Update readme

README.md

@@ -11,6 +11,29 @@ pipeline_tag: text-classification
 This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. 
 BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. 
 
+This pre-trained model demonstrates the use of several representation models that can be used within BERTopic. This model was trained on ~30000 ArXiv abstracts with the following topic representation methods (`bertopic.representation`):
+
+* POS
+    * `PartOfSpeech("en_core_web_lg")`
+* KeyBERTInspired
+    * `KeyBERTInspired()`
+* MMR
+    * `MaximalMarginalRelevance(diversity=0.3)`
+* KeyBERT + MMR
+    * `[KeyBERTInspired(), MaximalMarginalRelevance(diversity=0.3)]`
+* OpenAI_Label 
+    * `OpenAI(model="gpt-3.5-turbo", exponential_backoff=True, chat=True, diversity=0.1)`
+* OpenAI_Summary
+    * `[KeyBERTInspired(), summarization_model]`
+
+An example of the default c-TF-IDF representations:
+
+!["multiaspect.png"](visualization_multiaspect.png)
+
+An example of labels generated by ChatGPT (`gpt-3.5-turbo`):
+
+!["multiaspect.png"](visualization_multiaspect_openai.png)
+
 ## Usage 
 
 To use this model, please install BERTopic:
@@ -28,6 +51,12 @@ topic_model = BERTopic.load("MaartenGr/BERTopic_ArXiv")
 topic_model.get_topic_info()
 ```
 
+To view all different topic representations (keywords, labels, summary, etc.) you can run the following:
+
+```python
+topic_model.get_topic(1, full=True)
+```
+
 ## Topic overview
 
 * Number of topics: 107
@@ -148,6 +177,55 @@ topic_model.get_topic_info()
   
 </details>
 
+## Training Procedure
+
+The model was trained as follows:
+
+```python
+from cuml.manifold import UMAP
+from cuml.cluster import HDBSCAN
+from bertopic import BERTopic
+from sklearn.feature_extraction.text import CountVectorizer
+from bertopic.representation import PartOfSpeech, KeyBERTInspired, MaximalMarginalRelevance, OpenAI
+
+# Prepare sub-models
+embedding_model = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
+umap_model = UMAP(n_components=5, n_neighbors=50, random_state=42, metric="cosine", verbose=True)
+hdbscan_model = HDBSCAN(min_samples=20, gen_min_span_tree=True, prediction_data=False, min_cluster_size=20, verbose=True)
+vectorizer_model = CountVectorizer(stop_words="english", ngram_range=(1, 3), min_df=5)
+
+# Summarization with ChatGPT
+summarization_prompt = """
+I have a topic that is described by the following keywords: [KEYWORDS]
+In this topic, the following documents are a small but representative subset of all documents in the topic:
+[DOCUMENTS]
+
+Based on the information above, please give a description of this topic in the following format:
+topic: <description>
+"""
+summarization_model = OpenAI(model="gpt-3.5-turbo", chat=True, prompt=summarization_prompt, nr_docs=5, exponential_backoff=True, diversity=0.1)
+
+# Representation models
+representation_models = {
+    "POS": PartOfSpeech("en_core_web_lg"),
+    "KeyBERTInspired": KeyBERTInspired(),
+    "MMR": MaximalMarginalRelevance(diversity=0.3),
+    "KeyBERT + MMR": [KeyBERTInspired(), MaximalMarginalRelevance(diversity=0.3)],
+    "OpenAI_Label": OpenAI(model="gpt-3.5-turbo", exponential_backoff=True, chat=True, diversity=0.1),
+    "OpenAI_Summary": [KeyBERTInspired(), summarization_model],
+}
+
+# Fit BERTopic
+topic_model= BERTopic(
+        embedding_model=embedding_model,
+        umap_model=umap_model,
+        hdbscan_model=hdbscan_model,
+        vectorizer_model=vectorizer_model,
+        representation_model=representation_models,
+        verbose=True
+).fit(docs)
+```
+
 ## Training hyperparameters
 
 * calculate_probabilities: False