Add LLMCPNER model card and metadata

README.md

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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.
+

label_mapping.json

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+{
+  "label2id": {
+    "O": 0,
+    "taxa": 1,
+    "location": 2,
+    "section": 3,
+    "strata": 4,
+    "lithology": 5,
+    "facies": 6,
+    "age": 7
+  },
+  "id2label": {
+    "0": "O",
+    "1": "taxa",
+    "2": "location",
+    "3": "section",
+    "4": "strata",
+    "5": "lithology",
+    "6": "facies",
+    "7": "age"
+  }
+}
+

training_config.json

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+{
+  "method": "positive_confidence_curriculum",
+  "base_model": "allenai/scibert_scivocab_uncased",
+  "seed": 13,
+  "batch_size": 8,
+  "learning_rate": 2e-5,
+  "prediction_threshold": 0.3,
+  "num_voters": 3,
+  "voter_sample_ratio": 0.6,
+  "voter_epochs": 1,
+  "minimum_positive_weight": 0.7,
+  "curriculum_percentiles": [30, 0, 0],
+  "stage_epochs": [1, 0, 0],
+  "training_samples": 25460,
+  "training_entities": 159024,
+  "test_samples": 188,
+  "test_entities": 1348
+}