Metadata-Version: 2.1
Name: bertopic
Version: 0.12.0
Summary: BERTopic performs topic Modeling with state-of-the-art transformer models.
Home-page: https://github.com/MaartenGr/BERTopic
Author: Maarten P. Grootendorst
Author-email: maartengrootendorst@gmail.com
License: UNKNOWN
Project-URL: Documentation, https://maartengr.github.io/BERTopic/
Project-URL: Source Code, https://github.com/MaartenGr/BERTopic/
Project-URL: Issue Tracker, https://github.com/MaartenGr/BERTopic/issues
Keywords: nlp bert topic modeling embeddings
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Operating System :: Unix
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.8
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# BERTopic

<img src="images/logo.png" width="35%" height="35%" align="right" />

BERTopic is a topic modeling technique that leverages ðŸ¤— transformers and c-TF-IDF to create dense clusters
allowing for easily interpretable topics whilst keeping important words in the topic descriptions.

BERTopic supports 
[**guided**](https://maartengr.github.io/BERTopic/getting_started/guided/guided.html), 
(semi-) [**supervised**](https://maartengr.github.io/BERTopic/getting_started/supervised/supervised.html), 
[**hierarchical**](https://maartengr.github.io/BERTopic/getting_started/hierarchicaltopics/hierarchicaltopics.html), 
[**dynamic**](https://maartengr.github.io/BERTopic/getting_started/topicsovertime/topicsovertime.html), and 
[**online**](https://maartengr.github.io/BERTopic/getting_started/online/online.html) topic modeling. It even supports visualizations similar to LDAvis!

Corresponding medium posts can be found [here](https://towardsdatascience.com/topic-modeling-with-bert-779f7db187e6?source=friends_link&sk=0b5a470c006d1842ad4c8a3057063a99) 
and [here](https://towardsdatascience.com/interactive-topic-modeling-with-bertopic-1ea55e7d73d8?sk=03c2168e9e74b6bda2a1f3ed953427e4). For a more detailed overview, you can read the [paper](https://arxiv.org/abs/2203.05794). 

## Installation

Installation, with sentence-transformers, can be done using [pypi](https://pypi.org/project/bertopic/):

```bash
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: 

```bash
pip install bertopic[flair]
pip install bertopic[gensim]
pip install bertopic[spacy]
pip install bertopic[use]
```

## Getting Started
For an in-depth overview of the features of BERTopic 
you can check the [**full documentation**](https://maartengr.github.io/BERTopic/) or you can follow along 
with one of the examples below:

| Name  | Link  |
|---|---|
| Topic Modeling with BERTopic  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1FieRA9fLdkQEGDIMYl0I3MCjSUKVF8C-?usp=sharing)  |
| (Custom) Embedding Models in BERTopic  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/18arPPe50szvcCp_Y6xS56H2tY0m-RLqv?usp=sharing) |
| Advanced Customization in BERTopic  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1ClTYut039t-LDtlcd-oQAdXWgcsSGTw9?usp=sharing) |
| (semi-)Supervised Topic Modeling with BERTopic  |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bxizKzv5vfxJEB29sntU__ZC7PBSIPaQ?usp=sharing)  |
| Dynamic Topic Modeling with Trump's Tweets  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1un8ooI-7ZNlRoK0maVkYhmNRl0XGK88f?usp=sharing)  |
| Topic Modeling arXiv Abstracts | [![Kaggle](https://img.shields.io/static/v1?style=for-the-badge&message=Kaggle&color=222222&logo=Kaggle&logoColor=20BEFF&label=)](https://www.kaggle.com/maartengr/topic-modeling-arxiv-abstract-with-bertopic) |


## Quick Start
We start by extracting topics from the well-known 20 newsgroups dataset containing English documents:

```python
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 and their probabilities, we can access the frequent topics that were generated:

```python
>>> 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:

```python
>>> 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)]
```  

**NOTE**: 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 hundreds of 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](https://github.com/cpsievert/LDAvis):

```python
topic_model.visualize_topics()
``` 

<img src="images/topic_visualization.gif" width="60%" height="60%" align="center" />

We can create an overview of the most frequent topics in a way that they are easily interpretable. 
Horizontal barcharts typically convey information rather well and allow for an intuitive representation 
of the topics: 

```python
topic_model.visualize_barchart()
``` 

<img src="images/topics.png" width="70%" height="70%" align="center" />


Find all possible visualizations with interactive examples in the documentation 
[here](https://maartengr.github.io/BERTopic/getting_started/visualization/visualization.html). 

## Embedding Models
BERTopic supports many embedding models that can be used to embed the documents and words:
* Sentence-Transformers
* ðŸ¤— Transformers
* Flair
* Spacy
* Gensim
* USE

[**Sentence-Transformers**](https://github.com/UKPLab/sentence-transformers) is typically used as it has shown great results embedding documents 
meant for semantic similarity. Simply select any from their documentation 
[here](https://www.sbert.net/docs/pretrained_models.html) and pass it to BERTopic:

```python
topic_model = BERTopic(embedding_model="all-MiniLM-L6-v2")
```

Similarly, you can choose any [**ðŸ¤— Transformers**](https://huggingface.co/models) model and pass it to BERTopic:

```python
from transformers.pipelines import pipeline

embedding_model = pipeline("feature-extraction", model="distilbert-base-cased")
topic_model = BERTopic(embedding_model=embedding_model)
```

Click [here](https://maartengr.github.io/BERTopic/getting_started/embeddings/embeddings.html) 
for a full overview of all supported embedding models. 

## Overview
BERTopic has quite a number of functions that quickly can become overwhelming. To alleviate this issue, you will find an overview 
of all methods and a short description of its purpose. 

### Common
Below, you will find an overview of common functions in BERTopic. 

| Method | Code  | 
|-----------------------|---|
| Fit the model    |  `.fit(docs)` |
| Fit the model and predict documents  |  `.fit_transform(docs)` |
| Predict new documents    |  `.transform([new_doc])` |
| Access single topic   | `.get_topic(topic=12)`  |   
| Access all topics     |  `.get_topics()` |
| Get topic freq    |  `.get_topic_freq()` |
| Get all topic information|  `.get_topic_info()` |
| Get representative docs per topic |  `.get_representative_docs()` |
| Update topic representation | `.update_topics(docs, n_gram_range=(1, 3))` |
| Generate topic labels | `.generate_topic_labels()` |
| Set topic labels | `.set_topic_labels(my_custom_labels)` |
| Merge topics | `.merge_topics(docs, topics_to_merge)` |
| Reduce nr of topics | `.reduce_topics(docs, nr_topics=30)` |
| Find topics | `.find_topics("vehicle")` |
| Save model    |  `.save("my_model")` |
| Load model    |  `BERTopic.load("my_model")` |
| Get parameters |  `.get_params()` |


### Attributes
After having trained your BERTopic model, a number of attributes are saved within your model. These attributes, in part, 
refer to how model information is stored on an estimator during fitting. The attributes that you see below all end in `_` and are 
public attributes that can be used to access model information. 

| Attribute | Description |
|------------------------|---------------------------------------------------------------------------------------------|
| topics_               | The topics that are generated for each document after training or updating the topic model. |
| probabilities_ | The probabilities that are generated for each document if HDBSCAN is used. |
| topic_sizes_           | The size of each topic                                                                      |
| topic_mapper_          | A class for tracking topics and their mappings anytime they are merged/reduced.             |
| topic_representations_ | The top *n* terms per topic and their respective c-TF-IDF values.                             |
| c_tf_idf_              | The topic-term matrix as calculated through c-TF-IDF.                                       |
| topic_labels_          | The default labels for each topic.                                                          |
| custom_labels_         | Custom labels for each topic as generated through `.set_topic_labels`.                                                               |
| topic_embeddings_      | The embeddings for each topic if `embedding_model` was used.                                                              |
| representative_docs_   | The representative documents for each topic if HDBSCAN is used.                                                |


### Variations
There are many different use cases in which topic modeling can be used. As such, a number of 
variations of BERTopic have been developed such that one package can be used across across many use cases.

| Method | Code  | 
|-----------------------|---|
| (semi-) Supervised Topic Modeling | `.fit(docs, y=y)` |
| Topic Modeling per Class | `.topics_per_class(docs, classes)` |
| Dynamic Topic Modeling | `.topics_over_time(docs, timestamps)` |
| Hierarchical Topic Modeling | `.hierarchical_topics(docs)` |
| Guided Topic Modeling | `BERTopic(seed_topic_list=seed_topic_list)` |

### Visualizations
Evaluating topic models can be rather difficult due to the somewhat subjective nature of evaluation. 
Visualizing different aspects of the topic model helps in understanding the model and makes it easier 
to tweak the model to your liking. 

| Method | Code  | 
|-----------------------|---|
| Visualize Topics    |  `.visualize_topics()` |
| Visualize Documents    |  `.visualize_documents()` |
| Visualize Document Hierarchy    |  `.visualize_hierarchical_documents()` |
| Visualize Topic Hierarchy    |  `.visualize_hierarchy()` |
| Visualize Topic Tree   |  `.get_topic_tree(hierarchical_topics)` |
| Visualize Topic Terms    |  `.visualize_barchart()` |
| Visualize Topic Similarity  |  `.visualize_heatmap()` |
| Visualize Term Score Decline  |  `.visualize_term_rank()` |
| Visualize Topic Probability Distribution    |  `.visualize_distribution(probs[0])` |
| Visualize Topics over Time   |  `.visualize_topics_over_time(topics_over_time)` |
| Visualize Topics per Class | `.visualize_topics_per_class(topics_per_class)` | 


## Citation
To cite the [BERTopic paper](https://arxiv.org/abs/2203.05794), please use the following bibtex reference:

```bibtext
@article{grootendorst2022bertopic,
  title={BERTopic: Neural topic modeling with a class-based TF-IDF procedure},
  author={Grootendorst, Maarten},
  journal={arXiv preprint arXiv:2203.05794},
  year={2022}
}
```


