semvec/pypi-packages huggingface dataset:
sencore 0.1.56
sentence nlp parser for multilingua
Requires Python: <4.0,>=3.12
Keywords: NLP, sentence parsing, spacy

# Installation 

### Install from pip3
``` 
pip3 install --verbose sencore
```

### Install spacy lib
```
python -m spacy download en_core_web_trf
python -m spacy download es_dep_news_trf
```

# Usage

Please refer to [api docs](https://qishe-nlp.github.io/sencore/).

### Executable usage
* Parse sentence into vocabs

  ```
  parse2vocab --lang en --sentence "It is a great day."
  ```

* Parse sentence into phrases

  ```
  parse2phrase --lang en --sentence "It is a great day."
  ```

### Package usage
* Parse sentence into vocabs

  ```
  from sencore import VocabParser 

  def vocab(lang, sentence):
    sentences = {
        "en": "Apple is looking at buying U.K. startup for $1 billion.",
        "es": "En 1941, fue llamado a filas para incorporarse a la Armada.",
        "de": "Für Joachim Löw ist ein Nationalmannschafts-Comeback von Thomas Müller und Mats Hummels nicht mehr kategorisch ausgeschlossen.",
        "fr": "Nos jolis canards vont-ils détrôner les poules, coqueluches des jardiniers ?",
    }

    sen = sentence or sentences[lang]
    print(sen)
    vp = VocabParser(lang)
    vocabs = vp.digest(sen)
    print(vocabs)

  ```

* Parse sentence into phrases

  ```
  from sencore import PhraseParser

  def phrase(lang, sentence):
    sentences = {
        "en": "Apple is looking at buying U.K. startup for $1 billion.",
        "es": "En 1941, fue llamado a filas para incorporarse a la Armada.",
        "de": "Für Joachim Löw ist ein Nationalmannschafts-Comeback von Thomas Müller und Mats Hummels nicht mehr kategorisch ausgeschlossen.",
        "fr": "Nos jolis canards vont-ils détrôner les poules, coqueluches des jardiniers ?",
    }

    sen = sentence or sentences[lang]
    print(sen)
    pp = PhraseParser(lang)
    phrases = pp.digest(sen)
    print(phrases)
  ```

# Development

### Clone project
```
git clone https://github.com/qishe-nlp/sencore.git
```

### Install [poetry](https://python-poetry.org/docs/)

### Install dependencies
```
poetry update
python -m spacy download en_core_web_trf
python -m spacy download es_dep_news_trf
```

### Test
```
poetry run pytest -rP
```
which run tests under `tests/*`


### Execute
```
poetry run parse_to_vocab --help
```

### Create sphinx docs
```
poetry shell
cd apidocs
sphinx-apidoc -f -o source ../sencore
make html
python -m http.server -d build/html
```

### Host docs on github pages
```
cp -rf apidocs/build/html/* docs/
```

### Build
* Change `version` in `pyproject.toml` and `sencore/__init__.py`
* Build python package by `poetry build`

### Git commit and push

### Publish from local dev env
* Set pypi test environment variables in poetry, refer to [poetry doc](https://python-poetry.org/docs/repositories/)
* Publish to pypi test by `poetry publish -r test`

### Publish through CI 

* Github action build and publish package to [test pypi repo](https://test.pypi.org/)

```
git tag [x.x.x]
git push origin master
```

* Manually publish to [pypi repo](https://pypi.org/) through [github action](https://github.com/qishe-nlp/sencore/actions/workflows/pypi.yml)
konda 0.1.0
A wrapper for conda commands with automatic Miniconda installation
Requires Python: >=3.6

# 🐍 Konda - Conda Installer/Wrapper for Google Colab 🚀

[![PyPI version](https://badge.fury.io/py/konda.svg)](https://badge.fury.io/py/konda)
[![Python Versions](https://img.shields.io/pypi/pyversions/konda.svg)](https://pypi.org/project/konda/)
[![CI](https://github.com/tamnguyenvan/konda/actions/workflows/publish.yml/badge.svg)](https://github.com/tamnguyenvan/konda/actions)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 🔍 Why Konda?

Installing conda/miniconda and creating virtual environments on Google Colab is a common need, but it can be quite a hassle. Konda solves this problem by helping you do everything with a single command. Essentially, Konda makes conda installation and usage in Colab straightforward and painless.

There's no learning curve since Konda commands are simply wrappers around conda commands you already know.

## ✨ Features

- 🔄 **One-command Miniconda Installation**: Installs Miniconda with just one line of code
- 🌐 **Built for Google Colab**: Optimized for the Colab environment where conda integration is typically tricky 
- 🛠️ **Simple Command Wrapper**: Run conda commands without worrying about environment setup
- 🚀 **Seamless Environment Activation**: Automatically handles the special activation requirements in Colab
- 🧹 **Easy Cleanup**: Remove the Miniconda installation when you're done

## 📦 Installation

```bash
pip install konda
```

## 🚀 Quick Start

### 1️⃣ Install Miniconda

```python
import konda
konda.install()
```

### 2️⃣ Use conda commands with konda
Konda is a simple wrapper around conda.
```bash
# Create a new environment
konda create -n my_env python=3.8 -y

# Activate the environment
konda activate my_env

# Run a command in the environment
konda run "conda install anaconda::tensorflow-gpu=2.4.1 -y"
konda run "pip install requests"

# Deactivate the environment
konda deactivate
```

### Example
To see how konda works in action, check out the [example](https://github.com/tamnguyenvan/konda/blob/main/assets/example.ipynb).

## 🗑️ Uninstallation

To remove the Miniconda installation created by konda:

```bash
konda uninstall
```

To uninstall the konda package itself:

```bash
pip uninstall konda
```

## 🔍 How It Works

Konda simplifies conda management in Google Colab by:

1. 📥 Downloading and installing Miniconda in the Colab environment
2. 🔧 Setting up the necessary environment paths and variables
3. 🔄 Handling the `source /usr/local/etc/profile.d/conda.sh` requirement under the hood
4. 🚀 Providing a consistent interface for all conda commands

## 🤝 Contributing

Contributions are welcome! Here's how you can help:

1. 🍴 Fork the repository
2. 🔧 Create a feature branch
3. 💻 Commit your changes
4. 📤 Push to the branch
5. 🔄 Open a Pull Request

## 📝 License

This project is licensed under the MIT License.

---

<p align="center">Made with ❤️ to simplify your Colab experience</p>
palaestrai-arsenai 3.5.4.1
Adversarial Resilience Learning Design of Experiments.
Requires Python: >=3.10

# Adversarial Resilience Learning --- Design of Experiments


## Introduction

Reproducibility of scientific experiments is more important than ever.
The Adversarial Resilience Learning -- Design of Experiments Suite *arsenAI* serves exactly this purpose.
It also enables to test different combinations of agents and environments in *palaestrAI*.

## Installation

ArsenAI is written in Python and available on [pypi.org](https://pypi.org/project/palaestrai-arsenai/).
A virtual environment is recommended. 
To install arsenAI, type:

```bash
pip install palaestrai-arsenai
```

To execute experiment runs, you also need palaestrAI.

## Usage

In the sources of arsenAI, there is an `example_experiment.yml` file. 
You find it in the folder tests/fixtures/. 
Download this file and save it to your current working directory.
To use arsenAI, simply type

```bash
arsenai generate ./example_experiment.yml
```

An output folder will be created (default: (current working directory)/_outputs)).
After the arsenAI command has finished, you will find palaestrAI run files and that directory, which can be executed with 
```
$ palaestrai start _outputs/Dummy\ Experiment_run-0.yml
```

You can copy the example file and modify it to your needs.

## Documentation

In the future, you will find a more comprehensive documentation on [docs.palaestr.ai](http://docs.palaestr.ai/experiments.html) 

## Copyright & Authors

All source code, except where otherwise mentioned, is Copyright (C) 2020, 2021, 2022 OFFIS e.V.
Contributing authors are listed in order of their appearance in the file `CONTRIBUTING.md`.