v0.9.2 (#239)

* Update default embedding model from 'paraphrase' to 'all'
* Fix probability mapping
* Optimize cTFIDF topic extraction
* Fix algorithm image, update documentation, fix spelling, etc.
* Fix #258
* Update README with visualization example

Author: Maarten Grootendorst

.github/workflows/testing.yml

@@ -26,6 +26,6 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        pip install -e ".[dev]"
+        pip install -e ".[test]"
     - name: Run Checking Mechanisms
       run: make check

README.md

@@ -111,6 +111,20 @@ 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/tutorial/visualization/visualization.html). 
+
 ## Embedding Models
 BERTopic supports many embedding models that can be used to embed the documents and words:
 * Sentence-Transformers
@@ -119,12 +133,12 @@ BERTopic supports many embedding models that can be used to embed the documents
 * Gensim
 * USE
 
-[**Sentence-Transformers**]() is typically used as it has shown great results embedding documents 
+[**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="paraphrase-MiniLM-L6-v2")
+topic_model = BERTopic(embedding_model="all-MiniLM-L6-v2")
 ```
 
 [**Flair**](https://github.com/flairNLP/flair) allows you to choose almost any 🤗 transformers model. Simply 
@@ -185,34 +199,35 @@ For quick access to common functions, here is an overview of BERTopic's main met
 
 | Method | Code  | 
 |-----------------------|---|
-| Fit the model    |  `BERTopic().fit(docs)` |
-| Fit the model and predict documents    |  `BERTopic().fit_transform(docs)` |
-| Predict new documents    |  `BERTopic().transform([new_doc])` |
-| Access single topic   | `BERTopic().get_topic(topic=12)`  |   
-| Access all topics     |  `BERTopic().get_topics()` |
-| Get topic freq    |  `BERTopic().get_topic_freq()` |
-| Get all topic information|  `BERTopic().get_topic_info()` |
-| Get topics per class | `BERTopic().topics_per_class(docs, topics, classes)` |
-| Dynamic Topic Modeling | `BERTopic().topics_over_time(docs, topics, timestamps)` |
-| Update topic representation | `BERTopic().update_topics(docs, topics, n_gram_range=(1, 3))` |
-| Reduce nr of topics | `BERTopic().reduce_topics(docs, topics, nr_topics=30)` |
-| Find topics | `BERTopic().find_topics("vehicle")` |
-| Save model    |  `BERTopic().save("my_model")` |
+| 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()` |
+| Get topics per class | `.topics_per_class(docs, topics, classes)` |
+| Dynamic Topic Modeling | `.topics_over_time(docs, topics, timestamps)` |
+| Update topic representation | `.update_topics(docs, topics, n_gram_range=(1, 3))` |
+| Reduce nr of topics | `.reduce_topics(docs, topics, nr_topics=30)` |
+| Find topics | `.find_topics("vehicle")` |
+| Save model    |  `.save("my_model")` |
 | Load model    |  `BERTopic.load("my_model")` |
-| Get parameters |  `BERTopic().get_params()` |
+| Get parameters |  `.get_params()` |
 
 For an overview of BERTopic's visualization methods:
 
 | Method | Code  | 
 |-----------------------|---|
-| Visualize Topics    |  `BERTopic().visualize_topics()` |
-| Visualize Topic Hierarchy    |  `BERTopic().visualize_hierarchy()` |
-| Visualize Topic Terms    |  `BERTopic().visualize_barchart()` |
-| Visualize Topic Similarity  |  `BERTopic().visualize_heatmap()` |
-| Visualize Term Score Decline  |  `BERTopic().visualize_term_rank()` |
-| Visualize Topic Probability Distribution    |  `BERTopic().visualize_distribution(probs[0])` |
-| Visualize Topics over Time   |  `BERTopic().visualize_topics_over_time(topics_over_time)` |
-| Visualize Topics per Class | `BERTopic().visualize_topics_per_class(topics_per_class)` | 
+| Visualize Topics    |  `.visualize_topics()` |
+| Visualize Topic Hierarchy    |  `.visualize_hierarchy()` |
+| 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 BERTopic in your work, please use the following bibtex reference:
@@ -223,7 +238,7 @@ To cite BERTopic in your work, please use the following bibtex reference:
   title        = {BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.},
   year         = 2020,
   publisher    = {Zenodo},
-  version      = {v0.7.0},
+  version      = {v0.9.2},
   doi          = {10.5281/zenodo.4381785},
   url          = {https://doi.org/10.5281/zenodo.4381785}
 }

bertopic/__init__.py

@@ -1,6 +1,6 @@
 from bertopic._bertopic import BERTopic
 
-__version__ = "0.9.1"
+__version__ = "0.9.2"
 
 __all__ = [
     "BERTopic",