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# Contributors
This file contains the list of everyone who contributed to the repository
<br>
<table>
<th>Contributors1</th><th>Contributors2</th> <tr>
<td><img src="xxx1">
<br>
<a href="xxx1">xxx1</a></td>
<td><img src="xxx2">
<br>
<a href="xxx2">xxx2</a></td>
</tr>
</table>
<br>
### Thanks to everyone who helped in building this Repository :)
LICENSE 0 → 100644
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ModelZooStd.md 0 → 100644
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# 仓库目录结构
* 除预训练模型外其他文件总大小尽量不要超过50M
```
Project
├── dataset
│   ├── label_1
│    ├── xxx.png
│    ├── xxx.png
│ └── ...
│   └── label_2
│    ├── xxx.png
│    ├── xxx.png
│ └── ...
├── model
│   ├── xxx.pth #预训练模型
│   ├── xxx.onnx #对应的onnx模型
│ └── xxx.mxr #对应的migraphx离线推理模型
├── doc
│   ├── icon.png
│   ├── xxx.png
│ └── xxx.png
├── README.md
├── requirement.txt
├── model.properties
├── code_file1.py
├── code_file2.py
├── code_file3.py
├── dirs
│   ├── code_file4.py
│   ├── code_file5.py
└── └── code_file6.py
```
* icon.png:模型的图标文件，上传者需放至项目根目录供前端读取，所需算法类别图标可到 飞书->云文档->云盘->icon 查找，当前没有所需算法类别图标则提交设计申请即可。
![icon](./icon.png)
* README.md:参照下图，`十二大标题`为必选项，二级标题以下的标题或内容根据自己的项目灵活增删。
![img](./doc/readme.png)
* requirement.txt：模型依赖统一写到此文件，与深度学习相关的库请注释，以免安装为nv库。
```
说明：数据基本由公司网盘储存并提供url下载或直接读取，数据信息介绍由超算互联网商城提供，内部无数据时提供官网下载地址。
```
* 需要提供迷你数据集以供使用者快速上手项目。
* model.properties：`五大属性`固定模板如下：
```
# 模型唯一标识
modelCode=Project ID
# 模型名称
modelName=模型名称(同项目名称：模型名_深度学习框架)
# 模型描述
modelDescription=简要描述此模型(尽量50字以内)
# 应用场景
appScenario=推理,训练,OCR,政府,交通,零售,金融,医疗(首先描述推理、训练信息，然后描述算法类别信息，最后描述应用行业信息，多个标签用英文逗号隔开。)
# 框架类型
frameType=paddle(说明使用的算法框架, 多个标签用英文逗号隔开。)
```
* 增加LICENSE（必要），源github无LICENSE则在LICENSE里填：None LICENSE Currently；CONTRIBUTORS.md根据源github有无提供（非必要）。
\ No newline at end of file
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# Multilingual E5
## 论文
`Multilingual E5 Text Embeddings: A Technical Report`
- https://arxiv.org/abs/2402.05672
## 模型结构
多语言E5模型基于多语言MiniLM和XLM-RoBERTa，通过对比预训练和监督微调构建，支持小、基础、大型和指令调整变体，适用于多语言信息检索和语义相似性任务。
<div align=center>
<img src="./doc/xxx.png"/>
</div>
## 算法原理
多语言E5模型采用两阶段训练：首先通过InfoNCE对比损失在约10亿多语言文本对上进行弱监督预训练，学习语义表示；随后在约160万高品质标注数据上进行监督微调，结合硬负样本挖掘和跨编码器知识蒸馏，优化嵌入空间的语义相似性和多语言检索性能。
<div align=center>
<img src="./doc/xxx.png"/>
</div>
## 环境配置
### 硬件需求
DCU型号：K100_AI,节点数量：1台,卡数：4张。
### Docker（方法一）
```bash
docker pull image.sourcefind.cn:5000/dcu/admin/base/custom:vllm0.8.5-ubuntu22.04-dtk25.04-rc7-das1.5-py3.10-20250612-fixpy-rocblas0611-rc2
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/multilingual-e5-large_pytorch
pip install transformers>=4.51.0
pip install sentence-transformers>=4.1.0
```
### Dockerfile（方法二）
此处提供dockerfile的使用方法
```
docker build --no-cache -t xxx:latest .
docker run xxx
```
### Anaconda（方法三）
此处提供本地配置、编译的详细步骤，例如：
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、paddle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 transformers>=4.51.0
pip install sentence-transformers>=2.7.0
```
## 数据集
`此处填写公开数据集名称`
- 此处填写公开数据集在公司内部的下载地址（数据集存放中心为：[SCNet AIDatasets](http://111.11.100.223:58001/ui/aihub/datasets) （非必须），模型用到的各公开数据集请分别填上具体地址。），过小权重文件可打包到项目里。
- 此处填写公开数据集官网下载地址（必须）。
此处提供数据预处理脚本的使用方法
```
python xxx.py
```
项目中已提供用于试验训练的迷你数据集，训练数据目录结构如下，用于正常训练的完整数据集请按此目录结构进行制备：
```
── dataset
│   ├── label_1
│    ├── xxx.png
│    ├── xxx.png
│ └── ...
│   └── label_2
│    ├── xxx.png
│    ├── xxx.png
│ └── ...
```
## 训练
一般情况下，ModelZoo上的项目提供单机训练的启动方法即可，单机多卡、单机单卡至少提供其一训练方法。
### 单机多卡
```
sh xxx.sh # 或python xxx.py
```
### 单机单卡
```
sh xxx.sh 或python xxx.py
```
## 推理
### vllm推理方法
```
python ./infer/infer_vllm.py --model /path/your_model_path/
```
## result
```
提示: '你好，我的名字是' | 嵌入: [0.018951416015625, -0.0121612548828125, -0.042022705078125, -0.03936767578125, 0.007015228271484375, -0.040130615234375, -0.0189361572265625, 0.04925537109375, 0.037322998046875, -0.01776123046875, 0.035614013671875, 0.01861572265625, -0.048248291015625, -0.015716552734375, -0.032745361328125, -0.01061248779296875, ...] (大小=1024)
提示: '美国总统是' | 嵌入: [0.034271240234375, 0.0015573501586914062, -0.04266357421875, -0.0291290283203125, 0.01983642578125, -0.0435791015625, 0.02117919921875, 0.0745849609375, 0.062255859375, -0.002933502197265625, 0.0333251953125, 0.037200927734375, -0.0291748046875, -0.034210205078125, -0.01837158203125, -0.02392578125, ...] (大小=1024)
提示: '法国的首都是' | 嵌入: [0.051971435546875, 0.0068359375, -0.021087646484375, -0.0528564453125, 0.0175018310546875, -0.0198211669921875, 0.0147552490234375, 0.051300048828125, 0.057861328125, -0.017242431640625, 0.0195159912109375, 0.0260162353515625, -0.0477294921875, -0.0278167724609375, -0.04351806640625, -0.0135498046875, ...] (大小=1024)
提示: '人工智能的未来是' | 嵌入: [0.016876220703125, 0.0059814453125, -0.0308074951171875, -0.05712890625, 0.01332855224609375, -0.00024700164794921875, -0.00913238525390625, 0.08123779296875, 0.049835205078125, -0.026123046875, 0.039398193359375, -0.00975799560546875, -0.0128326416015625, -0.021697998046875, -0.033447265625, -0.0147857666015625, ...] (大小=1024)
所有嵌入已保存到: ./infer/embeddings_A800.npy
```
### 精度
```
# 运行acc.py之前，请分别在DCU和GPU上运行infer_vllm.py，得到各自的embedding数据
python ./infer/acc.py --gpu_embeddings /path/embeddings_A800.npy --dcu_embeddings /path/embeddings_dcu.npy
```
结果
```
abs_diff:[[1.52587891e-05 1.52587891e-05 3.05175781e-05 ... 2.67028809e-05
1.22070312e-04 1.06811523e-04]
[3.05175781e-05 1.33514404e-05 6.10351562e-05 ... 2.28881836e-05
1.22070312e-04 3.05175781e-05]
[3.05175781e-05 3.43322754e-05 9.15527344e-05 ... 0.00000000e+00
3.05175781e-05 1.22070312e-04]
[1.52587891e-05 3.05175781e-05 0.00000000e+00 ... 5.34057617e-05
7.62939453e-05 3.81469727e-05]]
mean_abs_diff:[3.93284135e-05 4.01343568e-05 3.79525591e-05 5.03971823e-05]
```
DCU与GPU精度一致，推理框架：vllm。
## 应用场景
### 算法类别
`文本理解`
### 热点应用行业
`制造，零售，互联网`
## 预训练权重
- [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)
## 源码仓库及问题反馈
-
## 参考资料
- https://github.com/microsoft/unilm/tree/master/e5
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icon.png

2.27 KB

infer/acc.py 0 → 100644
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import numpy as np
import argparse
import os
def parse_args():
parser = argparse.ArgumentParser(description='Compare two embedding files and calculate absolute differences.')
parser.add_argument('--gpu_embeddings', type=str, required=True,
help='Path to the GPU embeddings file (.npy)')
parser.add_argument('--dcu_embeddings', type=str, required=True,
help='Path to the DCU embeddings file (.npy)')
return parser.parse_args()
def main(args):
script_dir = os.path.dirname(os.path.abspath(__file__))
embeddings_1 = np.load(args.gpu_embeddings)
embeddings_2 = np.load(args.dcu_embeddings)
if embeddings_1.shape != embeddings_2.shape:
raise ValueError("两个嵌入文件的形状不匹配！")
abs_diff = np.abs(embeddings_1 - embeddings_2)
mean_abs_diff = np.mean(abs_diff, axis=1)
print(f"abs_diff:\n{abs_diff}")
print(f"mean_abs_diff:\n{mean_abs_diff}")
if __name__ == "__main__":
args = parse_args()
main(args)
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from sentence_transformers import SentenceTransformer
model = SentenceTransformer('/home/zwq2/model/models/multilingual-e5-large/multilingual-e5-large')
input_texts = [
'query: how much protein should a female eat',
'query: 南瓜的家常做法',
"passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 i s 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or traini ng for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"passage: 1.清炒南瓜丝 原料:嫩南瓜半个 调料:葱、盐、白糖、鸡精 做法: 1、南瓜用刀薄薄的削去表面一层皮 ,用勺子刮去瓤 2、擦成细丝(没有擦菜板就用刀慢慢切成细丝) 3、锅烧热放油,入葱花煸出香味 4、入南瓜丝快速翻炒一分钟左右, 放盐、一点白糖和鸡精调味出锅 2.香葱炒南瓜 原料:南瓜1只 调料:香葱、蒜末、橄榄油、盐 做法: 1、将南瓜去皮,切成片 2、油 锅8成热后,将蒜末放入爆香 3、爆香后,将南瓜片放入,翻炒 4、在翻炒的同时,可以不时地往锅里加水,但不要太多 5、放入盐,炒匀 6、南瓜差不多软和绵了之后,就可以关火 7、撒入香葱,即可出锅"
]
embeddings = model.encode(input_texts, normalize_embeddings=True)
print(f"embeddings:{embeddings}")
\ No newline at end of file
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# SPDX-License-Identifier: Apache-2.0
from argparse import Namespace
import os
import numpy as np
from vllm import LLM, EngineArgs
from vllm.utils import FlexibleArgumentParser
def main(args: Namespace):
prompts = [
"你好，我的名字是",
"美国总统是",
"法国的首都是",
"人工智能的未来是",
]
model = LLM(**vars(args))
outputs = model.embed(prompts)
script_dir = os.path.dirname(os.path.abspath(__file__))
embeddings = [output.outputs.embedding for output in outputs]
output_path = os.path.join(script_dir, 'embeddings_A800.npy')
np.save(output_path, np.array(embeddings))
for i, (prompt, output) in enumerate(zip(prompts, outputs)):
embeds = output.outputs.embedding
embeds_trimmed = ((str(embeds[:16])[:-1] +
", ...]") if len(embeds) > 16 else embeds)
print(f"提示: {prompt!r} | "
f"嵌入: {embeds_trimmed} (大小={len(embeds)})")
print(f"所有嵌入已保存到: {output_path}")
if __name__ == "__main__":
parser = FlexibleArgumentParser()
parser = EngineArgs.add_cli_args(parser)
parser.set_defaults(model="/home/zwq/model/models/multilingual-e5-large/multilingual-e5-large",
task="embed",
enforce_eager=True)
args = parser.parse_args()
main(args)
\ No newline at end of file
model.properties 0 → 100644
View file @ e3810de3
# 模型唯一标识
modelCode = 1637
# 模型名称
modelName=multilingual-e5-large_pytorch
# 模型描述
modelDescription=multilingual-e5-large模型是开源的文本嵌入模型，基于多语言MiniLM和XLM-RoBERTa构建，专门为文本嵌入任务而设计。
# 应用场景
appScenario=推理,文本理解,制造,广媒,家居,教育
# 框架类型
frameType=pytorch
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