Flair-abbr-roberta-pubmed-plos-filtered
This is a stacked model of embeddings from roberta-large, HunFlair pubmed models and character-level language models trained on PLOS, fine-tuning on the PLODv2 filtered dataset. It is released with our LREC-COLING 2024 publication Using character-level models for efficient abbreviation and long-form detection. It achieves the following results on the test set:
Results on abbreviations:
- Precision: 0.8924
- Recall: 0.9375
- F1: 0.9144
Results on long forms:
- Precision: 0.8750
- Recall: 0.9225
- F1: 0.8981
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