semvec/pypi-packages huggingface dataset:
spacy-trankit 0.1.0
spacy wrapper for Trankit, a Transformer-based multilingual neural dependency parser with tokenization and NER
Requires Python: >=3.8

<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>

# spaCy + Trankit

This package wraps the [Trankit](https://github.com/nlp-uoregon/trankit) library, so you can use trankit models in a
[spaCy](https://spacy.io) pipeline. 


[//]: # ([![tests]&#40;https://github.com/imvladikon/spacy-trankit/actions/workflows/tests.yml/badge.svg&#41;]&#40;https://github.com/imvladikon/spacy-trankit/actions/workflows/tests.yml&#41;)
[//]: # ([![PyPi]&#40;https://img.shields.io/pypi/v/spacy-trankit.svg?style=flat-square&#41;]&#40;https://pypi.python.org/pypi/spacy-trankit&#41;)
[![GitHub](https://img.shields.io/github/release/imvladikon/spacy-trankit/all.svg?style=flat-square)](https://github.com/imvladikon/spacy-trankit)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg?style=flat-square)](https://github.com/ambv/black)

Using this wrapper, you'll be able to use the following annotations, computed by
your pretrained `trankit` pipeline/model:

- Statistical tokenization (reflected in the `Doc` and its tokens)
- Lemmatization (`token.lemma` and `token.lemma_`)
- Part-of-speech tagging (`token.tag`, `token.tag_`, `token.pos`, `token.pos_`)
- Morphological analysis (`token.morph`)
- Dependency parsing (`token.dep`, `token.dep_`, `token.head`)
- Named entity recognition (`doc.ents`, `token.ent_type`, `token.ent_type_`,
  `token.ent_iob`, `token.ent_iob_`)
- Sentence segmentation (`doc.sents`)

## ️️️⌛️ Installation

As of v0.1.0 `spacy-trankit` is only compatible with **spaCy v3.x**. To install
the most recent version:

```bash
pip install git+https://github.com/imvladikon/spacy-trankit
```



## 📖 Usage & Examples

Load pre-trained `trankit` model into a spaCy pipeline:

```python
import spacy_trankit

# Initialize the pipeline
nlp = spacy_trankit.load("en")

doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.")
for token in doc:
    print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_)
print(doc.ents)
```

Load it from the path:

```python
import spacy_trankit

# Initialize the pipeline
nlp = spacy_trankit.load_from_path(name="en", path="./cache") 

doc = nlp("Barack Obama was born in Hawaii. He was elected president in 2008.")
for token in doc:
    print(token.text, token.lemma_, token.pos_, token.dep_, token.ent_type_)
print(doc.ents)
```
renderspec 2.3.0
Jinja2 template renderer for generating .spec files
Requires Python: >=3.8

========================
Team and repository tags
========================

.. image:: https://governance.openstack.org/tc/badges/renderspec.svg
    :target: https://governance.openstack.org/tc/reference/tags/index.html

.. Change things from this point on

renderspec
==========

renderspec is a tool to convert a .spec.j2 Jinja2 template to
a rpm .spec file which is usable for different distributions
and follow their policies and processes.

Documentation for renderspec can be found at

   https://docs.openstack.org/renderspec/latest/

For more information about the OpenStack rpm packaging initiative, please
follow the link to the wiki page:

   https://wiki.openstack.org/wiki/Rpm-packaging

In the unfortunate event that bugs are discovered, they should
be reported to the appropriate bug tracker. If you obtained
the software from a 3rd party operating system vendor, it is
often wise to use their own bug tracker for reporting problems.
In all other cases use the master OpenStack bug tracker,
available at:

   https://bugs.launchpad.net/renderspec

Developers wishing to work on renderspec should always base
their work on the latest code, available from the master GIT
repository at:

   https://opendev.org/openstack/renderspec

Developers should also join the discussion on the mailing list,
at:

   http://lists.openstack.org/cgi-bin/mailman/listinfo/openstack-discuss

or join the IRC channel on

   https://webchat.oftc.net/?channels=openstack-rpm-packaging
pymod2pkg 0.28.0
python module name to package name map
Requires Python: >=3.6

========================
Team and repository tags
========================

.. image:: https://governance.openstack.org/tc/badges/pymod2pkg.svg
    :target: https://governance.openstack.org/tc/reference/tags/index.html

.. Change things from this point on

pymod2pkg
=========

`pymod2pkg` is a simple python module for translating python module names to
corresponding package names which is a common problem in the packaging world.
It is used for the OpenStack rpm packaging initiative.
For more information about the OpenStack rpm packaging initiative, please
follow the link to the wiki page:

   https://wiki.openstack.org/wiki/Rpm-packaging

In the unfortunate event that bugs are discovered, they should
be reported to the appropriate bug tracker. If you obtained
the software from a 3rd party operating system vendor, it is
often wise to use their own bug tracker for reporting problems.
In all other cases use the master OpenStack bug tracker,
available at:

   https://bugs.launchpad.net/pymod2pkg

Developers wishing to work on pymod2pkg should always base
their work on the latest code, available from the master GIT
repository at:

   https://opendev.org/openstack/pymod2pkg

Developers should also join the discussion on the mailing list,
at:

   http://lists.openstack.org/cgi-bin/mailman/listinfo/openstack-discuss

or join the IRC channel on

   http://webchat.oftc.net/?channels=openstack-rpm-packaging
tg2fa-translit 1.0
Translate Tajik texts to Persian with the help of a seq2seq model

# tg2fa_translit

A Tajik-to-Persian transliteration model.
It shows a Levenshtein ratio of 0.988.

## Installation
```pip install tg2fa_translit```

## Dependency
- `numpy`
- `torch`

## API

```py
from tg2fa_translit import convert

tg_text = 'То ғами фардо нахӯрем!'
fa_text = convert(text)
print(fa_text)
'تا غم فردا نخوریم!'
# Depending on your setup, the resulting string can be displayed incorrectly.
```

## Datasets and Training Details

For more details visit https://github.com/stibiumghost/tajik-persian-transliteration
delegatum 1.0.0
Simple cascading/nested scope/namespace manager
Requires Python: >=3.7

=========
delegatum
=========


.. image:: https://img.shields.io/pypi/v/delegatum.svg
        :target: https://pypi.python.org/pypi/delegatum

.. image:: https://img.shields.io/travis/datagazing/delegatum.svg
        :target: https://travis-ci.com/datagazing/delegatum

.. image:: https://readthedocs.org/projects/delegatum/badge/?version=latest
        :target: https://delegatum.readthedocs.io/en/latest/?version=latest
        :alt: Documentation Status



Simple cascading/nested scope/namespace manager

Delegatum walks a list of objects that can represent key/value
mappings and returns the first match found for a key.

- Supports dictionaries, argparse, configparser, mapping functions
- Most dictionary-like objects should work as well
- Python json and yaml parsers return dictionaries, and so are supported

Examples
--------

The following example searches, in order: command line arguments,
a config file, a json string, a yaml string, and the output of a
function.

.. code-block:: python

  import delegatum
  import json
  import yaml # Requires external package PyYAML
  j = json.loads('{"verbose": true, "output": "file.txt"}')
  y = yaml.safe_load(some_yaml_string)
  some_f = lambda x: x * x
  delegatus = delegatum.Delegatum([cmdline, configfile, j, y, some_f])
  print(delegatus['verbose'])
  print(delegatus['garbage'])

Attributes
----------
default : object
    Default value to return if no lookup succeeds

Raises
------
DelegatumError
    General library error; should only occur if misconfigured
DelegatumMissError
    Used internally for flow control

See Also
--------
Standard library:

- argparse
- configparser
- json
- tomllib

External modules:

- PyYAML
- https://pypi.org/project/nested-lookup/

Credits
-------

This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
randomly-change-self 0.2.0
Change all instances of `self` to `this` in one randomly chosen scope
Requires Python: >=3.10,<4.0

# randomly-change-self
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/randomly-change-self/0.2.0?logo=python&logoColor=FFD43B)](https://pypi.org/project/randomly-change-self/)
[![PyPI](https://img.shields.io/pypi/v/randomly-change-self?logo=Python&logoColor=FFD43B)](https://pypi.org/project/randomly-change-self/)
[![PyPI - License](https://img.shields.io/pypi/l/randomly-change-self?color=magenta)](https://github.com/sco1/randomly-change-self/blob/master/LICENSE)
[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/sco1/randomly-change-self/main.svg)](https://results.pre-commit.ci/latest/github/sco1/randomly-change-self/main)
[![Code style: black](https://img.shields.io/badge/code%20style-black-black)](https://github.com/psf/black)

Change all instances of `self` to `this` in one randomly chosen scope per file.

Inspired by [David Beazley's toot](https://mastodon.social/@dabeaz/111703946510399408).

## Installation
Install from PyPi with your favorite `pip` invocation:

```bash
$ python3 -m pip install randomly-change-self
```

You can confirm proper installation via the `randomly-change-self` CLI:
<!-- [[[cog
import cog
from subprocess import PIPE, run
out = run(["randomly-change-self", "--help"], stdout=PIPE, encoding="ascii")
cog.out(
    f"```\n$ randomly-change-self --help\n{out.stdout.rstrip()}\n```"
)
]]] -->
```
$ randomly-change-self --help
usage: randomly-change-self [-h] [--replace-with REPLACE_WITH]
                            [--consider-mercury-in-retrograde CONSIDER_MERCURY_IN_RETROGRADE]
                            [filenames ...]

positional arguments:
  filenames

options:
  -h, --help            show this help message and exit
  --replace-with REPLACE_WITH
  --consider-mercury-in-retrograde CONSIDER_MERCURY_IN_RETROGRADE
```
<!-- [[[end]]] -->

## Arguments
### `filenames`
A collection of filename(s) to process. While this isn't required, you should probably pass some filename(s) if you want something to happen.

### `replace-with`
Specify the string to replace `"self"` with. Defaults to `"this"`

It's assumed that this value is a valid Python identifier. No validation of this is done, we're all adults here.

### `consider-mercury-in-retrograde`
Set to `True` if you only want to make changes if Mercury is in retrograde.

**NOTE:** Because it turns out that astrophysics is a little challenging and I haven't done it since college, this is currently implemented as an API call to the [Mercury Retrograde API](https://mercuryretrogradeapi.com/about.html). This call should fail gracefully, and if an issue is encountered then your flag will be ignored and your code will get changed anyway.

## Pre-Commit
You can even use this as a [pre-commit](https://pre-commit.com/) hook. Wow!

```yaml
- repo: https://github.com/sco1/randomly-change-self
  rev: v0.2.0
  hooks:
  - id: randomly-change-self
    args: [--consider-mercury-in-retrograde=False]
```