bertopic 0.4.3

BERTopic performs topic Modeling with state-of-the-art transformer models.

BERTopic

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.

Installation

Installation can be done using pypi:

pip install bertopic

To use the visualization options, install BERTopic as follows:

pip install bertopic[visualization]

Installation Errors

PyTorch 1.4.0 or higher is recommended. If the install gives an error, please install pytorch first here.

Getting Started

For an in-depth overview of the features of BERTopic you can check the full documentation here or you can follow along with the Google Colab notebook here.

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']

model = BERTopic(language="english")
topics, probabilities = model.fit_transform(docs)

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

>>> 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:

>>> 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)]

Supported Languages

Use "multilingual" to select a model that supports 50+ languages.

Moreover, the following languages are supported:
Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanize, Bosnian, Breton, Bulgarian, Burmese, Burmese zawgyi font, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanize, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskrit, Scottish Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanize, Telugu, Telugu Romanize, Thai, Turkish, Ukrainian, Urdu, Urdu Romanize, Uyghur, Uzbek, Vietnamese, Welsh, Western Frisian, Xhosa, Yiddish

Visualize Topics

After having trained our BERTopic model, we can iteratively go through perhaps a hundred topic to get a good understanding of the topics that were extract. 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:

model.visualize_topics()

Visualize Topic Probabilities

The variable probabilities that is returned from transform() or fit_transform() can be used to understand how confident BERTopic is that certain topics can be found in a document.

To visualize the distributions, we simply call:

# Make sure to input the probabilities of a single document!
model.visualize_distribution(probabilities[0])

Embedding Models

You can select any model from sentence-transformers and pass it through BERTopic with embedding_model:

from bertopic import BERTopic
model = BERTopic(embedding_model="xlm-r-bert-base-nli-stsb-mean-tokens")

You can also use previously generated embeddings by passing it through fit_transform():

model = BERTopic()
topics, probabilities = model.fit_transform(docs, embeddings)

Click here for a list of supported sentence transformers models.

Overview

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

Citation

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

@misc{grootendorst2020bertopic,
  author       = {Maarten Grootendorst},
  title        = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.},
  year         = 2020,
  publisher    = {Zenodo},
  version      = {v0.4.2},
  doi          = {10.5281/zenodo.4430182},
  url          = {https://doi.org/10.5281/zenodo.4430182}
}