semvec/pypi-packages huggingface dataset:
githerer 0.0.2
A tool used to clone organization repos using pattern-based matching

# The githerer

[![PyPI version](https://img.shields.io/pypi/v/githerer)](https://pypi.org/project/githerer/)

<img src="https://i.imgflip.com/adk2j6.jpg"/>

A command line tool for cloning all repositories in a given GitHub organization which meet
certain pattern matching formulae (at present, only if a repository name starts with
a given pattern, though `regex` incoming in a future release).

## Installation

Find this tool on `PyPI`: `python -m pip install githerer`

## Usage

Use the tool on the command line:
```
githerer --org <NAME_OF_ORG> --pattern <PATTERN_TO_MATCH>
```

As this queries the GitHub API, you'll need to have at least two environment variables set:
* `GITHUB_USER`: the username to provide to the API
* `GITHUB_TOKEN`: the token to use to access private repositories
  * Of course, for public repos in an org, no reason to provide either, really
Zuba-classify 0.0.1
A lightweight Nigerian language classifier.
Requires Python: >=3.12

# 🇳🇬 **Zuba Language Classifier**

**Zuba Language Classifier** is a lightweight Python package for classifying text into four major Nigerian languages:

* **Hausa**
* **Yoruba**
* **Igbo**
* **Broken English (Pidgin)**

The model is built using a fine-tuned **GPT-2 architecture** and loads automatically from the **HuggingFace Hub**, so you do **not** need to download or bundle any model files manually.

The package supports both single-sentence and batch predictions.

---

##  Features

* Predicts **four Nigerian languages**
*  Works with **single strings or lists**
* Auto-downloads model from **HuggingFace Hub**
* Fast inference and minimal dependencies

---

##  Installation

Install the package from **PyPI**:

```bash
pip install Zuba_classifier
```

Install **PyTorch** if you do not already have it:

```bash
pip install torch
```

---

##  Model Setup

The classifier automatically downloads the trained GPT-2 model from the **HuggingFace Hub** the first time you use it.

No manual setup is required.

You may also use a custom model if your architecture differs.

---

## 🚀 Quick Start

```python
from Zuba.Prediction import Classify

Classifier = Classify()

classifier=Classify()
result=classifier.predict([
    
        "Ina jin dadin wannan rana.",
        "Mo fẹ jẹ iyan.",
        "A na m aga ahịa echi."
    
])
print(result)
```

**Output:**

```text
["hausa", "yoruba", "igbo"]
```

---
## Note:
on the first run of this code block the function will download the trainable weight hosted on hugging face so it might take long to execute on the first run
## ⚙️ How It Works

1. Your model class is loaded by the `Classify` constructor.
2. The package downloads the trained weights from HuggingFace automatically.
3. Text is tokenized with **GPT-2 tokenizer (tiktoken)**.
4. Input is encoded, padded, and truncated to the required sequence length.
5. The model performs inference and outputs one of the four language labels.

---



### **classifier.py**

Handles tokenization, preprocessing, batching, and predictions.

### **model_loader.py**

Downloads and loads the trained model weights from HuggingFace.

---

## 🔧 Configuration

**Tokenization Length**

The default maximum token length is:

```text
277
```


## 🔗 Important Constants

```python
REPO_ID = "your-username/your-repo-name"
PACKAGE_LINK = "PYPI_link"
```

---

## 📜 License

This project is licensed under the **MIT License**.

---
Zuba-classifier 0.0.1
A lightweight Nigerian language classifier.
Requires Python: >=3.12

# 🇳🇬 **Zuba Language Classifier**

**Zuba Language Classifier** is a lightweight Python package for classifying text into four major Nigerian languages:

* **Hausa**
* **Yoruba**
* **Igbo**
* **Broken English (Pidgin)**

The model is built using a fine-tuned **GPT-2 architecture** and loads automatically from the **HuggingFace Hub**, so you do **not** need to download or bundle any model files manually.

The package supports both single-sentence and batch predictions.

---

##  Features

* Predicts **four Nigerian languages**
*  Works with **single strings or lists**
* Auto-downloads model from **HuggingFace Hub**
* Fast inference and minimal dependencies

---

##  Installation

Install the package from **PyPI**:

```bash
pip install Zuba
```

Install **PyTorch** if you do not already have it:

```bash
pip install torch
```

---

##  Model Setup

The classifier automatically downloads the trained GPT-2 model from the **HuggingFace Hub** the first time you use it.

No manual setup is required.

You may also use a custom model if your architecture differs.

---

## 🚀 Quick Start

```python
from Zuba.Prediction import Classify

Classifier = Classify()

classifier=Classify()
result=classifier.predict([
    
        "Ina jin dadin wannan rana.",
        "Mo fẹ jẹ iyan.",
        "A na m aga ahịa echi."
    
])
print(result)
```

**Output:**

```text
["hausa", "yoruba", "igbo"]
```

---
## Note:
on the first run of this code block the function will download the trainable weight hosted on hugging face so it might take long to execute on the first run
## ⚙️ How It Works

1. Your model class is loaded by the `Classify` constructor.
2. The package downloads the trained weights from HuggingFace automatically.
3. Text is tokenized with **GPT-2 tokenizer (tiktoken)**.
4. Input is encoded, padded, and truncated to the required sequence length.
5. The model performs inference and outputs one of the four language labels.

---



### **classifier.py**

Handles tokenization, preprocessing, batching, and predictions.

### **model_loader.py**

Downloads and loads the trained model weights from HuggingFace.

---

## 🔧 Configuration

**Tokenization Length**

The default maximum token length is:

```text
277
```


## 🔗 Important Constants

```python
REPO_ID = "your-username/your-repo-name"
PACKAGE_LINK = "PYPI_link"
```

---

## 📜 License

This project is licensed under the **MIT License**.

---
portableenv 2.0.4
A tool to create virtual environments using an embedded Python interpreter.
Requires Python: >=3.7

# portableenv

`portableenv` is a Python CLI tool that allows you to create virtual environments using an embedded Python interpreter. This makes it easy to manage isolated Python environments without relying on the system-wide Python installation.

## Features

- **Seamless Virtual Environment Creation**: Creates virtual environments using the embedded Python interpreter, ensuring portability and isolation from system-wide installations.
- **Simple CLI Interface**: Provides a command-line interface similar to `virtualenv` for ease of use.

## Installation

Install `portableenv` via pip:

```bash
pip install portableenv
```

## Usage

### Create a Virtual Environment

To create a virtual environment using the embedded Python interpreter, use the following command:

```bash
python -m portableenv myenv
```

This will create a virtual environment named `myenv` using the embedded Python, Python 3.10.9 by default if not specified.

### Specifying a Different Python Version

You can specify a different Python version using the `-v` or `--version` option:

```bash
python -m portableenv myenv -v 3.11.5
```

This will download the embedded Python version 3.11.5 from python.org and use it to create the virtual environment.

The tool downloads the embedded Python distribution directly from python.org and configures it automatically. It also installs and updates pip to the latest version in both the embedded Python and the created virtual environment, ensuring you have the most up-to-date package manager without seeing upgrade notices when installing packages.

## Requirements

- Python 3.7 or higher
- `virtualenv` library (automatically installed with this package)
- Internet connection for the initial download of the embedded Python interpreter

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please fork this repository and submit a pull request with your changes.

## Author

- [AbdulRahim](https://github.com/abdulrahimpds)

## Links

- [GitHub Repository](https://github.com/abdulrahimpds/portableenv)
- [PyPI Package](https://pypi.org/project/portableenv)
python-southtyrol-weather 0.0.1
Python library that provides real-time and forecast weather for southtyrol using the Open Data Hub API
Requires Python: >=3.10
Keywords: api, southtyrol, weather

# python-southtyrol-weather
Python library that provides real-time and forecast weather for southtyrol using the [Open Data Hub API](https://opendatahub.com).

## Features

- Get metadata for all available stations

- Find nearby stations

- Fetch latest measurement for each station

## Installation

Install via pip:

```bash
pip install python-southtyrol-weather
```

> Requires Python 3.10 or higher

## Dependencies

- aiohttp 3.11 or higher

## Usage Example

```python
async def main():   
    async with ClientSession() as session:
        stations = await fetchStations(session)
        print(stations)
        measurement = await fetchMeasurement(session, stations[0].id)
        print(measurement)
```

## License

MIT License

## Contributing

Contributions are welcome!\
Feel free to open issues, submit pull requests, or suggest features.