Update README.md

README.md

@@ -4,13 +4,13 @@ library_name: transformers
 datasets:
 - CCDS
 - Ensembl
-pipeline_tag: feature-extraction
+pipeline_tag: fill-mask
 tags:
 - protein language model
 - biology
 widget:
-- text: ( Z E V L P Y G D E K L S P Y G D G G D V G Q I F s C L Q ]
-  example_title: Example CCDS embedding extraction
+- text: ( Z E V L P Y G D E K L S P [MASK] G D G G D V G Q I F s C L Q ]
+  example_title: Fill codon mask (Y)
 ---
 
 # cdsBERT
@@ -18,7 +18,7 @@ widget:
 
 ## Model description
 
-
+cdsBERT is pLM with a codon vocabulary that was seeded with [ProtBERT](https://huggingface.co/Rostlab/prot_bert_bfd) and trained with a novel vocabulary extension pipeline called MELD. cdsBERT offers a highly biologically relevant latent space with excellent EC number prediction surpassing ProtBERT.
 
 ## How to use
 
@@ -46,10 +46,10 @@ vector_embedding = matrix_embedding.mean(dim=0)
 ```
 
 ## Intended use and limitations
-
+cdsBERT serves as a general purpose 
 
 ## Our lab
-The [Gleghorn lab](https://www.gleghornlab.com/) is an interdisciplinary research group out of the University of Delaware that focuses on translational problems around biomedicine. Recently we have begun exploration into protein language models and are passionate about excellent protein design and annotation.
+The [Gleghorn lab](https://www.gleghornlab.com/) is an interdiciplinary research group at the University of Delaware that focuses on solving translational problems with our expertise in engineering, biology, and chemistry. We develop inexpensive and reliable tools to study organ development, maternal-fetal health, and drug delivery. Recently we have begun exploration into protein language models and strive to make protein design and annotation accessible.
 
 ## Please cite
 Coming soon!