spelling, wording, and a bug in the PPI prompt builder

app.py

@@ -14,7 +14,7 @@ MAIN_MARKDOWN_TEXT = """
 The **[ibm/biomed.omics.bl.sm.ma-ted-458m](https://huggingface.co/models?sort=trending&search=ibm%2Fbiomed.omics.bl)**  model family is a biomedical foundation model and its finetuned variants trained on over 2 billion biological samples across multiple modalities, including proteins, small molecules, and single-cell gene data.  
 Designed for robust performance, it achieves state-of-the-art results over a variety of tasks across the entire drug discovery pipeline and the diverse biomedical domains.  
 
-Based on the **M**olecular **A**ligned **M**ulti-**M**odal **A**rchitecture and **L**anguage (**MAMMAL**), a flexible, multi-domain architecture with an adaptable task prompt syntax.  
+Based on the [**MAMMAL** - **M**olecular **A**ligned **M**ulti-**M**odal **A**rchitecture and **L**anguage](https://arxiv.org/abs/2410.22367v2), a flexible, multi-domain architecture with an adaptable task prompt syntax.  
 The syntax allows for dynamic combinations of tokens and scalars, enabling classification, regression, and generation tasks either within a single domain or with cross-domain entities.
 
 This page demonstraits a variety of drug discovery and biomedical tasks for the model family.  Select the task to access the specific demos.

mammal_demo/ppi_task.py

@@ -58,7 +58,7 @@ class PpiTask(MammalTask):
     def crate_sample_dict(self, sample_inputs: dict, model_holder: MammalObjectBroker):
         # Create and load sample
         sample_dict = dict()
-        prompt = self.generate_prompt(*sample_inputs)
+        prompt = self.generate_prompt(**sample_inputs)
         sample_dict[ENCODER_INPUTS_STR] = prompt
 
         # Tokenize

mammal_demo/tcr_task.py

@@ -16,17 +16,17 @@ from mammal_demo.demo_framework import MammalObjectBroker, MammalTask
 class TcrTask(MammalTask):
     def __init__(self, model_dict):
         super().__init__(
-            name="T-cell receptors-peptide binding specificity", model_dict=model_dict
+            name="Mammal based TCRbeta-epitope binding affinity", model_dict=model_dict
         )
-        self.description = "T-cell receptors-peptide binding specificity (TCR)"
+        self.description = "Mammal based TCRbeta-epitope binding affinity (TCR)"
         self.examples = {
             "tcr_beta_seq": "NAGVTQTPKFQVLKTGQSMTLQCAQDMNHEYMSWYRQDPGMGLRLIHYSVGAGITDQGEVPNGYNVSRSTTEDFPLRLLSAAPSQTSVYFCASSYSWDRVLEQYFGPGTRLTVT",
             "epitope_seq": "LLQTGIHVRVSQPSL",
         }
         self.markup_text = """
-# Mammal based T-cell receptors-peptide binding specificity demonstration
+# Mammal based Mammal based TCRbeta-epitope binding affinity demonstration
 
-Given the TCR beta sequence and the epitope sequence, estimate the binding specificity.
+Given a TCR beta chain and epitope amino acid sequences, estimate the binding affinity score.
 """
 
     def create_prompt(self, tcr_beta_seq, epitope_seq):