# BERTopic — Neural Topic Modeling with Transformers

> BERTopic is a topic modeling library that uses transformer embeddings, UMAP dimensionality reduction, and HDBSCAN clustering to discover interpretable topics from text documents.

## Install

Save as a script file and run:

# BERTopic — Neural Topic Modeling with Transformers

## Quick Use
```bash
pip install bertopic
python -c "
from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups
docs = fetch_20newsgroups(subset='all', remove=('headers','footers','quotes'))['data']
topic_model = BERTopic()
topics, probs = topic_model.fit_transform(docs)
print(topic_model.get_topic_info().head(10))
"
```

## Introduction
BERTopic is a topic modeling technique that combines document embeddings from sentence transformers with a class-based TF-IDF procedure (c-TF-IDF) to generate coherent topic representations. Unlike LDA, it does not assume a fixed number of topics and can discover the optimal topic count automatically through density-based clustering.

## What BERTopic Does
- Discovers topics from document collections without specifying the number of topics in advance
- Represents each topic with interpretable keywords derived from c-TF-IDF scores
- Supports dynamic topic modeling to track how topics evolve over time
- Enables hierarchical topic merging for exploring topic granularity
- Generates topic visualizations including intertopic distance maps and bar charts

## Architecture Overview
BERTopic's pipeline has four stages. First, documents are embedded into dense vectors using a sentence transformer (or any custom embedding model). Second, UMAP reduces the embedding dimensionality while preserving local structure. Third, HDBSCAN clusters the reduced embeddings into groups, with outliers assigned to a noise topic. Fourth, c-TF-IDF computes per-topic word importance scores to produce human-readable topic labels. Each stage is modular and can be swapped with alternatives (PCA instead of UMAP, k-means instead of HDBSCAN, KeyBERT for representation).

## Self-Hosting & Configuration
- Install with `pip install bertopic` (includes sentence-transformers, umap-learn, hdbscan)
- Configure the embedding model via `BERTopic(embedding_model='all-MiniLM-L6-v2')` or pass a custom model
- Adjust UMAP and HDBSCAN parameters for cluster granularity and noise handling
- Save and load trained models with `topic_model.save()` and `BERTopic.load()`
- Use GPU-accelerated embeddings for faster processing on large corpora

## Key Features
- Modular design: swap out embedding, reduction, clustering, and representation components independently
- Dynamic topic modeling tracks topic evolution across timestamps
- Online learning mode for incremental updates without retraining from scratch
- Multi-modal support: combine text with images for visual topic modeling
- LLM-enhanced topic labels using GPT or other language models for more descriptive names

## Comparison with Similar Tools
- **Gensim LDA** — classic probabilistic topic model requiring a fixed topic count; BERTopic discovers topics automatically with richer representations
- **Top2Vec** — similar embedding-based approach; BERTopic adds c-TF-IDF for more interpretable topics and offers more customization
- **Scikit-learn NMF/LDA** — lightweight but limited to bag-of-words; BERTopic uses contextual embeddings
- **CorEx** — information-theoretic topic model supporting anchored topics; BERTopic is more flexible with modern transformers
- **LDA2Vec** — combines LDA with word2vec; less actively maintained than BERTopic

## FAQ
**Q: How many documents does BERTopic need to produce good topics?**
A: Generally a few hundred documents suffice, but quality improves with thousands. Very small corpora (under 100 docs) may not cluster well.

**Q: Can I specify the number of topics?**
A: Yes. Set `nr_topics` to reduce topics after fitting, or use `min_topic_size` and HDBSCAN parameters to control granularity during clustering.

**Q: Does BERTopic support languages other than English?**
A: Yes. Use a multilingual embedding model (e.g., `paraphrase-multilingual-MiniLM-L12-v2`) and BERTopic will discover topics in any language.

**Q: Can I update a trained model with new documents?**
A: Yes. BERTopic supports online learning via `partial_fit()` and `transform()` to incorporate new documents without full retraining.

## Sources
- https://github.com/MaartenGr/BERTopic
- https://maartengr.github.io/BERTopic/

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Source: https://tokrepo.com/en/workflows/asset-56cec9fe
Author: Script Depot