Add an optional layer on top of BERTopic that allows for fine-tuning topic representations. You can use GPT-3, T5, KeyBERT, MMR, POS, and many other models!

KeyBERTInspired

The algorithm follows some principles of KeyBERT but does some optimization in order to speed up inference. Usage is straightforward:

from bertopic.representation import KeyBERTInspired
from bertopic import BERTopic

# Create your representation model
representation_model = KeyBERTInspired()

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

PartOfSpeech

Our candidate topics, as extracted with c-TF-IDF, do not take into account a keyword's part of speech as extracting noun-phrases from all documents can be computationally quite expensive. Instead, we can leverage c-TF-IDF to perform part of speech on a subset of keywords and documents that best represent a topic.

from bertopic.representation import PartOfSpeech
from bertopic import BERTopic

# Create your representation model
representation_model = PartOfSpeech("en_core_web_sm")

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

MaximalMarginalRelevance

When we calculate the weights of keywords, we typically do not consider whether we already have similar keywords in our topic. Words like "car" and "cars"
essentially represent the same information and often redundant. We can use MaximalMarginalRelevance to improve diversity of our candidate topics:

from bertopic.representation import MaximalMarginalRelevance
from bertopic import BERTopic

# Create your representation model
representation_model = MaximalMarginalRelevance(diversity=0.3)

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

Zero-Shot Classification

To perform zero-shot classification, we feed the model with the keywords as generated through c-TF-IDF and a set of candidate labels. If, for a certain topic, we find a similar enough label, then it is assigned. If not, then we keep the original c-TF-IDF keywords.

We use it in BERTopic as follows:

from bertopic.representation import ZeroShotClassification
from bertopic import BERTopic

# Create your representation model
candidate_topics = ["space and nasa", "bicycles", "sports"]
representation_model = ZeroShotClassification(candidate_topics, model="facebook/bart-large-mnli")

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

Text Generation: 🤗 Transformers

Nearly every week, there are new and improved models released on the 🤗 Model Hub that, with some creativity, allow for
further fine-tuning of our c-TF-IDF based topics. These models range from text generation to zero-classification. In BERTopic, wrappers around these
methods are created as a way to support whatever might be released in the future.

Using a GPT-like model from the huggingface hub is rather straightforward:

from bertopic.representation import TextGeneration
from bertopic import BERTopic

# Create your representation model
representation_model = TextGeneration('gpt2')

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

Text Generation: Cohere

Instead of using a language model from 🤗 transformers, we can use external APIs instead that
do the work for you. Here, we can use Cohere to extract our topic labels from the candidate documents and keywords.
To use this, you will need to install cohere first:

pip install cohere

Then, get yourself an API key and use Cohere's API as follows:

import cohere
from bertopic.representation import Cohere
from bertopic import BERTopic

# Create your representation model
co = cohere.Client(my_api_key)
representation_model = Cohere(co)

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

Text Generation: OpenAI

Instead of using a language model from 🤗 transformers, we can use external APIs instead that
do the work for you. Here, we can use OpenAI to extract our topic labels from the candidate documents and keywords.
To use this, you will need to install openai first:

pip install openai

Then, get yourself an API key and use OpenAI's API as follows:

import openai
from bertopic.representation import OpenAI
from bertopic import BERTopic

# Create your representation model
openai.api_key = MY_API_KEY
representation_model = OpenAI()

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

Text Generation: LangChain

Langchain is a package that helps users with chaining large language models.
In BERTopic, we can leverage this package in order to more efficiently combine external knowledge. Here, this
external knowledge are the most representative documents in each topic.

To use langchain, you will need to install the langchain package first. Additionally, you will need an underlying LLM to support langchain,
like openai:

pip install langchain, openai

Then, you can create your chain as follows:

from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
chain = load_qa_chain(OpenAI(temperature=0, openai_api_key=MY_API_KEY), chain_type="stuff")

Finally, you can pass the chain to BERTopic as follows:

from bertopic.representation import LangChain

# Create your representation model
representation_model = LangChain(chain)

# Use the representation model in BERTopic on top of the default pipeline
topic_model = BERTopic(representation_model=representation_model)

Features

• Improved the topic reduction technique when using nr_topics=int
• Added title parameters for all graphs (Feature request: title parameter for visualizations #800)

Fixes

• Probabilities loaded from a saved model not the same as probabilities after fit_transfom() #769
• Dominant topic in a document #954
• AttributeError: module 'cuml.cluster.hdbscan' has no attribute 'all_points_membership_vectors' #912
• bump pyyaml #903
• 'NoneType' object has no attribute 'embed_documents' #911
• large number of representative docs for topic 0 #965
• BUG - Random seed in _plotting #952
• custom labels on visualize_topics(), visualize_hierarchy() #976

Documentation

• Added word cloud example in tips and tricks section