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 created our vBERT model to address the aforementioned issues.  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-large-uncased' model for additional 66K steps (60k with MSL_128 and 6k with MSL_512) on VMware domain data. 
+We pretrained thevBERT model to address the aforementioned issues using our  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-large-uncased' model for additional 66K steps (60k with MSL_128 and 6k with MSL_512) on VMware domain data. 
 
 #### Intended Use
 The model functions as a VMware-specific Language Model.