Text Clustering
This repository contains tools to easily embed and cluster texts as well as label clusters and produce visualizations of those labeled clusters.
Install
Install the library to get started:
pip install --upgrade bocluster
Usage
The pipeline can be used following the code block below.
from datasets import load_dataset
from bocluster.cluster import BoClusterClassifier
# load a Tibetan language text dataset
ds = load_dataset('billingsmoore/LotsawaHouse-bo-en', split='train')
# initilialize a BoClusterClassifier object
bcc = BoClusterClassifier()
# fit the classifier on a set of texts
bcc.fit(ds['bo'][:1000])
# if you want to treat all data points as members of clusters, with no data treated as outliers
bcc.classify_outliers()
# show a visualization of results
bcc.show()
Metadata
Release files for bocluster 0.1.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| bocluster-0.1.0.tar.gz | 355.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| bocluster-0.1.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 710.6 kB
Release files / bocluster-0.1.0.tar.gz
| Download URL | bocluster-0.1.0.tar.gz |
|---|---|
| Size | 355.0 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / bocluster-0.1.0-py3-none-any.whl
| Download URL | bocluster-0.1.0-py3-none-any.whl |
|---|---|
| Size | 355.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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| Uploaded via |
twine/6.1.0 CPython/3.12.3
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