Fix cluster selection (#182)

docs/guides/keyllm.md

@@ -18,6 +18,7 @@ This data was chosen to show the different use cases and techniques. As you migh
 
 Let's start with `KeyLLM` only. 
 
+
 # Use Cases
 
 If you want the full performance and easiest method, you can skip the use cases below and go straight to number 5 where you will combine `KeyBERT` with `KeyLLM`.
@@ -180,6 +181,13 @@ If you have embeddings of your documents, you could use those to find documents
 --8<-- "docs/images/efficient.svg"
 </div>
 
+!!! Tip
+    Before you get started, it might be worthwhile to uninstall sentence-transformers and re-install it from the main branch. 
+    There is an issue with community detection (cluster) that might make the model run without finishing. It is as straightforward as:
+    `pip uninstall sentence-transformers`
+    `pip install --upgrade git+https://github.com/UKPLab/sentence-transformers`
+
+
 ```python
 import openai
 from keybert.llm import OpenAI
@@ -224,6 +232,13 @@ This is the best of both worlds. We use `KeyBERT` to generate a first pass of ke
 --8<-- "docs/images/keybert_keyllm.svg"
 </div>
 
+!!! Tip
+    Before you get started, it might be worthwhile to uninstall sentence-transformers and re-install it from the main branch. 
+    There is an issue with community detection (cluster) that might make the model run without finishing. It is as straightforward as:
+    `pip uninstall sentence-transformers`
+    `pip install --upgrade git+https://github.com/UKPLab/sentence-transformers`
+
+
 ```python
 import openai
 from keybert.llm import OpenAI

keybert/__init__.py

@@ -1,4 +1,4 @@
 from keybert._llm import KeyLLM
 from keybert._model import KeyBERT
 
-__version__ = "0.8.2"
+__version__ = "0.8.3"

keybert/_llm.py

@@ -101,7 +101,7 @@ def extract_keywords(
             if in_cluster:
                 selected_docs = [docs[cluster[0]] for cluster in clusters]
                 if candidate_keywords is not None:
-                    selected_keywords = [candidate_keywords[cluster[0]] for cluster in in_cluster]
+                    selected_keywords = [candidate_keywords[cluster[0]] for cluster in clusters]
                 else:
                     selected_keywords = None
                 in_cluster_keywords = self.llm.extract_keywords(

setup.py

@@ -37,7 +37,7 @@
 setup(
     name="keybert",
     packages=find_packages(exclude=["notebooks", "docs"]),
-    version="0.8.2",
+    version="0.8.3",
     author="Maarten Grootendorst",
     author_email="[email protected]",
     description="KeyBERT performs keyword extraction with state-of-the-art transformer models.",