Researchers from Allen Institute for AI Developed SPECTER2: A New Scientific Document Embedding Model via a 2-Step Training Process on Large Datasets

The field of scientific document embeddings faces challenges in adaptability and performance, notably within existing models like SPECTER and SciNCL. While effective in specific domains, these models grapple with limitations such as a narrow training data focus on citation prediction tasks. Researchers identified these challenges and set out to create a solution that addresses these […]

The post Researchers from Allen Institute for AI Developed SPECTER2: A New Scientific Document Embedding Model via a 2-Step Training Process on Large Datasets …

adaptability allen allen institute allen institute for ai challenges data datasets document domains editors pick embedding embeddings focus institute large datasets limitations narrow performance process researchers staff tech news training training data