GitHub - huggingface/text-clustering: Easily embed, cluster and semantically label text datasets (original) (raw)

The Text Clustering repository contains tools to easily embed and cluster texts as well as label clusters semantically. This repository is a work in progress and serves as a minimal codebase that can be modified and adapted to other use cases.

Clustering of texts in the Cosmopedia dataset.

How it works

The pipeline consists of several distinct blocks that can be customized and the whole pipeline can run in a few minutes on a consumer laptop. Each block uses existing standard methods and works quite robustly.

Text clustering pipeline.

Install

Install the following libraries to get started:

pip install scikit-learn umap-learn sentence_transformers faiss-cpu plotly matplotlib datasets

Clone this repository and navigate to the folder:

git clone https://github.com/huggingface/text-clustering.git cd text-clustering

Usage

Run pipeline and visualize results:

from src.text_clustering import ClusterClassifier from datasets import load_dataset

SAMPLE = 100_000

texts = load_dataset("HuggingFaceTB/cosmopedia-100k", split="train").select(range(SAMPLE))["text"]

cc = ClusterClassifier(embed_device="mps")

run the pipeline:

embs, labels, summaries = cc.fit(texts)

show the results

cc.show()

save

cc.save("./cc_100k")

Load classifier and run inference:

from src.text_clustering import ClusterClassifier

cc = ClusterClassifier(embed_device="mps")

load state

cc.load("./cc_100k")

visualize

cc.show()

classify new texts with k-nearest neighbour search

cluster_labels, embeddings = cc.infer(some_texts, top_k=1)

If you want to reproduce the color scheme in the plot above you can add the following code before you run cc.show():

from cycler import cycler import matplotlib.pyplot as plt

default_cycler = (cycler(color=[ "0F0A0A", "FF6600", "FFBE00", "496767", "87A19E", "FF9200", "0F3538", "F8E08E", "0F2021", "FAFAF0"]) ) plt.rc('axes', prop_cycle=default_cycler)

If you would like to customize the plotting further the easiest way is to customize or overwrite the _show_mpl and _show_plotly methods.

You can also run the pipeline using a script with:

run a new pipeline

python run_pipeline.py --mode run --save_load_path './cc_100k' --n_samples 100000 --build_hf_ds

load existing pipeline

python run_pipeline.py --mode load --save_load_path './cc_100k' --build_hf_ds

inference mode on new texts from an input dataset

python run_pipeline.py --mode infer --save_load_path './cc_100k' --n_samples --input_dataset

The build_hf_ds flag builds and pushes HF datasets, for the files and clusters, that can be directly used in the FW visualization space. In infer mode, we push the clusters dataset by default.

You can also change how the clusters are labeled (multiple topics (default) vs single topic with an educational score) using the flag --topic_mode.

Examples

Check the examples folder for an example of clustering and topic labeling applied to the AutoMathText dataset, utilizing Cosmopedia's web labeling approach.