Update README.md

README.md

@@ -4,48 +4,60 @@ language:
 - en
 base_model:
 - facebook/esm2_t33_650M_UR50D
+tags:
+- protein-classification
+- bioinformatics
+- anticancer
+- esm2
+- transformers
+- torch
 ---
-# ANTICP3: Anticancer Protein Prediction using ESM2
 
-This repository hosts the fine-tuned **ESM2-based classifier** for **anticancer protein (ACP) prediction**. The model is built on top of [facebook/esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D), and it performs binary classification to predict whether a given protein sequence is anticancer.
+# ANTICP3: Anticancer Protein Prediction
+
+This model is a fine-tuned version of [`facebook/esm2-t33-650M-UR50D`](https://huggingface.co/facebook/esm2_t33_650M_UR50D) designed for **binary classification of anticancer proteins (ACPs)** from their primary sequence.
+
+> **Developed by**: [G. P. S. Raghava Lab, IIIT-Delhi](https://webs.iiitd.edu.in/raghava/)
+>  
+> **Model hosted by**: [Dr. GPS Raghava's Group](https://huggingface.co/raghavagps-group/anticp3)
 
 ---
 
 ## Model Details
 
-- **Base Model:** `facebook/esm2_t33_650M_UR50D`
-- **Task:** Binary Sequence Classification
-- **Labels:**  
-  - `0`: Non-Anticancer  
-  - `1`: Anticancer
-- **Framework:** [Transformers](https://huggingface.co/docs/transformers/index)  
-- **Format:** `Safetensors`
+| Feature            | Description                                                  |
+|--------------------|--------------------------------------------------------------|
+| **Base Model**     | [`facebook/esm2_t33_650M_UR50D`](https://huggingface.co/facebook/esm2_t33_650M_UR50D) |
+| **Fine-tuned On**  | Anticancer Protein Dataset |
+| **Model Type**     | Binary Classification                            |
+| **Labels**         | `0`: Non-Anticancer<br>`1`: Anticancer                      |
+| **Framework**      | [Transformers](https://huggingface.co/docs/transformers) + PyTorch |
+| **Format**         | `safetensors`                                                |
 
 ---
 
-## 🚀 Usage
+## Usage
 
-You can load and use this model directly with the `transformers` library:
+Use this model with the Hugging Face `transformers` library:
 
 ```python
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load tokenizer and model
+# Load tokenizer and fine-tuned model
 tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")
 model = AutoModelForSequenceClassification.from_pretrained("AmishaG/anticp3")
 
-# Example input sequence
+# Example protein sequence
 sequence = "MANCVVGYIGERCQYRDLKWWELRGGGGSGGGGSAPAFSVSPASGLSDGQSVSVSVSGAAAGETYYIAQCAPVGGQDACNPATATSFTTDASGAASFSFVVRKSYTGSTPEGTPVGSVDCATAACNLGAGNSGLDLGHVALTFGGGGGSGGGGSDHYNCVSSGGQCLYSACPIFTKIQGTCYRGKAKCCKLEHHHHHH"
 
-# Tokenize
+# Tokenize and run inference
 inputs = tokenizer(sequence, return_tensors="pt", truncation=True)
 
-# Run inference
 with torch.no_grad():
     logits = model(**inputs).logits
     probs = torch.nn.functional.softmax(logits, dim=-1)
     prediction = torch.argmax(probs, dim=1).item()
 
 labels = {0: "Non-Anticancer", 1: "Anticancer"}
-print("Prediction:", labels[prediction])
+print("Prediction:", labels[prediction])