AI solutions have revolutionized the understanding of protein sequences through large language models (LLMs), bridging the gap between protein sequences and natural language. To address challenges in training and evaluating LLMs for protein comprehension, researchers from top institutions have introduced the ProteinLMDataset and ProteinLMBench. The ProteinLMDataset contains 17.46 billion tokens for self-supervised pretraining and 893K instructions for supervised fine-tuning. This comprehensive approach aims to enhance LLMs' ability to understand and generate accurate protein knowledge by bridging the gap in integrating protein sequences and textual content. The dataset is divided into self-supervised and supervised components, providing over 10 billion tokens for self-supervised pretraining and 893,000 instructions across seven segments for supervised fine-tuning. This ensures comprehensive representation, filtering, and tokenization for effective training and evaluation of LLMs in protein science. This work showcases the potential to transform biological research and applications, as the InternLM2-7B model trained on this dataset surpasses GPT-4 in protein comprehension tasks. For businesses looking to evolve and stay competitive with AI, practical AI solutions like the AI Sales Bot from itinai.com/aisalesbot can automate customer engagement 24/7 and redefine sales processes and customer engagement. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com and stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
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