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Using the .visualize_topics, we can visualize the topics and get insight into their relationships. However, you might want a more fine-grained approach where we can visualize the documents inside the topics to see if they were assigned correctly or whether they make sense. To do so, we can use the topic_model.visualize_documents() function. This function recalculates the document embeddings and reduces them to 2-dimensional space for easier visualization purposes. This process can be quite expensive, so it is advised to adhere to the following pipeline:

from sklearn.datasets import fetch_20newsgroups
from sentence_transformers import SentenceTransformer
from bertopic import BERTopic
from umap import UMAP

# Prepare embeddings
docs = fetch_20newsgroups(subset='all',  remove=('headers', 'footers', 'quotes'))['data']
sentence_model = SentenceTransformer("all-MiniLM-L6-v2")
embeddings = sentence_model.encode(docs, show_progress_bar=False)

# Train BERTopic
topic_model = BERTopic().fit(docs, embeddings)

# Run the visualization with the original embeddings
topic_model.visualize_documents(docs, embeddings=embeddings)

# Reduce dimensionality of embeddings, this step is optional but much faster to perform iteratively:
reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
topic_model.visualize_documents(docs, reduced_embeddings=reduced_embeddings)


The visualization above was generated with the additional parameter hide_document_hover=True which disables the option to hover over the individual points and see the content of the documents. This was done for demonstration purposes as saving all those documents in the visualization can be quite expensive and result in large files. However, it might be interesting to set hide_document_hover=False in order to hover over the points and see the content of the documents.

Custom Hover

When you visualize the documents, you might not always want to see the complete document over hover. Many documents have shorter information that might be more interesting to visualize, such as its title. To create the hover based on a documents' title instead of its content, you can simply pass a variable (titles) containing the title for each document:

topic_model.visualize_documents(titles, reduced_embeddings=reduced_embeddings)

Visualize Probablities or Distribution

We can generate the topic-document probability matrix by simply setting calculate_probabilities=True if a HDBSCAN model is used:

from bertopic import BERTopic
topic_model = BERTopic(calculate_probabilities=True)
topics, probs = topic_model.fit_transform(docs) 

The resulting probs variable contains the soft-clustering as done through HDBSCAN.

If a non-HDBSCAN model is used, we can estimate the topic distributions after training our model:

from bertopic import BERTopic

topic_model = BERTopic()
topics, _ = topic_model.fit_transform(docs) 
topic_distr, _ = topic_model.approximate_distribution(docs, min_similarity=0)

Then, we either pass the probs or topic_distr variable to .visualize_distribution to visualize either the probability distributions or the topic distributions:

# To visualize the probabilities of topic assignment

# To visualize the topic distributions in a document

Although a topic distribution is nice, we may want to see how each token contributes to a specific topic. To do so, we need to first calculate topic distributions on a token level and then visualize the results:

# Calculate the topic distributions on a token-level
topic_distr, topic_token_distr = topic_model.approximate_distribution(docs, calculate_tokens=True)

# Visualize the token-level distributions
df = topic_model.visualize_approximate_distribution(docs[1], topic_token_distr[1])


To get the stylized dataframe for .visualize_approximate_distribution you will need to have Jinja installed. If you do not have this installed, an unstylized dataframe will be returned instead. You can install Jinja via pip install jinja2


The distribution of the probabilities does not give an indication to the distribution of the frequencies of topics across a document. It merely shows how confident BERTopic is that certain topics can be found in a document.