| --- |
| 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. |
|
|
|
|
