semvec/pypi-packages huggingface dataset:
heix-2005-part1 2022.5.4.0
黑客X档案合订本2005年（上）第一部分
Requires Python: >=3.6

# 黑客X档案合订本2005年（上）第一部分

## 下载

### Docker

```
docker pull apachecn0/heix-2005-part1
docker run -tid -p <port>:80 apachecn0/heix-2005-part1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2005-part1
heix-2005-part1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglingwu-part1
heix-erlinglingwu-part1 <port>
# 访问 http://localhost:{port} 查看文档
```
heix-2003-part6 2022.5.4.0
黑客X档案合订本2003-2004年（下）第二部分
Requires Python: >=3.6

# 黑客X档案合订本2003-2004年（下）第二部分

## 下载

### Docker

```
docker pull apachecn0/heix-2003-part6
docker run -tid -p <port>:80 apachecn0/heix-2003-part6
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2003-part6
heix-2003-part6 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglingsan-part6
heix-erlinglingsan-part6 <port>
# 访问 http://localhost:{port} 查看文档
```
heix-2003-part5 2022.5.4.0
黑客X档案合订本2003-2004年（下）第一部分
Requires Python: >=3.6

# 黑客X档案合订本2003-2004年（下）第一部分

## 下载

### Docker

```
docker pull apachecn0/heix-2003-part5
docker run -tid -p <port>:80 apachecn0/heix-2003-part5
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2003-part5
heix-2003-part5 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglingsan-part5
heix-erlinglingsan-part5 <port>
# 访问 http://localhost:{port} 查看文档
```
heix-2003-part4 2022.5.4.0
黑客X档案合订本2003-2004年（中）第二部分
Requires Python: >=3.6

# 黑客X档案合订本2003-2004年（中）第二部分

## 下载

### Docker

```
docker pull apachecn0/heix-2003-part4
docker run -tid -p <port>:80 apachecn0/heix-2003-part4
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2003-part4
heix-2003-part4 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglingsan-part4
heix-erlinglingsan-part4 <port>
# 访问 http://localhost:{port} 查看文档
```
heix-2003-part3 2022.5.4.0
黑客X档案合订本2003-2004年（中）第一部分
Requires Python: >=3.6

# 黑客X档案合订本2003-2004年（中）第一部分

## 下载

### Docker

```
docker pull apachecn0/heix-2003-part3
docker run -tid -p <port>:80 apachecn0/heix-2003-part3
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2003-part3
heix-2003-part3 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglingsan-part3
heix-erlinglingsan-part3 <port>
# 访问 http://localhost:{port} 查看文档
```
heix-2002-part2 2022.5.3.0
黑客X档案合订本2002-2003年（上）第二部分
Requires Python: >=3.6

# 黑客X档案合订本2002-2003年（上）第二部分

## 下载

### Docker

```
docker pull apachecn0/heix-2002-part2
docker run -tid -p <port>:80 apachecn0/heix-2002-part2
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2002-part2
heix-2002-part2 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglinger-part2
heix-erlinglinger-part2 <port>
# 访问 http://localhost:{port} 查看文档
```
drone-2d-custom-gym-env 1.3.3
OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone.
Keywords: reinforcement learning,gym environment,StableBaselines3,OpenAI Gym

This package contains OpenAI Gym environment designed for training RL agents to control the flight of a two-dimensional drone. The environment is automatically registered under id: drone-2d-custom-v0, so it can be easily used by RL agent training libraries, such as StableBaselines3.<br /><br />At the https://github.com/marek-robak/Drone-2d-custom-gym-env-for-reinforcement-learning.git you can find a detailed description of the environment, along with a description of the package installation and sample code made to train and evaluate agents in this environment.<br /><br />This environment was created for the needs of my bachelor's thesis, available at https://www.ap.uj.edu.pl/diplomas/151837/ site.
single-cartpole-custom-gym-env 1.2.5
OpenAI Gym environment designed for training RL agents to bring CartPole upright and its further balancing.
Keywords: reinforcement learning,gym environment,StableBaselines3,OpenAI Gym

This package contains OpenAI Gym environment designed for training RL agents to bring CartPole upright and its further balancing. The environment is automatically registered under id: single-cartpole-custom-v0, so it can be easily used by RL agent training libraries, such as StableBaselines3.<br /><br />At the https://github.com/marek-robak/Single-cartpole-custom-gym-env-for-reinforcement-learning.git you can find a detailed description of the environment, along with a description of the package installation and sample code made to train and evaluate agents in this environment.<br /><br />This environment was created for the needs of my bachelor's thesis, available at https://www.ap.uj.edu.pl/diplomas/151837/ site.
double-cartpole-custom-gym-env 1.1.3
OpenAI Gym environment designed for training RL agents to balance double CartPole.
Keywords: reinforcement learning,gym environment,StableBaselines3,OpenAI Gym

This package contains OpenAI Gym environment designed for training RL agents to balance double CartPole. The environment is automatically registered under id: double-cartpole-custom-v0, so it can be easily used by RL agent training libraries, such as StableBaselines3.<br /><br />At the https://github.com/marek-robak/Double-cartpole-custom-gym-env-for-reinforcement-learning.git you can find a detailed description of the environment, along with a description of the package installation and sample code made to train and evaluate agents in this environment.<br /><br />This environment was created for the needs of my bachelor's thesis, available at https://www.ap.uj.edu.pl/diplomas/151837/ site.
heix-2002-part1 2022.5.3.0
黑客X档案合订本2002-2003年（上）第一部分
Requires Python: >=3.6

# 黑客X档案合订本2002-2003年（上）第一部分

## 下载

### Docker

```
docker pull apachecn0/heix-2002-part1
docker run -tid -p <port>:80 apachecn0/heix-2002-part1
# 访问 http://localhost:{port} 查看文档
```

### PYPI

```
pip install heix-2002-part1
heix-2002-part1 <port>
# 访问 http://localhost:{port} 查看文档
```

### NPM

```
npm install -g heix-erlinglinger-part1
heix-erlinglinger-part1 <port>
# 访问 http://localhost:{port} 查看文档
```
receive-sms 1.0.4
Receive SMS
Keywords: python,python3,sms,receive,message,free,unlimited,scraping


# 📖 Presentation
`receive_sms` is a library which allows to receive SMS in python free and unlimited.
# ⚙️ Installation
You can download the file or use [PyPi](https://pypi.org/project/receive-sms/) :
```
pip install receive-sms
```
For getting informations, you can use : 
```
pip show receive-sms
```
# ⌨️ Use
```python3
receive_sms.get_messages(phone_number='12018577757')
```
Returns the last 100 messages sent to `phone_number` (newest to oldest). [How do I know which `phone_number` to put?](https://github.com/Emilien-B/receive-sms#the-phone-numbers-you-can-use)


Messages are returned as a `message` class, here are its attributes:
```python3
message.text
```
Returns the content of message (`str`)
```python3
message.from_number
```
Returns the phone number you originally chose (`str`)
```python3
message.to_number
```
Returns the phone number that sent the message (`str`)
```python3
message.date
```
Returns the date of the message, imprecise (`datetime`)
# 💾 Example
```python3
import receive_sms # Import the library

data = receive_sms.get_messages(phone_number = '12018577757') # Get messages sent to 12018577757

# Print informations of each message
for msg in data:
    print(msg.text) # The content of the message
    print('from '+msg.from_number) # The number from which the message originated
    print('to '+msg.to_number) # Your number, in this case it's 12018577757
    print('the '+msg.date.strftime("%m/%d/%Y, %H:%M")) # The date
    print('\n') # Line break
```
This file is [here](/test.py).
# ℹ️ To know
• Phone numbers are public, everyone can see received messages.

• Messages longer than 150 characters are split.

• The library uses [sms-online.co](https://sms-online.co/). 

### The phone numbers you can use

`+1 201-857-7757` - 🇺🇸 United States

`+1 787-337-5275` - 🇵🇷 Puerto Rico

`+60 11-1700 0917` - 🇲🇾 Malaysia

`+44 7520 635797` - 🇬🇧 United Kingdom

`+46 76 943 62 66` - 🇸🇪 Sweden
NERDA-Con 0.0
Extending NERDA Library for Continual Learning
Requires Python: >=3.6

# NERDA-Con <img src="https://raw.githubusercontent.com/SupritiVijay/NERDA-Con/main/logo.png" align="right" height=350/>

Extending NERDA Library for Continual Learning

## Installation Guide

`pip install NERDA-Con`

## Implementation and Execution

### Training

`model.train_next_task(training,validation)`

The `training` and `validation` must be NERDA-optimized dataloaders.

### Evaluation

`model.evaluate_performance(test)`

### Shared Model

To set shared model parameters, 

`model.shared_model = model.transformer_model`

## NERDA

Nerda is a framework for fine-tuning pretrained transformers for Named-Entity Recognition (NER) tasks.
```
@inproceedings{nerda,
  title = {NERDA},
  author = {Kjeldgaard, Lars and Nielsen, Lukas},
  year = {2021},
  publisher = {{GitHub}},
  url = {https://github.com/ebanalyse/NERDA}
}
```

## Cite This Work

```
@inproceedings{nerda-con,
  title = {NERDA-Con},
  author = {Supriti Vijay, Aman Priyanshu},
  year = {2022},
  publisher = {{GitHub}},
  url = {https://github.com/SupritiVijay/NERDA-Con}
}

```