semvec/pypi-packages huggingface dataset:
nka 1.0.2
An example package. Generated with cookiecutter-pylibrary.
License: MIT
Requires Python: >=3.6

========
Overview
========

Input Sequence Length
Transformer models like BERT / RoBERTa / DistilBERT etc. the runtime and the memory requirement grows quadratic with the input length. This limits transformers to inputs of certain lengths. A common value for BERT & Co. are 512 word pieces, which corresponde to about 300-400 words (for English). Longer texts than this are truncated to the first x word pieces.

By default, the provided methods use a limit fo 128 word pieces, longer inputs will be truncated. You can get and set the maximal sequence length like this:




An example package. Generated with cookiecutter-pylibrary.

* Free software: MIT license

Installation
============

::

    pip install nka

You can also install the in-development version with::

    pip install https://github.com/tobibias/neural-keyword-assignment/archive/main.zip


Documentation
=============


https://neural-keyword-assignment.readthedocs.io/


Development
===========

To run all the tests run::

    tox

Note, to combine the coverage data from all the tox environments run:

.. list-table::
    :widths: 10 90
    :stub-columns: 1

    - - Windows
      - ::

            set PYTEST_ADDOPTS=--cov-append
            tox

    - - Other
      - ::

            PYTEST_ADDOPTS=--cov-append tox


Changelog
=========

0.0.0 (2022-06-22)
------------------

* First release on PyPI.