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library_name: pytorch
license: mit
base_model: allenai/scibert_scivocab_uncased
tags:
- named-entity-recognition
- paleontology
- scibert
- curriculum-learning
- weak-supervision
---
# LLMCPNER
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.
## Entity types
The model recognizes seven entity types: `taxa`, `location`, `section`,
`strata`, `lithology`, `facies`, and `age`.
## Evaluation
The model was evaluated on a manually corrected test set containing 188 texts
and 1,348 entities.
| Matching criterion | Precision | Recall | F1 |
| --- | ---: | ---: | ---: |
| Strict | 88.19 | 87.54 | 87.86 |
| Partial | 91.26 | 90.58 | 90.92 |
Strict matching requires exact entity boundaries and type. Partial matching
requires at least 50% boundary overlap and the correct entity type.
## Files
- `model.pt`: PyTorch state dictionary of the final model.
- `label_mapping.json`: entity label-to-ID and ID-to-label mappings.
- `training_config.json`: core settings recorded for the reported run.
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:
https://github.com/goodXHD/LLMCPNER-Integrating-Large-Language-Models-and-Curriculum-Learning-for-Paleontological-Named-Entity
## Intended use
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.
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