Update README.md

README.md

@@ -14,7 +14,7 @@ datasets:
 ## Model Description
 
 
-This model predicts receptor classes, identified by their PDB IDs, from peptide sequences using the [ESM2](https://huggingface.co/docs/transformers/model_doc/esm) (Evolutionary Scale Modeling) protein language model with esm2_t6_8M_UR50D pre-trained weights. The model is fine-tuned for receptor prediction using datasets from [PROPEDIA](http://bioinfo.dcc.ufmg.br/propedia2/) and [PepNN](https://www.nature.com/articles/s42003-022-03445-2), as well as novel peptides experimentally validated to bind to their target proteins, with binding conformations determined using ClusPro, a protein-protein docking tool. The name `pep2rec_cppp` reflects the model's ability to predict peptide-to-receptor relationships, leveraging training data from ClusPro, PROPEDIA, and PepNN.
+This model predicts receptor classes, identified by their PDB IDs, from peptide sequences using the [ESM2](https://huggingface.co/docs/transformers/model_doc/esm) (Evolutionary Scale Modeling) protein language model with esm2_t12_35M_UR50D pre-trained weights. The model is fine-tuned for receptor prediction using datasets from [PROPEDIA](http://bioinfo.dcc.ufmg.br/propedia2/) and [PepNN](https://www.nature.com/articles/s42003-022-03445-2), as well as novel peptides experimentally validated to bind to their target proteins, with binding conformations determined using ClusPro, a protein-protein docking tool. The name `pep2rec_cppp` reflects the model's ability to predict peptide-to-receptor relationships, leveraging training data from ClusPro, PROPEDIA, and PepNN.
 It's particularly useful for researchers and practitioners in bioinformatics, drug discovery, and related fields, aiming to understand or predict peptide-receptor interactions.
 
 ## How to Use