Text-Clustering

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text-clustering is an open-source tool that helps easily embed, cluster, and semantically label text datasets. It provides a customizable pipeline for text analysis and visualization.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is text-clustering?

text-clustering is an open-source repository from Hugging Face designed to simplify the process of embedding, clustering, and semantically labeling text datasets. It offers a minimal yet robust codebase that can be adapted for various use cases, making it suitable for researchers and developers working with large text corpora. The tool's pipeline consists of several distinct, customizable blocks, ensuring flexibility and control over the text analysis process. It supports installation via pip and provides clear usage examples for running the pipeline, visualizing results, and performing inference on new texts. The repository also includes options for customizing plotting and integrating with Hugging Face datasets for visualization.

Best used for

Ideal for developers and data scientists who need to organize unstructured text data, identify underlying themes, and prepare datasets for machine learning. Especially valuable for quickly embedding, clustering, and semantically labeling large text corpora with a customizable and robust pipeline.

Common actions

cluster text data

label text semantically

embed text

analyze text datasets

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Capabilities

Key features

Embed text datasets
Cluster text datasets
Semantically label clusters
Customizable pipeline blocks
Visualize clustering results
Classify new texts

Target Audience

developer

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What libraries are required to install text-clustering?

To get started with text-clustering, you need to install several Python libraries including scikit-learn, umap-learn, sentence_transformers, faiss-cpu, plotly, matplotlib, and datasets. These can be installed using pip after cloning the repository.

Can I customize the plotting and visualization of the clusters?

Yes, text-clustering allows for customization of plotting. You can modify or overwrite the `_show_mpl` and `_show_plotly` methods within the tool. Additionally, you can adjust the color scheme for plots using matplotlib's cycler before displaying results.

How can I use text-clustering for inference on new texts?

After training a `ClusterClassifier` and saving its state, you can load it and use the `infer` method with your new texts. This allows you to classify new texts based on the learned clusters, providing their cluster labels and embeddings.

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Also listed in

This tool also appears in

Data & Analytics › Data Cleaning & Prep AI Agents & Automation › RAG & Document AI Data & Analytics › Statistical & Scientific

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