semvec/pypi-packages huggingface dataset:
itns 0.0.2
A simple, deterministic, and extensible approach to inverse text normalization for numbers
License: MIT
Requires Python: >=3.7
Keywords: inverse text normalization,natural language processing,speech recognition,itn,nlp,asr

# Inverse Text Normalization

A simple, deterministic, and extensible approach to [inverse text normalization](https://www.google.com/search?q=inverse+text+normalization) (ITN) for numbers.

## Overview

A [csv](https://github.com/Brandhsu/itns/tree/master/assets/vocab.csv) file is provided to define the basic rules for transforming spoken form numbers into numerical digits, and extra post-processing may be applied for more specific formatting requirements, i.e. dates, measurements, money, etc.

---

<div align="center">
    <img src="https://raw.githubusercontent.com/Brandhsu/itns/master/assets/example.png" width=60%>
</div>

<div align="center">
    These examples were produced by running this <a href="https://github.com/Brandhsu/itns/tree/master/scripts/demo.py">script</a>.
</div>

## Installation

This package supports Python versions >= 3.7

To install from [pypi](https://pypi.org/project/itns):

```shell
$ pip install itn
```

To install locally:

```shell
$ pip install -e .
```

## Tests

To run tests, use `pytest` in the root folder of this repository:

```shell
$ ls
README.md		assets			requirements.txt	scripts			src			tests

$ pytest
```

## Issues

This package has been verified on a limited set of [test-cases](https://github.com/Brandhsu/itns/tree/master/tests/assets/). For any translation mistakes, feel free to open a pull request and update `failing.csv` with the input and expected output, thanks!

## Citation

If you find this work useful, please consider citing it.

```
@misc{hsu2022itn,
  title = {A simple, deterministic, and extensible approach to inverse text normalization for numbers},
  author = {Brandhsu},
  howpublished = {https://github.com/Brandhsu/itns},
  year = {2022},
}
```
servalfix 2.3.4.11.1
Library for AminoApps. Discord - https://discord.gg/7NmhxQBmET
License: MIT
Requires Python: >=3.6
Keywords: aminoapps,servalfix,servalfix,serval,serval-bot,narvii,amino,api,python,python3,python3.x,servalfix.py,serval-amino,capuchino

### Discord
`https://discord.gg/7NmhxQBmET`
### About lib
`Fix Amino.fix and Amino.py 1.2.17`
### How to install?
`pip install servalfix`
### What is the Improvement that this library offers?
`Minor issues, and improvement for efficient use and/or faster use.`
### API Reference
[Read the Docs Link](https://aminopy.readthedocs.io/en/latest/)
naics 1.0.1
Utility tools for the 2022 NAICS industry classification standards.
License: MIT
Keywords: naics,NAICS,industry

# naics
Utility tools for 2022 NAICS standards in Python.

## Installation
Install this package using naics.
```
pip install naics
```

## Usage
Get industry by NAICS code.
```py
from naics import industry
print(industry(11))
```

## Contributing
Contributions are HIGHLY encouraged.

## License
[MIT](https://choosealicense.com/licenses/mit/)
ducks 0.5.1
Provides Dex, a Python container for indexing objects of any type.
License: MIT
Requires Python: >=3.7,<4.0

=========
ducks  🦆
=========

Index your Python objects for fast lookup by their attributes.

.. image:: https://github.com/manimino/ducks/workflows/tests/badge.svg
    :target: https://github.com/manimino/ducks/actions
    :alt: tests Actions Status
.. image:: https://img.shields.io/static/v1?label=Coverage&message=100%&color=2ea44f
    :target: https://github.com/manimino/ducks/blob/main/test/cov.txt
    :alt: Coverage - 100%
.. image:: https://img.shields.io/static/v1?label=license&message=MIT&color=2ea44f
    :target: https://github.com/manimino/ducks/blob/main/LICENSE
    :alt: license - MIT
.. image:: https://img.shields.io/static/v1?label=python&message=3.7%2B&color=2ea44f
    :target: https://github.com/manimino/ducks/
    :alt: python - 3.7+

-------
Install
-------

.. code-block::

    pip install ducks

-----
Usage
-----

The main container in ducks is called Dex.

.. code-block::

    from ducks import Dex

    # make some objects
    objects = [
        {'x': 3, 'y': 'a'},
        {'x': 6, 'y': 'b'},
        {'x': 9, 'y': 'c'}
    ]

    # Create a Dex containing the objects.
    # Index on x and y.
    dex = Dex(objects, ['x', 'y'])

    # match objects
    dex[{
        'x': {'>': 5, '<': 10},  # where 5 < x < 10
        'y': {'in': ['a', 'b']}  # and y is 'a' or 'b'
    }]
    # result: [{'x': 6, 'y': 'b'}]

This is a Dex of dicts, but the objects can be any type.

Dex supports ==, !=, in, not in, <, <=, >, >=.

The indexes can be dict keys, object attributes, or custom functions.

------------
Is Dex fast?
------------

Yes. Here's how Dex compares to other object-finders on an example task.

.. image:: https://raw.githubusercontent.com/manimino/ducks/main/docs/img/perf_bench.png
    :width: 600

`Benchmark source <https://github.com/manimino/ducks/blob/main/examples/perf_demo.ipynb>`_

The closest thing to a Dex is an in-memory SQLite. While SQLite is a fantastic database, it requires
more overhead. As such, Dex is generally faster.

----
Docs
----

There's more to ducks than making a Dex of dicts. `Continue in the docs. <https://ducks.readthedocs.io/en/latest/quick_start.html>`_