Update README.md

README.md

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 <!-- Provide a quick summary of what the model is/does. -->
 
-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. 
+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.