Improve model card and add metadata

Hi, I'm Niels from the community science team at Hugging Face. I've updated your model card to include:
- A link to the paper page on the Hugging Face Hub.
- A link to the official GitHub repository.
- Relevant metadata, including the `pipeline_tag: feature-extraction`.

This helps researchers find and cite your work more easily!

README.md

@@ -1,23 +1,27 @@
 ---
 license: mit
+pipeline_tag: feature-extraction
 ---
 
+# AbAffinity
 
-## **Overview**
+This repository contains the model presented in the paper [AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2](https://huggingface.co/papers/2603.04480).
 
-`AbAffinity` is a Large Languge Model to predict binding affinity of scFv antibody sequence against SARS-CoV-2 HR2 peptide. It takes input of antibody heavy and light chain sequence, and predicts the binding affinity against a peptide in SARS-CoV-2 HR2 peptide. This peptide is common in all the variants of SARS-CoV-2. 
+**GitHub Repository**: [ucrbioinfo/AbAffinity](https://github.com/ucrbioinfo/AbAffinity)
 
-## **Key Features**
+## **Overview**
 
-- Predict Binding Affinity: Given the input antiobdy seqeunce, predict binding affinity 
+`AbAffinity` is a Large Language Model designed to predict the binding affinity of scFv antibody sequences against the SARS-CoV-2 HR2 peptide. It takes the antibody heavy and light chain sequences as input and predicts the binding affinity against a peptide common to all SARS-CoV-2 variants.
 
-- Antibody Representation: Given the input antiobdy seqeunce, provide embedding of the antibody. The model gives both residue level representation and sequence level representation. 
+## **Key Features**
 
-- Attention Contact Map:  Given the input antibody sequence, AbAffinity will give residue-residue attention maps of the antibody. 
+- **Predict Binding Affinity**: Given the input antibody sequence, predict binding affinity.
+- **Antibody Representation**: Provides both residue-level and sequence-level embeddings (representations) of the antibody.
+- **Attention Contact Map**: Generates residue-residue attention maps for the input antibody sequence.
 
 ## **Installation**
 
-You can install `AbAffinity` from Huggingface: 
+You can install `AbAffinity` from Hugging Face: 
 
 ```bash
 pip install git+https://huggingface.co/faisalashraf/abaffinity
@@ -32,7 +36,6 @@ cd abaffinity
 pip install .
 ```
 
-
 ## **Usage**
 
 Here's a quick example to get started:
@@ -44,7 +47,8 @@ from abaffinity import AbAffinity
 abmodel=AbAffinity() 
 
 
-#The model takes complete scFv sequences as input. Heavy and Light chain are connected with a linker sequence. Use make_scFv() method from the model to get the complete scFv seqeunce from heavy chain and light chain sequence.
+# The model takes complete scFv sequences as input. Heavy and Light chain are connected with a linker sequence. 
+# Use make_scFv() method from the model to get the complete scFv sequence from heavy chain and light chain sequence.
 
 heavy_seq = 'EVQLVESGAEVKKPGASVKVSCKASGYTFTSYGISWVRQAPGQGLEWMGWISAYNGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARVGRGVIDHWGQGTLVTVSS' 
 light_seq = 'SSELTQDPAVSVALGQTVRITCEGDSLDYYYANWYQQKPGQAPILVIYGKNNRPSGIADRFSGSNSGDTSSLIITGAQAEDEADYYCSSRDSSGFEVTFGAGTKLTVL'
@@ -52,15 +56,13 @@ light_seq = 'SSELTQDPAVSVALGQTVRITCEGDSLDYYYANWYQQKPGQAPILVIYGKNNRPSGIADRFSGSNSG
 scFv_seq = abmodel.make_scFv(heavy_seq, light_seq) 
 print(scFv_seq)  # Output: EVQLVESGAEVKKPGASVKVSCKASGYTFTSYGISWVRQAPGQGLEWMGWISAYNGNTNYAQKLQGRVTMTTDTSTSTAYMELRSLRSDDTAVYYCARVGRGVIDHWGQGTLVTVSSGGGGSGGGGSGGGGSSSELTQDPAVSVALGQTVRITCEGDSLDYYYANWYQQKPGQAPILVIYGKNNRPSGIADRFSGSNSGDTSSLIITGAQAEDEADYYCSSRDSSGFEVTFGAGTKLTVL
 
-#Use `get_affinity()` method to get the predicted binding affinity of the antibody sequence. 
-#You can pass a list of sequences to get embeddings for all. Make sure that you have enough memory to process the sequences altogether. You can tune the batch size for this purpose. Example: `model.get_affinity(list_sequences, batch_size=16)`. Default batch_size is 4. 
-
+# Use `get_affinity()` method to get the predicted binding affinity of the antibody sequence. 
 pred_affinity = abmodel.get_affinity(scFv_seq)
 print(pred_affinity) # Output: tensor([3.1595]) 
 
 
-# Use `get_embeddings()` method to get the embeddings for input sequences. Use `mode='res'` to get residue wise embeddings, and `mode='seq'` will give seqeunce embedding. 
-# You can pass a list of sequences to get embeddings for all. Make sure that you have enough memory to process the sequences altogether. You can tune the batch size for this purpose. Example: `model.get_embeddings(list_sequences, mode='seq', batch_size=16)`. Default batch_size is 4.
+# Use `get_embeddings()` method to get the embeddings for input sequences. 
+# Use `mode='res'` to get residue wise embeddings, and `mode='seq'` will give sequence embedding. 
 
 res_emb = abmodel.get_embeddings(scFv_seq, mode='res')
 print(res_emb.shape)  # Output: torch.Size([258, 1280])
@@ -68,22 +70,20 @@ print(res_emb.shape)  # Output: torch.Size([258, 1280])
 seq_emb = abmodel.get_embeddings(scFv_seq, mode='seq')
 print(seq_emb.shape) # Output: torch.Size([1280]) 
 
-# Use  `get_contact_map()` method to get the contact maps of the given antibody sequence. It will return a matrix of shape `L x L` where `L` is the length of input sequence. Each value in the matrix represents the contact weight between two residue in the sequence.  
+# Use `get_contact_map()` method to get the contact maps of the given antibody sequence. 
 # Use `mode='VH-VL'` if you want to plot the contacts for heavy chain and light chain separately, and `mode='scFv'` to plot single contacts for the entire scFv sequence. 
 
 contacts = abmodel.get_contact_map(scFv_seq, mode = 'scFv')
-print(contacts.shape) # Output: contact map figure,  (240, 240)
-
+print(contacts.shape) # Output: contact map figure, (240, 240)
 ```
 
-
 ## **License**
 
-This project is licensed under the MIT License - see the [LICENSE](https://github.com/yourusername/AbAffinity/blob/main/LICENSE) file for details.
+This project is licensed under the MIT License.
 
 ## **Acknowledgments**
 
-For citation:
+If you find this work useful, please cite:
 
 ```
 @article{ashraf2024large,
@@ -94,4 +94,4 @@ For citation:
   year={2024},
   publisher={Cold Spring Harbor Laboratory}
 }
-```
+```