Metadata-Version: 2.1
Name: embed_clustering
Version: 0.0.3
Summary: Clustering using different non-parameteric models with the combination of word Embedding
Home-page: https://github.com/masumeazee/Embedding_Crp_Clustering_Pro
Author: Masume Azizyan, Deepak John Reji
Author-email: masume.azee@gmail.com
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown

Clustering using different non-parameteric models with the power of bert embeddings

## Usage

```python
# import the crp algorithm
from embed_clustering.latent_component import crp_algorithm

# read the data you want to cluster
import pandas as pd
df = pd.read_csv('sample.csv')

corpus = df[column] # mention the column you want to cluster

# apply the algorithm by passing the parameters
df['cluster'] = crp_algorithm(corpus, compute='cuda', cleaning=True) #if you have gpu, else computer='cpu', if you doesn't wish to clean the text before clustering you can flag cleaning=False

```

## License
[MIT](https://choosealicense.com/licenses/mit/)
