RNAElectra: Single Nucleotide ELECTRA Style Pre training for RNA Representation Learning RNAElectra is a nucleotide resolution RNA language model trained using an ELECTRA style objective for efficient and discriminative representation learning. The model produces contextualized embeddings for RNA sequences and is designed for downstream transcriptomic and regulatory modeling tasks. Model Details Model Type : Transformer based discriminator model Training Objective : ELECTRA style replaced token detection Resolution : Single nucleotide Domain : RNA and transcriptomic sequences Architecture : ModernBERT style backbone adapted for nucleotide sequences RNAElectra focuses on efficient pre training by learning to discriminate corrupted tokens rather than reconstruct them, leading to strong representations with improved training efficiency. Key Features Single nucleotide tokenization Contextual RNA sequence embeddings ELECTRA style discriminative pre training Suitable for RNA function prediction, RBP binding modeling, stability prediction, regulatory element analysis, and downstream fine tuning tasks Usage Basic Feature Extraction Installation Requirements transformers = 5.0.0 torch = 2.1…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy