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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. It even supports visualizations similar to LDAvis!

Corresponding medium post can be found here and here.


Installation can be done using pypi:

pip install bertopic

To use the visualization options, install BERTopic as follows:

pip install bertopic[visualization]

To use Flair embeddings, install BERTopic as follows:

pip install bertopic[flair]

Finally, to install all versions:

pip install bertopic[all]


Below is an example of how to use the model. The example uses the 20 newsgroups dataset.

You can also follow along with the Google Colab notebook here.

from bertopic import BERTopic
from sklearn.datasets import fetch_20newsgroups

docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']

topic_model = BERTopic()
topics, _ = topic_model.fit_transform(docs)

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

>>> topic_model.get_topic_freq().head()
Topic   Count
-1  7288
49  3992
30  701
27  684
11  568

-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 49:

>>> topic_model.get_topic(49)
[('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.


Methods Code
Fit the model])
Fit the model and predict documents topic_model.fit_transform(docs])
Predict new documents topic_model.transform([new_doc])
Access single topic topic_model.get_topic(12)
Access all topics topic_model.get_topics()
Get topic freq topic_model.get_topic_freq()
Visualize Topics topic_model.visualize_topics()
Visualize Topic Probability Distribution topic_model.visualize_distribution(probabilities[0])
Update topic representation topic_model.update_topics(docs, topics, n_gram_range=(1, 3))
Reduce nr of topics topic_model.reduce_topics(docs, topics, nr_topics=30)
Find topics topic_model.find_topics("vehicle")
Save model"my_model")
Load model BERTopic.load("my_model")
Get parameters topic_model.get_params()


To cite BERTopic in your work, please use the following bibtex reference:

  author       = {Maarten Grootendorst},
  title        = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.},
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.5.0},
  doi          = {10.5281/zenodo.4430182},
  url          = {}