Update README.md

README.md

@@ -10,7 +10,7 @@ tags:
 library_name: biomed
 license: apache-2.0
 base_model:
-- ibm/biomed.omics.bl.sm.ma-ted-400m
+- ibm/biomed.omics.bl.sm.ma-ted-458m
 ---
 
 Accurate prediction of drug-target binding affinity is essential in the early stages of drug discovery.  
@@ -33,13 +33,13 @@ By default, we are using Drug+Target cold-split, as provided by tdcommons.
 
 ## Usage
 
-Using `ibm/biomed.omics.bl.sm.ma-ted-400m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD)
+Using `ibm/biomed.omics.bl.sm.ma-ted-458m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD)
 
 ```
 pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git#egg=mammal[examples]
 ```
 
-A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-400m`:
+A simple example for a task already supported by `ibm/biomed.omics.bl.sm.ma-ted-458m`:
 ```python
 import os
 from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp
@@ -53,11 +53,11 @@ target_seq = "NLMKRCTRGFRKLGKCTTLEEEKCKTLYPRGQCTCSDSKMNTHSCDCKSC"
 drug_seq = "CC(=O)NCCC1=CNc2c1cc(OC)cc2"
 
 # Load Model
-model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m.dti_bindingdb_pkd")
+model = Mammal.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd")
 model.eval()
 
 # Load Tokenizer
-tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m.dti_bindingdb_pkd")
+tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-458m.dti_bindingdb_pkd")
 
 # convert to MAMMAL style
 sample_dict = {"target_seq": target_seq, "drug_seq": drug_seq}