Metadata-Version: 2.4
Name: bikkuri
Version: 0.1.0
Summary: Calculate the surprisal of words in texts.
Author-email: "J. Nathanael Philipp" <jnathanael@philipp.land>
License: GPLv3+
Project-URL: Homepage, https://github.com/jnphilipp/bikkuri
Project-URL: Bug Tracker, http://github.com/jnphilipp/bikkuri/issues
Keywords: surprisal
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Dynamic: license-file

# Bikkuri

Calculate the surprisal of words in texts.

![Tests](https://github.com/jnphilipp/bikkuri/actions/workflows/tests.yml/badge.svg)

## Requirements

* Python >= 3.11

## Usage

```python
from bikkuri import UniGramSurprisal


unigram_surprisal = UniGramSurprisal()
unigram_surprisal.fit([
    ["lorem", "ipsum", "dolor", "sit", "amet", ...],
    ["convallis", "fringilla", "dignissim", "massa", ...],
    ...
])

unigram_surprisal([["lorem", "ipsum", "dolor"]])
```
