Update README.md

README.md

@@ -9,11 +9,22 @@ tags:
 - biology
 - protein
 ---
-# Model Card for Model ID
+# Contrastively Learned Attention based Stratified PTM Predictor (CLASPP) a unified PTM prediction model
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+Post-Translational Modifications (PTMs) are a fundamental mechanism for regulating cellular functions and increasing the functional diversity of the proteome. 
+Despite the identification of hundreds of unique PTMs through mass-spectrometry (MS) studies, accurately predicting many PTM types based on sequence data alone 
+remains a significant challenge. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple 
+models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types making it difficult to 
+predict multiple PTM types with a single model. To address this limitation, we present the Contrastively Learned Attention-Based Stratified PTM Predictor (CLASPP), 
+a unified PTM prediction model. CLASPP overcomes data imbalance challenges by leveraging unsupervised clustering-based under-sampling and incorporating a novel 
+contrastive learning framework tailored to PTM data. Drawing inspiration from advancements in image and natural language processing, the CLASPP model employs 
+a multi-stage training strategy and utilizes a high-quality curated training dataset to improve PTM prediction accuracy compared to existing multi-PTM prediction 
+models. Existing PTM prediction models predominantly focus on either single PTM types or employ ensemble methods that combine multiple 
+models to predict different PTM types. This fragmentation is largely driven by the vast imbalance in data availability across PTM types making it difficult to 
+predict multiple PTM types with a single model. To address this limitation, we present the Contrastively Learned Attention-Based Stratified PTM Predictor (CLASPP), 
+a unified PTM prediction model. 
 
 ## Model Details
 
@@ -23,13 +34,13 @@ This modelcard aims to be a base template for new models. It has been generated
 
 
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** [Nathan Gravel]
+- **Funded by [optional]:** [NIH]
+- **Shared by [optional]:** [More Information Neede]
+- **Model type:** [Text classication]
+- **Language(s) (NLP):** [Protein Sequence]
+- **License:** [MIT]
+- **Finetuned from model [optional]:** [ESM-2 150M]
 
 ### Model Sources [optional]