Labelled Topic Clustering

Labelled Topic Clustering is as the name suggests, feed it an array of sentences and it will cluster them with human-readable names.

The aim of this project is to make it as easy-as-possible to:

1. generate topic clusters on a text dataset using a cosine-similarity approach.
2. get human-readable labels for those clusters

Installation

To use the TopicClusterer class, you need to install the required packages. Assuming you have a package manager like pip, you can install the dependencies as follows:

pip install labelled-topic-clustering

Usage

1. Initialize the TopicClusterer:

from topic_clusterer import TopicClusterer

hf_token = "your_hugging_face_token"
# This can be any sentence-transformer, anecdotally I've found this the best.
model = "sentence-transformers/all-mpnet-base-v2"

clusterer = TopicClusterer(hf_token, model, debug=True)

2. Get clusters:

sentences = [
    "the weather is great",
    "This is some perfect weather",
    "we're having some really good weather",
    "my dog ate my homework",
    "why do dogs love homework?",
    "dog keeps devouring my homework"
]

clusters = clusterer.get_clusters(sentences)

Example Output

[[0, 1, 2], [3, 4, 5]]

clusters will be a 2d array representing clusters with sentence indicies for the original dataset

3. Get labels from clusters:

clusters_labelled = clusterer.get_labels_from_clusters(clusters, sentences)

Example Output

{'Weather great perfect': [0, 1, 2], 'Dog eat homework': [3, 4, 5]}

clusters_labelled is a dictionary where the keys are topic labels, and the values are arrays of sentence indices corresponding to the original dataset.

You can also just get it all at once:

# Get clusters with labels
labelled_clusters = clusterer.get_clusters_with_labels(sentences)
print(labelled_clusters)

Contributing

You can view all the info on development and contributing here

Looking Forward

I have done virtually no performance testing as I wrote this once and it was all I needed for a side project.

Some ideas to work on:

• Allow custom tokenizers
• Benchmark performance on large datasets
• Allow for feature extraction locally