Bertopic

*Release date: 2 March, 2023*

<h3><b>Highlights:</a></b></h3>

* Use [**ChatGPT**](https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#chatgpt) to create topic representations!:
* Added `delay_in_seconds` parameter to OpenAI and Cohere representation models for throttling the API
* Setting this between 5 and 10 allows for trial users now to use more easily without hitting RateLimitErrors
* Fixed missing `title` param to visualization methods
* Fixed probabilities not correctly aligning ([1024](https://github.com/MaartenGr/BERTopic/issues/1024))
* Fix typo in textgenerator [dkopljar27](https://github.com/dkopljar27) in [#1002](https://github.com/MaartenGr/BERTopic/pull/1002)

<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#chatgpt">ChatGPT</a></b></h3>

Within OpenAI's API, the ChatGPT models use a different API structure compared to the GPT-3 models.
In order to use ChatGPT with BERTopic, we need to define the model and make sure to set `chat=True`:

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

Create your representation model
openai.api_key = MY_API_KEY
representation_model = OpenAI(model="gpt-3.5-turbo", delay_in_seconds=10, chat=True)

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


Prompting with ChatGPT is very satisfying and can be customized in BERTopic by using certain tags.
There are currently two tags, namely `"[KEYWORDS]"` and `"[DOCUMENTS]"`.
These tags indicate where in the prompt they are to be replaced with a topics keywords and top 4 most representative documents respectively.
For example, if we have the following prompt:

python
prompt = """
I have topic that contains the following documents: \n[DOCUMENTS]
The topic is described by the following keywords: [KEYWORDS]

Based on the information above, extract a short topic label in the following format:
topic: <topic label>
"""


then that will be rendered as follows and passed to OpenAI's API:

python
"""
I have a topic that contains the following documents:
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The topic is described by the following keywords: videos video you our support want this us channel patreon make on we if facebook to patreoncom can for and more watch

Based on the information above, extract a short topic label in the following format:
topic: <topic label>
"""


!!! note
Whenever you create a custom prompt, it is important to add

Based on the information above, extract a short topic label in the following format:
topic: <topic label>

at the end of your prompt as BERTopic extracts everything that comes after `topic: `. Having
said that, if `topic: ` is not in the output, then it will simply extract the entire response, so
feel free to experiment with the prompts.

*Release date: 14 February, 2023*

<h3><b>Highlights:</a></b></h3>

* Fine-tune [topic representations](https://maartengr.github.io/BERTopic/getting_started/representation/representation.html) with `bertopic.representation`
* Diverse range of models, including KeyBERT, MMR, POS, Transformers, OpenAI, and more!'
* Create your own prompts for text generation models, like GPT3:
* Use `"[KEYWORDS]"` and `"[DOCUMENTS]"` in the prompt to decide where the keywords and set of representative documents need to be inserted.
* Chain models to perform fine-grained fine-tuning
* Create and customize your represention model
* Improved the topic reduction technique when using `nr_topics=int`
* Added `title` parameters for all graphs ([800](https://github.com/MaartenGr/BERTopic/issues/800))


<h3><b>Fixes:</a></b></h3>

* Improve documentation ([837](https://github.com/MaartenGr/BERTopic/issues/837), [#769](https://github.com/MaartenGr/BERTopic/issues/769), [#954](https://github.com/MaartenGr/BERTopic/issues/954), [#912](https://github.com/MaartenGr/BERTopic/issues/912), [#911](https://github.com/MaartenGr/BERTopic/issues/911))
* Bump pyyaml ([903](https://github.com/MaartenGr/BERTopic/issues/903))
* Fix large number of representative docs ([965](https://github.com/MaartenGr/BERTopic/issues/965))
* Prevent stochastisch behavior in `.visualize_topics` ([952](https://github.com/MaartenGr/BERTopic/issues/952))
* Add custom labels parameter to `.visualize_topics` ([976](https://github.com/MaartenGr/BERTopic/issues/976))
* Fix cuML HDBSCAN type checks by [FelSiq](https://github.com/FelSiq) in [#981](https://github.com/MaartenGr/BERTopic/pull/981)

<h3><b>API Changes:</a></b></h3>
* The `diversity` parameter was removed in favor of `bertopic.representation.MaximalMarginalRelevance`
* The `representation_model` parameter was added to `bertopic.BERTopic`

<br>

<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#keybertinspired">Representation Models</a></b></h3>

Fine-tune the c-TF-IDF representation with a variety of models. Whether that is through a KeyBERT-Inspired model or GPT-3, the choice is up to you!

<iframe width="1200" height="500" src="https://user-images.githubusercontent.com/25746895/218417067-a81cc179-9055-49ba-a2b0-f2c1db535159.mp4
" title="BERTopic Overview" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<br>


<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#keybertinspired">KeyBERTInspired</a></b></h3>

The algorithm follows some principles of [KeyBERT](https://github.com/MaartenGr/KeyBERT) but does some optimization in order to speed up inference. Usage is straightforward:


python
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)



<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#partofspeech">PartOfSpeech</a></b></h3>

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.



python
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)




<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#maximalmarginalrelevance">MaximalMarginalRelevance</a></b></h3>

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:



python
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)



<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#zero-shot-classification">Zero-Shot Classification</a></b></h3>

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:

python
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)



<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#transformers">Text Generation: 🤗 Transformers</a></b></h3>

Nearly every week, there are new and improved models released on the 🤗 [Model Hub](https://huggingface.co/models) 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:

python
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)




<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#cohere">Text Generation: Cohere</a></b></h3>

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](https://docs.cohere.ai/) to extract our topic labels from the candidate documents and keywords.
To use this, you will need to install cohere first:

bash
pip install cohere


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

python
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)




<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#openai">Text Generation: OpenAI</a></b></h3>

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](https://openai.com/api/) 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:

python
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)




<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/representation/representation.html#langchain">Text Generation: LangChain</a></b></h3>

[Langchain](https://github.com/hwchase17/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:

bash
pip install langchain, openai


Then, you can create your chain as follows:

python
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:

python
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)

*Release date: 4 January, 2023*

<h3><b>Highlights:</a></b></h3>

* Calculate [topic distributions](https://maartengr.github.io/BERTopic/getting_started/distribution/distribution.html) with `.approximate_distribution` regardless of the cluster model used
* Generates topic distributions on a document- and token-levels
* Can be used for any document regardless of its size!
* [Fully supervised BERTopic](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html)
* You can now use a classification model for the clustering step instead to create a fully supervised topic model
* [Manual topic modeling](https://maartengr.github.io/BERTopic/getting_started/manual/manual.html)
* Generate topic representations from labels directly
* Allows for skipping the embedding and clustering steps in order to go directly to the topic representation step
* [Reduce outliers](https://maartengr.github.io/BERTopic/getting_started/outlier_reduction/outlier_reduction.html) with 4 different strategies using `.reduce_outliers`
* Install BERTopic without `SentenceTransformers` for a [lightweight package](https://maartengr.github.io/BERTopic/getting_started/tips_and_tricks/tips_and_tricks.html#lightweight-installation):
* `pip install --no-deps bertopic`
* `pip install --upgrade numpy hdbscan umap-learn pandas scikit-learn tqdm plotly pyyaml`
* Get meta data of trained documents such as topics and probabilities using `.get_document_info(docs)`
* Added more support for cuML's HDBSCAN
* Calculate and predict probabilities during `fit_transform` and `transform` respectively
* This should give a major speed-up when setting `calculate_probabilities=True`
* More images to the documentation and a lot of changes/updates/clarifications
* Get representative documents for non-HDBSCAN models by comparing document and topic c-TF-IDF representations
* Sklearn Pipeline [Embedder](https://maartengr.github.io/BERTopic/getting_started/embeddings/embeddings.html#scikit-learn-embeddings) by [koaning](https://github.com/koaning) in [#791](https://github.com/MaartenGr/BERTopic/pull/791)

<h3><b>Fixes:</a></b></h3>

* Improve `.partial_fit` documentation ([837](https://github.com/MaartenGr/BERTopic/issues/837))
* Fixed scipy linkage usage ([807](https://github.com/MaartenGr/BERTopic/issues/807))
* Fixed shifted heatmap ([782](https://github.com/MaartenGr/BERTopic/issues/782))
* Fixed SpaCy backend ([744](https://github.com/MaartenGr/BERTopic/issues/744))
* Fixed representative docs with small clusters (<3) ([703](https://github.com/MaartenGr/BERTopic/issues/703))
* Typo fixed by [timpal0l](https://github.com/timpal0l) in [#734](https://github.com/MaartenGr/BERTopic/pull/734)
* Typo fixed by [srulikbd](https://github.com/timpal0l) in [#842](https://github.com/MaartenGr/BERTopic/pull/842)
* Correcting iframe urls by [Mustapha-AJEGHRIR](https://github.com/Mustapha-AJEGHRIR) in [#798](https://github.com/MaartenGr/BERTopic/pull/798)
* Refactor embedding methods by [zachschillaci27](https://github.com/zachschillaci27) in [#855](https://github.com/MaartenGr/BERTopic/pull/855)
* Added diversity parameter to update_topics() function by [anubhabdaserrr](https://github.com/anubhabdaserrr) in [#887](https://github.com/MaartenGr/BERTopic/pull/887)

<h3><b><a href="https://maartengr.github.io/BERTopic/algorithm/algorithm.html">Documentation</a></b></h3>

Personally, I believe that documentation can be seen as a feature and is an often underestimated aspect of open-source. So I went a bit overboard😅... and created an animation about the three pillars of BERTopic using Manim. There are many other visualizations added, one of each variation of BERTopic, and many smaller changes.

<iframe width="1200" height="500" src="https://user-images.githubusercontent.com/25746895/205490350-cd9833e7-9cd5-44fa-8752-407d748de633.mp4
" title="BERTopic Overview" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/distribution/distribution.html">Topic Distributions</a></b></h3>

The difficulty with a cluster-based topic modeling technique is that it does not directly consider that documents may contain multiple topics. With the new release, we can now model the distributions of topics! We even consider that a single word might be related to multiple topics. If a document is a mixture of topics, what is preventing a single word to be the same?

To do so, we approximate the distribution of topics in a document by calculating and summing the similarities of tokensets (achieved by applying a sliding window) with the topics:

python
After fitting your model run the following for either your trained documents or even unseen documents
topic_distr, _ = topic_model.approximate_distribution(docs)


To calculate and visualize the topic distributions in a document on a token-level, we can run the following:

python
We need to calculate the topic distributions on a token level
topic_distr, topic_token_distr = topic_model.approximate_distribution(docs, calculate_tokens=True)

Create a visualization using a styled dataframe if Jinja2 is installed
df = topic_model.visualize_approximate_distribution(docs[0], topic_token_distr[0]); df


<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html">Supervised Topic Modeling</a></b></h3>

BERTopic now supports fully-supervised classification! Instead of using a clustering algorithm, like HDBSCAN, we can replace it with a classifier, like Logistic Regression:

python
from bertopic import BERTopic
from bertopic.dimensionality import BaseDimensionalityReduction
from sklearn.datasets import fetch_20newsgroups
from sklearn.linear_model import LogisticRegression

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

Allows us to skip over the dimensionality reduction step
empty_dimensionality_model = BaseDimensionalityReduction()

Create a classifier to be used instead of the cluster model
clf= LogisticRegression()

Create a fully supervised BERTopic instance
topic_model= BERTopic(
umap_model=empty_dimensionality_model,
hdbscan_model=clf
)
topics, probs = topic_model.fit_transform(docs, y=y)


<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/manual/manual.html">Manual Topic Modeling</a></b></h3>

When you already have a bunch of labels and simply want to extract topic representations from them, you might not need to actually learn how those can predicted. We can bypass the `embeddings -> dimensionality reduction -> clustering` steps and go straight to the c-TF-IDF representation of our labels:

python
from bertopic import BERTopic
from bertopic.backend import BaseEmbedder
from bertopic.cluster import BaseCluster
from bertopic.dimensionality import BaseDimensionalityReduction

Prepare our empty sub-models and reduce frequent words while we are at it.
empty_embedding_model = BaseEmbedder()
empty_dimensionality_model = BaseDimensionalityReduction()
empty_cluster_model = BaseCluster()

Fit BERTopic without actually performing any clustering
topic_model= BERTopic(
embedding_model=empty_embedding_model,
umap_model=empty_dimensionality_model,
hdbscan_model=empty_cluster_model,
)
topics, probs = topic_model.fit_transform(docs, y=y)


<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/outlier_reduction/outlier_reduction.html">Outlier Reduction</a></b></h3>

Outlier reduction is an frequently-discussed topic in BERTopic as its default cluster model, HDBSCAN, has a tendency to generate many outliers. This often helps in the topic representation steps, as we do not consider documents that are less relevant, but you might want to still assign those outliers to actual topics. In the modular philosophy of BERTopic, keeping training times in mind, it is now possible to perform outlier reduction **after** having trained your topic model. This allows for ease of iteration and prevents having to train BERTopic many times to find the parameters you are searching for. There are 4 different strategies that you can use, so make sure to check out the [documentation](https://maartengr.github.io/BERTopic/getting_started/outlier_reduction/outlier_reduction.html)!

Using it is rather straightforward:

python
new_topics = topic_model.reduce_outliers(docs, topics)


<h3><b><a href="https://maartengr.github.io/BERTopic/getting_started/tips_and_tricks/tips_and_tricks.html#lightweight-installation">Lightweight BERTopic</a></b></h3>

The default embedding model in BERTopic is one of the amazing sentence-transformers models, namely `"all-MiniLM-L6-v2"`. Although this model performs well out of the box, it typically needs a GPU to transform the documents into embeddings in a reasonable time. Moreover, the installation requires `pytorch` which often results in a rather large environment, memory-wise.

Fortunately, it is possible to install BERTopic without `sentence-transformers` and use it as a lightweight solution instead. The installation can be done as follows:

bash
pip install --no-deps bertopic
pip install --upgrade numpy hdbscan umap-learn pandas scikit-learn tqdm plotly pyyaml


Then, we can use BERTopic without `sentence-transformers` as follows using a CPU-based embedding technique:

python
from sklearn.pipeline import make_pipeline
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer

pipe = make_pipeline(
TfidfVectorizer(),
TruncatedSVD(100)
)

topic_model = BERTopic(embedding_model=pipe)


As a result, the entire package and resulting model can be run quickly on the CPU and no GPU is necessary!

<h3><b><a href="https://maartengr.github.io/BERTopic/api/bertopic.html#bertopic._bertopic.BERTopic.get_document_info">Document Information</a></b></h3>

Get information about the documents on which the topic was trained including the documents themselves, their respective topics, the name of each topic, the top n words of each topic, whether it is a representative document, and the probability of the clustering if the cluster model supports it. There are also options to include other metadata, such as the topic distributions or the x and y coordinates of the reduced embeddings that you can learn more about <a href="https://maartengr.github.io/BERTopic/api/bertopic.html#bertopic._bertopic.BERTopic.get_document_info">here</a>.

To get the document info, you will only need to pass the documents on which the topic model was trained:


python
>>> topic_model.get_document_info(docs)

Document Topic Name Top_n_words Probability ...
I am sure some bashers of Pens... 0 0_game_team_games_season game - team - games... 0.200010 ...
My brother is in the market for... -1 -1_can_your_will_any can - your - will... 0.420668 ...
Finally you said what you dream... -1 -1_can_your_will_any can - your - will... 0.807259 ...
Think! It is the SCSI card doing... 49 49_windows_drive_dos_file windows - drive - docs... 0.071746 ...
1) I have an old Jasmine drive... 49 49_windows_drive_dos_file windows - drive - docs... 0.038983 ...

*Release date: 5 September, 2022*

**Highlights**:

* Perform [online/incremental topic modeling](https://maartengr.github.io/BERTopic/getting_started/online/online.html) with `.partial_fit`
* Expose [c-TF-IDF model](https://maartengr.github.io/BERTopic/getting_started/ctfidf/ctfidf.html) for customization with `bertopic.vectorizers.ClassTfidfTransformer`
* The parameters `bm25_weighting` and `reduce_frequent_words` were added to potentially improve representations:
* Expose attributes for easier access to internal data
* Major changes to the [Algorithm](https://maartengr.github.io/BERTopic/algorithm/algorithm.html) page of the documentation, which now contains three overviews of the algorithm:
* [Visualize Overview](https://maartengr.github.io/BERTopic/algorithm/algorithm.html#visual-overview)
* [Code Overview](https://maartengr.github.io/BERTopic/algorithm/algorithm.html#code-overview)
* [Detailed Overview](https://maartengr.github.io/BERTopic/algorithm/algorithm.html#detailed-overview)
* Added an [example](https://maartengr.github.io/BERTopic/getting_started/tips_and_tricks/tips_and_tricks.html#keybert-bertopic) of combining BERTopic with KeyBERT
* Added many tests with the intention of making development a bit more stable

**Fixes**:

* Fixed iteratively merging topics ([632](https://github.com/MaartenGr/BERTopic/issues/632) and ([#648](https://github.com/MaartenGr/BERTopic/issues/648))
* Fixed 0th topic not showing up in visualizations ([667](https://github.com/MaartenGr/BERTopic/issues/667))
* Fixed lowercasing not being optional ([682](https://github.com/MaartenGr/BERTopic/issues/682))
* Fixed spelling ([664](https://github.com/MaartenGr/BERTopic/issues/664) and ([#673](https://github.com/MaartenGr/BERTopic/issues/673))
* Fixed 0th topic not shown in `.get_topic_info` by [oxymor0n](https://github.com/oxymor0n) in [#660](https://github.com/MaartenGr/BERTopic/pull/660)
* Fixed spelling by [domenicrosati](https://github.com/domenicrosati) in [#674](https://github.com/MaartenGr/BERTopic/pull/674)
* Add custom labels and title options to barchart [leloykun](https://github.com/leloykun) in [#694](https://github.com/MaartenGr/BERTopic/pull/694)

**Online/incremental topic modeling**:

Online topic modeling (sometimes called "incremental topic modeling") is the ability to learn incrementally from a mini-batch of instances. Essentially, it is a way to update your topic model with data on which it was not trained on before. In Scikit-Learn, this technique is often modeled through a `.partial_fit` function, which is also used in BERTopic.

At a minimum, the cluster model needs to support a `.partial_fit` function in order to use this feature. The default HDBSCAN model will not work as it does not support online updating.

python
from sklearn.datasets import fetch_20newsgroups
from sklearn.cluster import MiniBatchKMeans
from sklearn.decomposition import IncrementalPCA
from bertopic.vectorizers import OnlineCountVectorizer
from bertopic import BERTopic

Prepare documents
all_docs = fetch_20newsgroups(subset=subset, remove=('headers', 'footers', 'quotes'))["data"]
doc_chunks = [all_docs[i:i+1000] for i in range(0, len(all_docs), 1000)]

Prepare sub-models that support online learning
umap_model = IncrementalPCA(n_components=5)
cluster_model = MiniBatchKMeans(n_clusters=50, random_state=0)
vectorizer_model = OnlineCountVectorizer(stop_words="english", decay=.01)

topic_model = BERTopic(umap_model=umap_model,
hdbscan_model=cluster_model,
vectorizer_model=vectorizer_model)

Incrementally fit the topic model by training on 1000 documents at a time
for docs in doc_chunks:
topic_model.partial_fit(docs)


Only the topics for the most recent batch of documents are tracked. If you want to be using online topic modeling, not for a streaming setting but merely for low-memory use cases, then it is advised to also update the `.topics_` attribute as variations such as hierarchical topic modeling will not work afterward:

python
Incrementally fit the topic model by training on 1000 documents at a time and track the topics in each iteration
topics = []
for docs in doc_chunks:
topic_model.partial_fit(docs)
topics.extend(topic_model.topics_)

topic_model.topics_ = topics


**c-TF-IDF**:

Explicitly define, use, and adjust the `ClassTfidfTransformer` with new parameters, `bm25_weighting` and `reduce_frequent_words`, to potentially improve the topic representation:

python
from bertopic import BERTopic
from bertopic.vectorizers import ClassTfidfTransformer

ctfidf_model = ClassTfidfTransformer(bm25_weighting=True)
topic_model = BERTopic(ctfidf_model=ctfidf_model)


**Attributes**:

After having fitted your BERTopic instance, you can use the following attributes to have quick access to certain information, such as the topic assignment for each document in `topic_model.topics_`.

| Attribute | Type | Description |
|--------------------|----|---------------------------------------------------------------------------------------------|
| topics_ | List[int] | The topics that are generated for each document after training or updating the topic model. The most recent topics are tracked. |
| probabilities_ | List[float] | The probability of the assigned topic per document. These are only calculated if a HDBSCAN model is used for the clustering step. When `calculate_probabilities=True`, then it is the probabilities of all topics per document. |
| topic_sizes_ | Mapping[int, int] | The size of each topic. |
| topic_mapper_ | TopicMapper | A class for tracking topics and their mappings anytime they are merged, reduced, added, or removed. |
| topic_representations_ | Mapping[int, Tuple[int, float]] | The top *n* terms per topic and their respective c-TF-IDF values. |
| c_tf_idf_ | csr_matrix | The topic-term matrix as calculated through c-TF-IDF. To access its respective words, run `.vectorizer_model.get_feature_names()` or `.vectorizer_model.get_feature_names_out()` |
| topic_labels_ | Mapping[int, str] | The default labels for each topic. |
| custom_labels_ | List[str] | Custom labels for each topic as generated through `.set_topic_labels`. |
| topic_embeddings_ | np.ndarray | The embeddings for each topic. It is calculated by taking the weighted average of word embeddings in a topic based on their c-TF-IDF values. |
| representative_docs_ | Mapping[int, str] | The representative documents for each topic if HDBSCAN is used. |

*Release date: 11 July, 2022*

**Highlights**:

* Perform [hierarchical topic modeling](https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html) with `.hierarchical_topics`

python
hierarchical_topics = topic_model.hierarchical_topics(docs, topics)


* Visualize [hierarchical topic representations](https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html#visualizations) with `.visualize_hierarchy`

python
topic_model.visualize_hierarchy(hierarchical_topics=hierarchical_topics)


* Extract a [text-based hierarchical topic representation](https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html#visualizations) with `.get_topic_tree`

python
tree = topic_model.get_topic_tree(hierarchical_topics)


* Visualize [2D documents](https://maartengr.github.io/BERTopic/getting_started/visualization/visualization.html#visualize-documents) with `.visualize_documents()`

python
Use input embeddings
topic_model.visualize_documents(docs, embeddings=embeddings)

or use 2D reduced embeddings through a method of your own (e.g., PCA, t-SNE, UMAP, etc.)
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)


* Visualize [2D hierarchical documents](https://maartengr.github.io/BERTopic/getting_started/visualization/visualization.html#visualize-hierarchical-documents) with `.visualize_hierarchical_documents()`

python
Run the visualization with the original embeddings
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, embeddings=embeddings)

Or, if you have reduced the original embeddings already which speed things up quite a bit:
reduced_embeddings = UMAP(n_neighbors=10, n_components=2, min_dist=0.0, metric='cosine').fit_transform(embeddings)
topic_model.visualize_hierarchical_documents(docs, hierarchical_topics, reduced_embeddings=reduced_embeddings)


* Create [custom labels](https://maartengr.github.io/BERTopic/getting_started/topicrepresentation/topicrepresentation.html#custom-labels) to the topics throughout most visualizations

python
Generate topic labels
topic_labels = topic_model.generate_topic_labels(nr_words=3, topic_prefix=False, word_length=10, separator=", ")

Set them internally in BERTopic
topic_model.set_topic_labels(topics_labels)


* Manually [merge topics](https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html#merge-topics) with `.merge_topics()`

python
Merge topics 1, 2, and 3
topics_to_merge = [1, 2, 3]
topic_model.merge_topics(docs, topics, topics_to_merge)

Merge topics 1 and 2, and separately merge topics 3 and 4
topics_to_merge = [[1, 2], [3, 4]]
topic_model.merge_topics(docs, topics, topics_to_merge)


* Added example for finding similar topics between two models in the [tips & tricks](https://maartengr.github.io/BERTopic/getting_started/tips_and_tricks/tips_and_tricks.html) page
* Add multi-modal example in the [tips & tricks](https://maartengr.github.io/BERTopic/getting_started/tips_and_tricks/tips_and_tricks.html) page
* Added native [Hugging Face transformers](https://maartengr.github.io/BERTopic/getting_started/embeddings/embeddings.html#hugging-face-transformers) support

**Fixes**:

* Fix support for k-Means in `.visualize_heatmap` ([532](https://github.com/MaartenGr/BERTopic/issues/532))
* Fix missing topic 0 in `.visualize_topics` ([533](https://github.com/MaartenGr/BERTopic/issues/533))
* Fix inconsistencies in `.get_topic_info` ([572](https://github.com/MaartenGr/BERTopic/issues/572)) and ([#581](https://github.com/MaartenGr/BERTopic/issues/581))
* Add `optimal_ordering` parameter to `.visualize_hierarchy` by [rafaelvalero](https://github.com/rafaelvalero) in [#390](https://github.com/MaartenGr/BERTopic/pull/390)
* Fix RuntimeError when used as sklearn estimator by [simonfelding](https://github.com/simonfelding) in [#448](https://github.com/MaartenGr/BERTopic/pull/448)
* Fix typo in visualization documentation by [dwhdai](https://github.com/dwhdai) in [#475](https://github.com/MaartenGr/BERTopic/pull/475)
* Fix typo in docstrings by [xwwwwww](https://github.com/xwwwwww) in [#549](https://github.com/MaartenGr/BERTopic/pull/549)
* Support higher Flair versions

*Release date: 30 April, 2022*


**Highlights**:

* Use any dimensionality reduction technique instead of UMAP:

python
from bertopic import BERTopic
from sklearn.decomposition import PCA

dim_model = PCA(n_components=5)
topic_model = BERTopic(umap_model=dim_model)


* Use any clustering technique instead of HDBSCAN:

python
from bertopic import BERTopic
from sklearn.cluster import KMeans

cluster_model = KMeans(n_clusters=50)
topic_model = BERTopic(hdbscan_model=cluster_model)


**Documentation**:

* Add a CountVectorizer page with tips and tricks on how to create topic representations that fit your use case
* Added pages on how to use other dimensionality reduction and clustering algorithms
* Additional instructions on how to reduce outliers in the FAQ:

python
import numpy as np