Update README.md

README.md

@@ -13,29 +13,37 @@ tags:
 # gbpatentdata/lt-patent-inventor-linking
 
 This is a [LinkTransformer](https://linktransformer.github.io/) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model - it just wraps around the class. 
-Take a look at the documentation of [sentence-transformers](https://www.sbert.net/index.html) if you want to use this model for more than what we support in our applications. 
-
 This model has been fine-tuned on the model: `sentence-transformers/all-mpnet-base-v2`. It is pretrained for the language: `en`.
 
-## Usage (LinkTransformer)
+## Usage (Sentence-Transformers)
 
-Using this model becomes easy when you have [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
+To use this model using sentence-transformers:
 
+```python 
+from sentence_transformers import SentenceTransformer
+
+# load 
+model = SentenceTransformer("all-MiniLM-L6-v2")
 ```
-pip install -U linktransformer
-```
 
-Then you can use the model like this:
+## Usage (LinkTransformer)
+
+To use this model for clustering with [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
 
 ```python
 import linktransformer as lt
 import pandas as pd
 
-df_lm_matched = lt.cluster_rows(df,
-                                model='gbpatentdata/lt-patent-inventor-linking',
-                                on=['name', 'occupation', 'year', 'address', 'firm', 'patent_title'],
-                                cluster_type='SLINK',
-                                cluster_params={'threshold': 0.1, 'min cluster size': 1, 'metric': 'cosine'}
+df_lm_matched = lt.cluster_rows(df, # df should be a dataset of unique patent-inventors 
+                                model='matthewleechen/lt-patent-inventor-linking',
+                                on=['name', 'occupation', 'year', 'address', 'firm', 'patent_title'], # cluster on these variables 
+                                cluster_type='SLINK', # use SLINK algorithm 
+                                cluster_params={ # default params 
+                                    'threshold': 0.1,
+                                    'min cluster size': 1,
+                                    'metric': 'cosine'
+                                }
+    )
 )
 
 ```