Update README.md

README.md

@@ -58,6 +58,12 @@ kw_model.extract_keywords(doc, stop_words=None)
 
 The [keyBERT Homepage](https://github.com/MaartenGr/KeyBERT) gives several other examples on how this can be used. For instance how it can be combined with stop words, how longer phrases can be extracted and how it directly can output the highlighted text.
 
+## Keyword Extraction
+https://github.com/MaartenGr/BERTopic
+
+## Similarity Search
+[Javier]??
+
 
 ## Embeddings and Sentence Similarity (Sentence-Transformers)
 
@@ -92,6 +98,7 @@ print(scipy_cosine_scores)
 
 
 
+
 ## Embeddings and Sentence Similarity (HuggingFace Transformers)
 Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
@@ -135,6 +142,11 @@ print(scipy_cosine_scores)
 
 ```
 
+# Evaluation and Parameters
+
+## Evaluaton
+Rov-Arild?
+
 
 ## Training
 The model was trained with the parameters: