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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 which is comprised of 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, 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)]


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

Visualize Topics

After having trained our BERTopic model, we can iteratively go through perhaps a hundred topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis:


Save/Load BERTopic model

We can easily save a trained BERTopic model by calling save:

from bertopic import BERTopic
topic_model = BERTopic()"my_model")

Then, we can load the model in one line:

topic_model = BERTopic.load("my_model")


If you do not want to save the embedding model because it is loaded from the cloud, simply run"my_model", save_embedding_model=False) instead. Then, you can load in the model with BERTopic.load("my_model", embedding_model="whatever_model_you_used").

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