Update README.md

README.md

@@ -1,4 +1,4 @@
-# TransHLA2.0
+# TransHLA2.0-BIND
 
 A minimal Hugging Face-compatible PyTorch model for peptide–HLA binding classification using ESM with optional LoRA and cross-attention. There is no custom predict API; inference follows the training path: tokenize peptide and HLA pseudosequence with the ESM tokenizer, pad or truncate to fixed lengths (default peptide=16, HLA=36), run a forward pass as `logits, features = model(epitope_ids, hla_ids)`, then apply softmax to get the binding probability.
 
@@ -13,8 +13,9 @@ Requirements:
 Install:
 ```bash
 pip install torch transformers peft
-
-
+```
+How to use TransHLA2.0-BIND
+```python
 import torch
 import torch.nn.functional as F
 from transformers import AutoModel, AutoTokenizer
@@ -33,9 +34,7 @@ tok = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
 peptide = "GILGFVFTL"  # 9-mer example
 # Fake placeholder pseudosequence for demo; replace with a real one from your mapping/data
 hla_pseudoseq = (
-    "GSHSMRYFYTAVSRPGRGEPRFIAVGYVDDTQFVRFDSDAASPRMEPRAPWIEQEGPEYWERETRNVK"
-    "AQSQTDRVDLRTLLRYNQSEAGSHTVQRMYGCDVGSDWRFLRGYHQYAYDGKDYIALNEDLRSWTAAD"
-    "MAAQTTKHKWEQAGAAER"
+    "YYSEYRNIYAQTDESNLYLSYDYYTWAERAYEWY"
 )
 
 # Fixed lengths (must match training)
@@ -64,4 +63,6 @@ with torch.no_grad():
     prob_bind = F.softmax(logits, dim=1)[0, 1].item()
     pred = int(prob_bind >= 0.5)
 
-print({"peptide": peptide, "bind_prob": round(prob_bind, 6), "label": pred})
+print({"peptide": peptide, "bind_prob": round(prob_bind, 6), "label": pred})
+```
+