```python
import numpy as np
def rerank(query, candidates):
inputs = tokenizer([query]*len(candidates), candidates, return_tensors='pt', padding=True, truncation=True)
outputs = model(**inputs).logits.detach().numpy()
scores = {i: score for i, (score,) in enumerate(outputs)}
sorted_scores = dict(sorted(scores.items(), key=lambda item: item[1], reverse=True))
ranked_candidates = [candidates[i] for i in sorted_scores.keys()]
return ranked_candidates
# 测试数据
test_query = "What is the capital of France?"
candidate_answers = [
"The Eiffel Tower stands tall in Paris.",
"Paris has been called 'The City of Light'.",
"Berlin serves as Germany's bustling metropolis."
]
ranked_results = rerank(test_query, candidate_answers)
for idx, answer in enumerate(ranked_results, start=1):
print(f"{idx}. {answer}")
```
上述代码展示了如何基于给定查询字符串对多个备选项按照相关度高低排序的过程。
### 注意事项
当处理大规模语料库时,建议采用批量化操作以提升性能;同时注意调整 GPU/CPU 资源分配策略以便更好地支持实际应用场景中的需求。
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