semvec/pypi-packages huggingface dataset:
ediwheels 0.1.3
AdHoc Ediwheel XML API port to Python
Requires Python: >=3.10

# Python EDIWHEEL - A Python package for data exchange in the tyre industry
#### DISCLAIMER: This is not an official EDIWHEEL package. It is a third-party implementation of the EDIWHEEL standard, and is in no way affiliated with any company or organization. Use at your own risk, no warranty is provided.


## Summary
This package is a Python implementation of the EDIWHEEL standard for data exchange in the tyre industry. It is based on the [EDIWHEEL standard](https://www.ediwheel.net/).

This standard uses XML requests to make stock inquiries and to place orders. The responses are also in XML format.

## Installation
The package can be installed using pip:
```
pip install ediwheel
```

## Usage
To use this package, you will need to obtain API authentication credentials from a given supplier.

A config is instantiated as such:
```python
from ediwheel import EdiConnectorConfig, EdiConnector

config = EdiConnectorConfig(
    host="https:/some_api_url.com",
    username="username",
    password="password",
    id="customer_id",
    timeout_s=10,
)

connector = EdiConnector(config)
connector.enquiry("EAN_CODE", "MANUFACTURER_CODE")
```
chucknorris 2.9.0
Chuck Norris quips
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/chucknorris.svg
   :target: https://pypi.org/project/chucknorris

.. image:: https://img.shields.io/pypi/pyversions/chucknorris.svg

.. image:: https://github.com/pmxbot/chucknorris/workflows/tests/badge.svg
   :target: https://github.com/pmxbot/chucknorris/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
   :target: https://github.com/psf/black
   :alt: Code style: Black

.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2023-informational
   :target: https://blog.jaraco.com/skeleton

A library of quips about Chuck Norris, including a plugin for pmxbot to
apply the quips to your chat colleagues.


Usage
=====

    >>> import chucknorris.quips as q
    >>> q.random('Janet')
    'Janet doesn't wear a watch. She decides what time it is.'


Plugin Setup
============

To enable the pmxbot plugin, just install this package in your
pmxbot environment. Once you've done this, the !norris command
will be available.

To override the default gender for a given nickname, just add names
to the relevant nicks in your config.yaml::

    female nicks:
      - beibeix

    male nicks::
      - jaraco

Names like 'judy' or 'brad' will be detected automatically.
keyword-spacy 0.1.2
A spaCy pipeline component for extracting keywords from text using cosine similarity.

[![GitHub Stars](https://img.shields.io/github/stars/wjbmattingly/keyword-spacy?style=social)](https://github.com/wjbmattingly/keyword-spacy)
[![PyPi Version](https://img.shields.io/pypi/v/keyword-spacy)](https://pypi.org/project/keyword-spacy/0.0.1/)
[![PyPi Downloads](https://img.shields.io/pypi/dm/keyword-spacy)](https://pypi.org/project/keyword-spacy/0.0.1/)

# 🔑 Keyword spaCy

![keyword spacy](https://github.com/wjbmattingly/keyword-spacy/blob/main/images/keyword-spacy-logo.png?raw=true)

Keyword spaCy is a spaCy pipeline component for extracting keywords from text using cosine similarity. The basis for this comes from [KeyBERT: A Minimal Method for Keyphrase Extraction using BERT](https://github.com/MaartenGr/KeyBERT), a transformer-based approach to keyword extraction. The methods employed by Keyword spaCy follow this methodology closely. It allows users to specify the range of n-grams to consider and can operate in a strict mode, which limits results to the specified n-gram range.

## Installation

Before using Keyword spaCy, make sure you have spaCy installed:

```
pip install keyword-spacy
```

Then, download the `en_core_web_md` model:

```
python -m spacy download en_core_web_md
```

## Usage

To use the Keyword Extractor, first, create a spaCy `nlp` object:

```python
import spacy
nlp = spacy.load("en_core_web_md")
```

Then, add the `KeywordExtractor` to the pipeline:

```python
nlp.add_pipe("keyword_extractor", last=True, config={"top_n": 10, "min_ngram": 3, "max_ngram": 3, "strict": True})
```

Now you can process text and extract keywords:

```python
text = "Natural language processing is a fascinating domain of artificial intelligence. It allows computers to understand and generate human language."
doc = nlp(text)
print("Top Keywords:", doc._.keywords)
```
Output:
```
Top Keywords: ['generate human language', 'Natural language processing']
```

Each token that is not a punctuation also receives a special attribute `._.keyword_value`, this is the value of a given word's similarity to the `doc.vector`. This may be helpful for other downstream tasks.

## Configuration

The `KeywordExtractor` can be configured using the following parameters:

- `top_n`: The number of top keywords to extract.
- `min_ngram`: The minimum size for n-grams.
- `max_ngram`: The maximum size for n-grams.
- `strict`: If set to `True`, only n-grams within the `min_ngram` to `max_ngram` range are considered. If `False`, individual tokens and the specified range of n-grams are considered.

## Methodology

The methodology employed by Keyword spaCy is inspired by [KeyBERT](https://github.com/MaartenGr/KeyBERT). It utilizes cosine similarity between tokens (and n-grams) and the entire document to determine the relevance of terms. The most similar terms are then considered as keywords.

## References

- [KeyBERT: A Minimal Method for Keyphrase Extraction using BERT](https://github.com/MaartenGr/KeyBERT)