semvec/pypi-packages huggingface dataset:
LIMEaid 1.0
LIMEaid is a tabular version of LIME
License: MIT


LIMEaid
========
LIMEaid is a project for DATA515 class project.
To get started using these components in your own software, please go to the
repository README_.
.. _README: https://github.com/pking70/LIMEaid/blob/master/README.md
License
=======
``LIMEaid`` is licensed under the terms of the MIT license. See the file
"LICENSE" for information on the history of this software, terms & conditions
for usage, and a DISCLAIMER OF ALL WARRANTIES.
All trademarks referenced herein are property of their respective holders.
Copyright (c) 2019--, Suman Bhagavathula, Javier Salido, Patrick King,
The University of Washington
eScience Institute.
mlaction 0.0.7
Statistical Learning Method package
License: MIT

# mlaction

[![Build Status](https://travis-ci.org/iOSDevLog/mlaction.svg?branch=master)](https://travis-ci.org/iOSDevLog/mlaction)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mlaction.svg)
![PyPI](https://img.shields.io/pypi/v/mlaction.svg)
![PyPI - License](https://img.shields.io/pypi/l/mlaction.svg)
![PyPI - Wheel](https://img.shields.io/pypi/wheel/mlaction.svg)
![PyPI - Downloads](https://img.shields.io/pypi/dm/mlaction.svg)
[![Commitizen friendly](https://img.shields.io/badge/commitizen-friendly-brightgreen.svg)](http://commitizen.github.io/cz-cli/)
[![Twitter](https://img.shields.io/twitter/url/https/github.com/iOSDevLog/mlaction.svg?style=social)](https://twitter.com/intent/tweet?text=Wow:&url=https%3A%2F%2Fgithub.com%2FiOSDevLog%2Fmlaction)

Machine Learning in Action 机器学习实战 Python3

GitHub: <https://github.com/iOSDevLog/mlaction>

## 方法

## 截图

## PyPI 境像加速

```python
pip3 install -i https://pypi.tuna.tsinghua.edu.cn/simple mlaction
```

## 代码规范

参考：<https://docs.python.org/3/tutorial>

### .vscode/settings.json

```json
{
  "python.pythonPath": "/Users/iosdevlog/.Envs/mlaction/bin/python",
  "python.linting.flake8Enabled": true,
  "python.formatting.provider": "yapf",
  "python.linting.flake8Args": ["--max-line-length=248"],
  "python.linting.pylintEnabled": false
}
```

- 字符串使用双引号： **""**

## 安装

```sh
pip install mlaction
```

## 开发

### 本地开发

```sh
pip3 install -e .
```

### 测试

```sh
pytest mlaction
```

### 发布

```sh
python3 setup.py sdist bdist_wheel
twine upload dist/*
```

### 生成CHANGELOG

```
npm install -g conventional-changelog-cli
./version.sh
```

### 联系方式

网站: [http://2019.iosdevlog.com/](https://2019.iosdevlog.com/)

微信公众号: AI 开发日志

![AIDevLog](https://2019.iosdevlog.com/uploads/AIDevLog.jpg)

## License

mlaction is released under the MIT license. See [LICENSE](LICENSE) for details.
bert-multitask-client 0.1.1
A service to serve bert_multitask_learning models(Client)
License: MIT
Keywords: bert nlp tensorflow machine learning sentence encoding embedding serving


# bert-multitask-as-service

A serving service for [bert-multitask-learning](https://github.com/JayYip/bert-multitask-learning)

## Install

```bash
pip install bert-multitask-server
pip install bert-multitask-client
```

## Getting Started

1. Train and export model.

    A typical trained checkpoint dir looks like below.

    ```text
    bert_serving_ckpt/
    ├── *_label_encoder.pkl
    ├── bert_config.json
    ├── export_model
    ├── params.json
    └── vocab.txt
    ```

2. Start server using CLI

    ```bash
    bert-multitask-serving-start -model_dir ~/CWS_NER_POS_ckpt/ -num_worker=4 -problem "CWS|NER|POS"
    ```

3. Use Client to Get Prediction

    ```python
    from bert_serving.client import BertClient
    bc = BertClient()
    bc.encode(['我爱北京天安门'])
    ```

# Bert多任务学习服务

一个部署[Bert多任务学习](https://github.com/JayYip/bert-multitask-learning)的服务

## 安装

```bash
pip install bert-multitask-server
pip install bert-multitask-client
```

## 开始使用

1. 训练模型并导出模型.

    导出后的模型目录应该有以下文件

    ```text
    bert_serving_ckpt/
    ├── *_label_encoder.pkl
    ├── bert_config.json
    ├── export_model
    ├── params.json
    └── vocab.txt
    ```

2. 用CLI启动服务

    ```bash
    bert-multitask-serving-start -model_dir models/ -num_worker=4 -problem "fake_problem"
    ```

3. 用客户端获取预测结果

    ```python
    from bert_serving.client import BertClient
    bc = BertClient()
    bc.encode(['我爱北京天安门'])
    ```
texttrans 1.0.10
To computes transition probability of text
License: MIT
Keywords: texttrans,text-trans

text-trans
===

It computes a transition probability of a text.


## Description

I want to determine if a word was randomly generated. 
I guess that it can be determined by text transition probabilities trained from correct words.


## Vertify

I trained transition probability using almost english words. 
I computed and compared probability for english words learned at junior high school in Japan, and randomly generated words.
From the figure below, it can see that each peak is different.

![text transition prob](examples/Figure_1.png)


## Install

```shell
$ pip install texttrans
```


## Usage


### default

Transition probability is computed for English words. I use "words_alpha.txt" of [dwyl/english-words](https://github.com/dwyl/english-words) to train default probability.

```python
from texttrans.texttrans import TextTrans
p = TextTrans().prob("pen")
print(p)
```

```
0.11640052876679541
```

### training

It prepares a text file that lists words, e.g. like below.

```txt:train.txt
hogehoge
piyopiyo
```

It train text transtion of input text.

```python
from texttrans.texttrans import TextTrans

train_path = "train.txt"
model_path = "model.pki"

tt1 = TextTrans(lang=None)
tt1.train(train_path= train_path, save_path= model_path)
print("p =", tt.prob("hoge"))
```

It computes the probability according to trained model.

```
tt2 = TextTrans(model_path=model_path)
print("p =", tt.prob("hoge"))
```

## Licence

[MIT](https://github.com/mitsuharu/text-trans/blob/master/LICENSE)


## Appendix

- [kyotolt22_20181109.pdf - Speaker Deck](https://speakerdeck.com/mitsuharu/kyotolt22-20181109)
- [ランダムな文字列を判定したい - Qiita](https://qiita.com/mitsuharu_e/items/309288b68220adddaf8b#pypi%E3%81%AB%E7%99%BB%E9%8C%B2%E3%81%97%E3%81%BE%E3%81%97%E3%81%9F%E8%BF%BD%E8%A8%98-20190605)