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BERTopic is a topic modeling technique that leverages 🤗 transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions.

BERTopic supports guided, (semi-) supervised, and dynamic topic modeling. It even supports visualizations similar to LDAvis!

Corresponding medium posts can be found here and here.


Installation, with sentence-transformers, can be done using pypi:

pip install bertopic

You may want to install more depending on the transformers and language backends that you will be using. The possible installations are:

pip install bertopic[flair]
pip install bertopic[gensim]
pip install bertopic[spacy]
pip install bertopic[use]

Quick Start

We start by extracting topics from the well-known 20 newsgroups dataset containing English documents:

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)

After generating topics and their probabilities, we can access the frequent topics that were generated:

>>> topic_model.get_topic_info()

Topic   Count   Name
-1      4630    -1_can_your_will_any
0       693     49_windows_drive_dos_file
1       466     32_jesus_bible_christian_faith
2       441     2_space_launch_orbit_lunar
3       381     22_key_encryption_keys_encrypted

-1 refers to all outliers and should typically be ignored. Next, let's take a look at the most frequent topic that was generated, topic 0:

>>> topic_model.get_topic(0)

[('windows', 0.006152228076250982),
 ('drive', 0.004982897610645755),
 ('dos', 0.004845038866360651),
 ('file', 0.004140142872194834),
 ('disk', 0.004131678774810884),
 ('mac', 0.003624848635985097),
 ('memory', 0.0034840976976789903),
 ('software', 0.0034415334250699077),
 ('email', 0.0034239554442333257),
 ('pc', 0.003047105930670237)]

NOTE: Use BERTopic(language="multilingual") to select a model that supports 50+ languages.


For quick access to common functions, here is an overview of BERTopic's main methods:

Method Code
Fit the model .fit(docs)
Fit the model and predict documents .fit_transform(docs)
Predict new documents .transform([new_doc])
Access single topic .get_topic(topic=12)
Access all topics .get_topics()
Get topic freq .get_topic_freq()
Get all topic information .get_topic_info()
Get representative docs per topic .get_representative_docs()
Get topics per class .topics_per_class(docs, topics, classes)
Dynamic Topic Modeling .topics_over_time(docs, topics, timestamps)
Update topic representation .update_topics(docs, topics, n_gram_range=(1, 3))
Reduce nr of topics .reduce_topics(docs, topics, nr_topics=30)
Find topics .find_topics("vehicle")
Save model .save("my_model")
Load model BERTopic.load("my_model")
Get parameters .get_params()

For an overview of BERTopic's visualization methods:

Method Code
Visualize Topics .visualize_topics()
Visualize Topic Hierarchy .visualize_hierarchy()
Visualize Topic Terms .visualize_barchart()
Visualize Topic Similarity .visualize_heatmap()
Visualize Term Score Decline .visualize_term_rank()
Visualize Topic Probability Distribution .visualize_distribution(probs[0])
Visualize Topics over Time .visualize_topics_over_time(topics_over_time)
Visualize Topics per Class .visualize_topics_per_class(topics_per_class)


To cite the BERTopic paper, please use the following bibtex reference:

  title={BERTopic: Neural topic modeling with a class-based TF-IDF procedure},
  author={Grootendorst, Maarten},
  journal={arXiv preprint arXiv:2203.05794},
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