semvec/pypi-packages huggingface dataset:
luhn-abstract 0.1.1
A library for automatically generating an abstract from a document using unsupervised techniques.
Requires Python: >=3.6

**luhn_abstract** is a Python module for automatically generating an abstract from a document using unsupervised techniques.  The Luhn paper was published in 1958, and the general concept of the algorithm is outlined in the diagram below.

Dependencies
------------------------
~~~~~~~~~~~~
luhn_abstract requires:

- Python (>=3.6)
- NumPy
- NLTK
- Pandas
~~~~~~~~~~~~

Example
------------------------

See the GitHub repository for a Jupyter notebook with examples.

```
Quantile Significance Lower = 1
Quantile Significance Upper = 31
Machine learning approaches, which include both statistical and neural networks, 
on the other hand, have many advantages over the symbolic approach:  Although 
rule-based systems for manipulating symbols were still in use in 2020, they 
have become mostly obsolete with the advance of LLMs in 2023. [22.5000] [8] In 
2003, word n-gram model, at the time the best statistical algorithm, was 
outperformed by a multi-layer perceptron (with a single hidden layer and 
context length of several words trained on up to 14 million of words with a 
CPU cluster in language modelling) by Yoshua Bengio with co-authors. [18.0000] 
The premise of symbolic NLP is well-summarized by John Searle's Chinese room 
experiment: Given a collection of rules (e.g., a Chinese phrasebook, with 
questions and matching answers), the computer emulates natural language 
understanding (or other NLP tasks) by applying those rules to the data it 
confronts. [17.4222] As an example, George Lakoff offers a methodology to 
build natural language processing (NLP) algorithms through the perspective 
of cognitive science, along with the findings of cognitive linguistics,[50] 
with two defining aspects: Ties with cognitive linguistics are part of the 
historical heritage of NLP, but they have been less frequently addressed since 
the statistical turn during the 1990s. [16.2881]
```

Citation
------------------------
Luhn, Hans Peter. "The Automatic Creation of Literature Abstracts." __IBM Journal of Research and Development 2.2__ (1958): 159-165.
quantguard 0.1.38
交易对账
Requires Python: <4.0,>=3.10

# 安装环境
基础环境安装
``` 
conda create --name quantguard python=3.10.6
conda activate quantguard

```
安装poetry包管理工具

```
pip install poetry

# 解决 poetry publish 问题
pip install urllib3==1.26.6
```

安装项目依赖
```
poetry install
```

开发过程中安装具体某个包
```
poetry add xxx包名
```

发布
```
poetry build
poetry publish
```

启动项目

本地测试
```
请在config目录下创建settings.local.yml填写自己的配置
```
启动
```
quantguard server
```

生成环境安装
```
pip install quantguard==0.1.24 -i https://pypi.Python.org/simple 
```

## 安装superset

卸载本地安装的pandas，因为安装superset容易产生冲突（测试只支持pandas 2.0.3）
```
poetry remove pandas
poetry add apache-superset
```

### 初始化db
```
设置 superset SECRET_KEY, 详见superset_config.py文件
```

### Create an admin user in your metadata database (use `admin` as username to be able to load the examples)
export FLASK_APP=superset
superset db upgrade
superset fab create-admin

### Load some data to play with
superset load_examples

### Create default roles and permissions
superset init

### To start a development web server on port 8088, use -p to bind to another port
superset run -p 8088 --with-threads --reload --debugger