Update README.md

README.md

@@ -4,7 +4,17 @@ language:
 license: mit
 widget:
 - text: "Lou Gehrig who works for XCorp and lives in New York suffers from [MASK]"
-  example_title: "Disease test"
+  example_title: "Test for entity type: Disease"
+- text: "Overexpression of [MASK] occurs across a wide range of cancers"
+  example_title: "Test for entity type: Gene"
+- text: "Patients treated with [MASK] are vulnerable to infectious diseases"
+  example_title: "Test for entity type: Drug"
+- text: "A eGFR level below [MASK] indicates chronic kidney disease"
+  example_title: "Test for entity type: Measure "
+- text: "In the [MASK], increased daily imatinib dose induced MMR"
+  example_title: "Test for entity type: STUDY/TRIAL"
+- text: "Paul Erdos died at [MASK]"
+  example_title: "Test for entity type: TIME"
 tags:
 - {fill-mask}  # Example: audio
 ---
@@ -14,4 +24,6 @@ This model was pretrained from scratch on a custom vocabulary on Pubmed, Clinica
 
 It was used to do NER as is, with no fine-tuning as described [in this post](https://ajitrajasekharan.github.io/2021/01/02/my-first-post.html)
 
-[Towards Data Science link to the same post](https://twitter.com/TDataScience/status/1486300137366466560?s=20)
+[Towards Data Science link](https://twitter.com/TDataScience/status/1486300137366466560?s=20) to the same post
+
+[Github link](https://github.com/ajitrajasekharan/unsupervised_NER) to NER using this model in an ensemble with bert-base cased to detect 69 entity types (17 broad entity groups)