Update README.md

README.md

@@ -7,6 +7,9 @@ metrics:
 model-index:
 - name: output_v3
   results: []
+widget:
+  - text: >-
+      <|endoftext|>MAADGYLPDWLEDNLSEGIREWWALKPGAPQPKANQQHQDNARGLVLPGYKYLGPGNGL
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -25,7 +28,42 @@ More information needed
 
 ## Intended uses & limitations
 
-More information needed
+### Generate novel sequences for viral capsid proteins
+
+### Calculate the perplexity of a protein sequence
+
+```python
+def calculatePerplexity(sequence, model, tokenizer):
+    input_ids = torch.tensor(tokenizer.encode(sequence)).unsqueeze(0) 
+    input_ids = input_ids.to(device)
+    with torch.no_grad():
+        outputs = model(input_ids, labels=input_ids)
+    loss, logits = outputs[:2]
+    return math.exp(loss)
+
+def split_sequence(sequence):
+    chunks = []
+    max_i = 0
+    for i in range(0, len(sequence), 60):
+        chunk = sequence[i:i+60]
+        
+        if i == 0:
+            chunk = '<|endoftext|>' + chunk[:-1]
+        chunks.append(chunk)
+        max_i = i
+    
+    chunks = '\n'.join(chunks)
+    
+    if max_i+61==len(sequence):
+        chunks = chunks+"\n<|endoftext|>"
+    else:
+        chunks = chunks+"<|endoftext|>"
+    return chunks
+
+seq = "MAADGYLPDWLEDNLSEGIREWWALKPGAPQPKANQQHQDNARGLVLPGYKYLGPGNGLDKGEPVNAADAAALEHDKAYDQQLKAGDNPYLKYNHADAEFQERLKEDTSFGGNLGRAVFQAKKRLLEPLGLVEEAAKTAPGKKRPVEQSPQEPDSSAGIGKSGAQPAKKRLNFGQTGDTESVPDPQPIGEPPAAPSGVGSLTMASGGGAPVADNNEGADGVGSSSGNWHCDSQWLGDRVITTSTRTWALPTYNNHLYKQISNSTSGGSSNDNAYFGYSTPWGYFDFNRFHCHFSPRDWQRLINNNWGFRPKRLNFKLFNIQVKEVTDNNGVKTIANNLTSTVQVFTDSDYQLPYVLGSAHEGCLPPFPADVFMIPQYGYLTLNDGSQAVGRSSFYCLEYFPSQMLRTGNNFQFSYEFENVPFHSSYAHSQSLDRLMNPLIDQYLYYLSKTINGSGQNQQTLKFSVAGPSNMAVQGRNYIPGPSYRQQRVSTTVTQNNNSEFAWPGASSWALNGRNSLMNPGPAMASHKEGEDRFFPLSGSLIFGKQGTGRDNVDADKVMITNEEEIKTTNPVATESYGQVATNHQSAQAQAQTGWVQNQGILPGMVWQDRDVYLQGPIWAKIPHTDGNFHPSPLMGGFGMKHPPPQILIKNTPVPADPPTAFNKDKLNSFITQYSTGQVSVEIEWELQKENSKRWNPEIQYTSNYYKSNNVEFAVNTEGVYSEPRPIGTRYLTRNL"
+seq = split_sequence(seq)
+print(f"{calculatePerplexity(seq, model, tokenizer):.2f}")
+```
 
 ## Training and evaluation data