Update README.md

README.md

@@ -36,22 +36,25 @@ By default, we are using Drug+Target cold-split, as provided by tdcommons.
 Using `ibm/biomed.omics.bl.sm.ma-ted-400m` requires installing [https://github.com/BiomedSciAI/biomed-multi-alignment](https://github.com/TBD)
 
 ```
-pip install git+https://github.com/BiomedSciAI/biomed-multi-alignment.git
+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`:
 ```python
 import os
-
 from fuse.data.tokenizers.modular_tokenizer.op import ModularTokenizerOp
-from fuse.data.utils.collates import CollateDefault
 
 from mammal.examples.dti_bindingdb_kd.task import DtiBindingdbKdTask
 from mammal.keys import CLS_PRED, SCORES
 from mammal.model import Mammal
 
+# input
+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.eval()
 
 # Load Tokenizer
 tokenizer_op = ModularTokenizerOp.from_pretrained("ibm/biomed.omics.bl.sm.ma-ted-400m.dti_bindingdb_pkd")
@@ -65,11 +68,11 @@ sample_dict = DtiBindingdbKdTask.data_preprocessing(
     drug_sequence_key="drug_seq",
     norm_y_mean=None,
     norm_y_std=None,
-    device=nn_model.device,
+    device=model.device,
 )
 
-# forward pass - encoder_only mode which supports scalars predictions
-batch_dict = nn_model.forward_encoder_only([sample_dict])
+# forward pass - encoder_only mode which supports scalar predictions
+batch_dict = model.forward_encoder_only([sample_dict])
 
 # Post-process the model's output
 batch_dict = DtiBindingdbKdTask.process_model_output(
@@ -91,7 +94,7 @@ For more advanced usage, see our detailed example at: on `https://github.com/Bio
 
 ## Citation
 
-If you found our work useful, please consider to give a star to the repo and cite our paper:
+If you found our work useful, please consider giving a star to the repo and cite our paper:
 ```
 @article{TBD,
   title={TBD},