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Contributors.md

+# Contributors
+None
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README.md

+# BGE-Reranker
+## 论文
+暂无
+
+## 模型结构
+基于transformers结构
+<div align=center>
+    <img src="./doc/transformers.png"/>
+</div>
+
+## 算法原理
+交叉编码器会对输入的成对数据进行全注意力处理，这比embedding模型（即双编码器）更准确，但比embedding模型更耗时。因此，它可以用于对嵌入模型返回的前 k 个文档进行重新排序。
+<div align=center>
+    <img src="./doc/methods.png"/>
+</div>
+
+## 环境配置
+
+DCU型号：BW200/K100_AI 节点数量：1台,卡数：1 张。
+
+`-v 路径`、`docker_name`和`imageID`根据实际情况修改
+
+### Docker（方法一）
+```bash
+docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04.1-rc4-das1.6-py3.10-20250620-fixpy
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/bge-reranker_pytorch
+pip install FlagEmbedding
+```
+
+### Dockerfile（方法二）
+```bash
+cd docker
+docker build --no-cache -t bge-reranker:latest .
+docker run -it --shm-size 200g --network=host --name {docker_name} --privileged --device=/dev/kfd --device=/dev/dri --device=/dev/mkfd --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -u root -v /path/your_code_data/:/path/your_code_data/ -v /opt/hyhal/:/opt/hyhal/:ro {imageID} bash
+
+cd /your_code_path/bge-reranker_pytorch
+pip install FlagEmbedding
+```
+
+### Anaconda（方法三）
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
+```bash
+DTK: 25.04
+python: 3.10
+vllm: 0.8.5
+torch: 2.4.1+das.opt2.dtk2504
+deepspeed: 0.14.2+das.opt2.dtk2504
+
+```
+`Tips：以上dtk驱动、python、torch等DCU相关工具版本需要严格一一对应`
+
+其它非深度学习库安装方式如下：
+```bash
+pip install FlagEmbedding
+```
+
+## 数据集
+无
+
+## 训练
+暂无
+
+## 推理
+### vllm推理方法
+#### server
+
+server启动
+```bash
+vllm serve BAAI/bge-reranker-base --trust-remote-code --port 8002 --served-model-name bge-reranker-base
+```
+
+client调用
+```bash
+curl http://localhost:8002/score \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "bge-reranker-base",
+        "text_1": "What is the capital of France?",
+        "text_2": [
+            "The capital of Brazil is Brasilia.",
+            "The capital of France is Paris.",
+            "Horses and cows are both animals"
+        ]
+    }'
+```
+
+## result
+<div align=center>
+    <img src="./doc/results-dcu.png"/>
+</div>
+
+### 精度
+DCU与GPU精度一致，推理框架：vllm。
+
+## 应用场景
+### 算法类别
+文本分类
+
+### 热点应用行业
+制造,广媒,家居,教育
+
+## 预训练权重
+- [bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)
+- [bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/bge-reranker_pytorch
+
+## 参考资料
+- https://github.com/FlagOpen/FlagEmbedding/tree/master/research/reranker
+- https://huggingface.co/FacebookAI/xlm-roberta-base

docker/Dockerfile

+FROM image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04.1-rc4-das1.6-py3.10-20250620-fixpy
\ No newline at end of file

model.properties

+# 模型唯一标识
+modelCode=1652
+# 模型名称
+modelName=bge-reranker_pytorch
+# 模型描述
+modelDescription=北京智源人工智能研究院（BAAI）发布的用于文本排序的模型,主要用于候选检索结果的精细排序，提升检索的精度。
+# 应用场景
+appScenario=推理,文本分类,制造,广媒,家居,教育
+# 框架类型
+frameType=pytorch

test_reranker.py

+import argparse
+
+
+def use_hf(model_path):
+    import torch
+    from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+    # from transformers
+    tokenizer = AutoTokenizer.from_pretrained(model_path)
+    model = AutoModelForSequenceClassification.from_pretrained(model_path)
+    model.eval()
+
+    pairs = [['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.']]
+    with torch.no_grad():
+        inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+        scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+        print(scores)
+
+
+def use_flagE(model_path):
+    # from FlagEmbedding
+    from FlagEmbedding import FlagReranker
+    reranker = FlagReranker(model_path, use_fp16=True) #use fp16 can speed up computing
+    score = reranker.compute_score(['query', 'passage'], normalize=True)
+    print(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)
+    print(scores)
+
+
+def get_args():
+    parse = argparse.ArgumentParser('Testing reranker in FlagEmbedding or Transformers.')
+    parse.add_argument('--model_path', default='BAAI/bge-reranker-base')
+    parse.add_argument('--use_hf', action='store_true')
+    args = parse.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = get_args()
+    model_path = args.model_path
+
+    if args.use_hf:
+        use_hf(model_path)
+    else:
+        use_flagE(model_path)