Update README.md

README.md

@@ -24,7 +24,8 @@ license: "apache-2.0"
 #### Motivation
 Traditional BERT models struggle with VMware-specific words (Tanzu, vSphere, etc.), technical terms, and compound words. (<a href =https://medium.com/@rickbattle/weaknesses-of-wordpiece-tokenization-eb20e37fec99>Weaknesses of WordPiece Tokenization</a>)
 
-We have pretrained our vBERT model to address the aforementioned issues using our (BERT Pretraining Library)[https://medium.com/vmware-data-ml-blog/pretraining-a-custom-bert-model-6e37df97dfc4].  We have replaced the first 1k unused tokens of BERT's vocabulary with VMware-specific terms to create a modified vocabulary.  We then pretrained the 'bert-base-uncased' model for additional 78K steps (71k With MSL_128 and 7k with MSL_512) (approximately 5 epochs) on VMware domain data. 
+We have pretrained our vBERT model to address the aforementioned issues using our <a href=https://medium.com/vmware-data-ml-blog/pretraining-a-custom-bert-model-6e37df97dfc4>BERT Pretraining Library</a>. 
+<br> We have replaced the first 1k unused tokens of BERT's vocabulary with VMware-specific terms to create a modified vocabulary.  We then pretrained the 'bert-base-uncased' model for additional 78K steps (71k With MSL_128 and 7k with MSL_512) (approximately 5 epochs) on VMware domain data. 
 
 #### Intended Use
 The model functions as a VMware-specific Language Model.