semvec/pypi-packages huggingface dataset:
m-rjgf-ai-python 0.1.1
bytebroad AI framework python extension.

## 润建视觉 python sdk库

### 介绍
目前主要功能：
1. 证书的生成，列表，最大授权数
2. 物体大目标检测

### 编译，发布

**构建**
```angular2html
python setup.py sdist bdist_wheel
```

**发布pypi**

注意：上传 前提安装 twine库
```angular2html
twine upload dist/*
```

### 使用
robi-circle 0.0.6
A small Robi Airtel Circle Project
Requires Python: >=3.10

<p align="center">
  <img src="https://img.shields.io/badge/Version-0.0.6-orange?style=for-the-badge">
</p>

<p align="center">
  <img src="https://img.shields.io/badge/Author-ARU-green?style=flat-square">
  <img src="https://img.shields.io/badge/Open%20Source-Yes-green?style=flat-square">
  <img src="https://img.shields.io/badge/Written%20In-Python-green?style=flat-square">
</p>

<br>
<br>
<h3><p align="center">Disclaimer</p></h3>
<p><i>This script is create only for enhancing <b>Robi and Airtel Circle</b> experience and personally for keeping everything in practice with a realtime project.
</i></p>
<br>
<br>





## Install
```
pip install robi-circle
```


`` 📡 Get in Touch `` 
<br>

<a href="https://www.facebook.com/Aru.Ofc" target="_blank"><img src="https://img.shields.io/badge/FACEBOOK-4267B2.svg?&style=flat-square&logo=facebook&logoColor=white" alt="LinkedIn"></a>
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<a href="mailto: aru.ofc@yahoo.com" target="_blank"><img src="https://img.shields.io/badge/Email-BB001B.svg?&style=flat-square&logo=gmail&logoColor=white" alt="Gmail"></a>
<a href="https://github.com/Aru-Ofc-git" target="_blank"><img src="https://img.shields.io/badge/GitHub-171515.svg?&style=flat-square&logo=github&logoColor=white" alt="Github"></a>



<p align="center">Made With ❤️ by <a href="https://www.facebook.com/Siillent.Killer.Arman">ARU</a> </p>
fastbm25 0.0.2
Fast text match algorithm implementation for bm25
Requires Python: >=3.5

# Installation
The easiest way to install this package is through pip, using

```
pip install fastbm25
```
# fastbm25
The fast bm25 algorithm for text match optimized by reverted index. So the complexity will be no more than O(N(log N)).

（利用倒排索引加速的bm25文本匹配算法，从一堆数据中寻找最相似的文本）

# usage
## find top k similar sentences from corpus; Note you should tokenize text and use stop words in advance
```
from fastbm25 import fastbm25

corpus = [
    "How are you !",
    "Hello Jack! Nice to meet you!",
    "I am from China, I like math."
]
tokenized_corpus = [doc.lower().split(" ") for doc in corpus]
model = fastbm25(tokenized_corpus)
query = "where are you from".lower().split()
result = model.top_k_sentence(query,k=1)
print(result)
```
The result is list of tuple like
> [('I am from China, I like math.', 2, -0.06000000000000001)]

For some language like Chinese that doesn't need tokenization. you can use this example
```
from fastbm25 import fastbm25

corpus = [
    "张三考上了清华",
    "李四考上了北大",
    "我烤上了地瓜.",
    "我们都有光明的未来."
]
model = fastbm25(corpus)
query = "我考上了大学"
result = model.top_k_sentence(query,k=1)
print(result)
```
> [('李四考上了北大', 1, 1.21)]
## find document pair similarity  between document a and document b
Note that a and b don't need to be included in the reference corpus;

```
from fastbm25 import fastbm25
corpus = [
    "How are you !",
    "Hello Jack! Nice to meet you!",
    "I am from China, I like math."
]
tokenized_corpus = [doc.lower().split(" ") for doc in corpus]
model = fastbm25(tokenized_corpus)
document_a = "where are you from".lower().split()
document_b = "where are you".lower().split()

result = model.similarity_bm25(document_a,document_b)
print(result)
```
> 1.944187075527278