Model checkpoint for the manuscript "LLMCPNER: Integrating Large Language Models and Curriculum Learning for Paleontological Named Entity Recognition", currently under review.

LLMCPNER is a span-based named entity recognition model for paleontological literature. It combines SciBERT, multi-model voting, and confidence-weighted curriculum learning.

The model recognizes seven entity types: taxa, location, section, strata, lithology, facies, and age.

The model was evaluated on a manually corrected test set containing 188 texts and 1,348 entities.

Strict matching requires exact entity boundaries and type. Partial matching requires at least 50% boundary overlap and the correct entity type.

This checkpoint uses a custom span-classification architecture and is not a drop-in AutoModel.from_pretrained() model. The implementation and test set are available in the associated code repository:

The model is intended for research on named entity recognition in English paleontological literature. Performance outside this domain has not been established. Predictions should be reviewed before use in scientific databases or downstream knowledge resources.