Profluent - E1 is a faithful implementation of the E1 model by Profluent Bio, released by Synthyra. It is a pre-trained language model focused on protein sequence processing, integrating Hugging Face AutoModel compatibility and providing efficient embedding capabilities, aiming to simplify protein sequence analysis and representation learning in bioinformatics tasks.

Scientific ComputingTransformers

Profluent-E1 is a faithful implementation of the official Profluent E1 model, integrating Hugging Face AutoModel compatibility and focusing on the representation learning of protein sequences. It can generate high-quality protein sequence embeddings, support tasks such as masked language modeling, sequence classification, and token classification, and provides convenient embedding extraction and fine-tuning functions.

Scientific ComputingTransformers

ESM-2 is a protein language model optimized by NVIDIA using the TransformerEngine library based on the original ESM-2 model from Facebook Research. It has the same weights and outputs within the numerical precision range and is suitable for protein sequence analysis tasks.

Scientific ComputingTransformers

ESM-2 is a protein language model optimized by NVIDIA based on TransformerEngine, which can predict the 3D structure of proteins from amino acid sequences. This model is trained with a masked language modeling objective and has faster training and inference speeds on NVIDIA GPUs.

Scientific ComputingTransformers

AbLang2 is a Transformer language model developed by the Oxford Protein Informatics Group specifically for antibody sequences. It can perform antibody sequence analysis, restoration, and representation learning, providing a powerful computational tool for antibody design and research.

Natural Language ProcessingTransformersOther

Intern-S1 is currently the most advanced open-source multimodal reasoning model. It combines powerful general task processing capabilities with excellent performance in a wide range of scientific tasks and can be comparable to leading closed-source commercial models. The model is continuously pre-trained on a 5T token dataset, of which more than 50% is professional scientific data. It has a dynamic word segmenter that can natively understand molecular formulas, protein sequences, and seismic signals.

MultimodalSafetensors

AMPLIFY is an efficient and advanced protein language model focused on protein sequence analysis and prediction tasks.

Scientific ComputingTransformersEnglish

The ESMC Protein Function Predictor is a tool based on the Evolutionary Scale Model (ESM) that can predict protein functions using the Gene Ontology (GO) based on amino acid sequences. This model is pre-trained on the UniRef, MGnify, and Joint Genome Institute databases and fine-tuned on the AmiGO Boost dataset. It can predict the molecular functions, biological processes, and cellular locations of proteins.

Scientific ComputingTensorboard

AMPLIFY is an efficient protein language model optimized using NVIDIA's TransformerEngine library. It can generate residue and protein embeddings, suggest mutations, and distinguish disordered proteins from non - protein sequences. This model maintains the same weights and outputs as the original model within the numerical precision range and can be used for both commercial and non - commercial purposes.

Scientific ComputingTransformersEnglish

ESM-2 is a protein language model optimized by NVIDIA using TransformerEngine. Based on the original ESM-2 model from Facebook Research, it has the same weights and outputs. This model is trained with a masked language modeling objective and is suitable for fine-tuning on various protein sequence analysis tasks.

Scientific ComputingTransformers

Trias is an encoder-decoder language model designed for reverse translating protein sequences into codon sequences, trained on 10 million mRNA coding sequences from 640 vertebrate species.

Scientific ComputingSafetensors

A protein function prediction tool based on the Evolutionary Scale Modeling (ESM), which predicts the molecular function of proteins through amino acid sequences.

Scientific ComputingTransformers

ChatNT is the first multimodal conversational agent with deep understanding of biological sequences (DNA, RNA, proteins), supporting natural language interaction for biological data processing.

MultimodalTransformers

Bacformer is a foundational model for bacterial genomics that models entire bacterial genomes as protein sequences. The model takes a set of proteins sorted by position in the genome as input and computes contextual protein representations through a Transformer to capture protein - protein interactions. It is trained on approximately 1.3 million bacterial genomes and 40,000 complete genomes, containing approximately 3 billion protein sequences.

Scientific ComputingTransformers

Ankh3 is a protein language model based on the T5 architecture. It is pre - trained by jointly optimizing masked language modeling and sequence completion tasks, and is suitable for protein feature extraction and sequence analysis.

Scientific ComputingTransformers

This is an autoregressive protein language model specifically designed for generating paired antibody libraries. The model is pre-trained on unpaired heavy and light chain sequences from the Observed Antibody Space (OAS) and fine-tuned on paired sequences, with good generalization ability.

Scientific ComputingTransformers

This model is fine-tuned based on Bert-Base-Uncased and is used for the multi-label classification task of predicting protein functions based on protein amino acid sequences.

Scientific ComputingTransformers

ProGen2-base is a protein generation model based on the research by Nijkamp et al., supporting protein sequence generation and prediction.

Natural Language ProcessingTransformers

The ultimate tool for codon optimization, capable of converting protein sequences into DNA sequences optimized for target organisms.

Scientific ComputingTransformers

Isoformer is a multimodal biological sequence model capable of processing DNA, RNA, and protein sequences, and predicting gene expression.

Scientific ComputingTransformers