semvec/pypi-packages huggingface dataset:
icssplit 1.0.1
Split big .ics/.ical files into smaller ones for easy import into Google Calendar
License: MIT
Keywords: ics calendar ical vcal google gcal

icssplit
========

.. image:: https://img.shields.io/pypi/v/icssplit.svg
    :target: https://pypi.python.org/pypi/icssplit
    :alt: Latest PyPI version

.. image:: https://travis-ci.org/beorn/icssplit.png
   :target: https://travis-ci.org/beorn/icssplit
   :alt: Latest Travis CI build status

Split big .ics/.ical files into smaller ones for import into
Google Calendar, which only supports files <1MB.  To install and use:

.. code-block:: console

  $ pip3 install icssplit
  $ icssplit somefile.ics outfile --maxsize=900000

This will split `somefile` into `outfile1.ics`, `outfile2.ics`...


Related Works
-------------
 - https://github.com/druths/icssplitter - splits by year - so doesn't work if one year is >1MB
 - https://github.com/rtsai/icalutil gcalfiltersplit - lots of dependencies
 - https://www.g-transfer.com - doesn't seem to be operational?
sequence-tagging 0.1.6
Sequence Tagging powered by the Averaged Perceptron.
License: MIT
Requires Python: >=3.6
Keywords: NLP,Sequence Tagging,ML

# Part of Speech Tagging
##

A Part of Speech tagger using the Average Perceptron.

Based on the tagger from [here](https://explosion.ai/blog/part-of-speech-pos-tagger-in-python)

This uses the following features:

 * The Suffix (last 3 characters) of the current word (unnormalized).
 * The Prefix (first character) of the current word (unnormalized).
 * The current word.
 * The previous Part of Speech tag and the current word.
 * The Previous Part of Speech tag.
 * The Part of Speech tag from the word before last.
 * Both of the previous Part of Speech tags.
 * The previous word.
 * The previous word suffix.
 * The word from 2 steps back.
 * The next word.
 * The next word suffix.
 * The word after next.
 * A Bias

Includes the following Pretrained models.

  * POS Tagger, Trained on the CoNLL 2000 Chunking data
  * Chunker, Trained on the CoNLL 2000 Chunking data
  * Slot filler, Trained on ATIS data