Flair-abbr-roberta-pubmed-plos-unfiltered

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 unfiltered 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: 0.8977
• Recall: 0.9351
• F1: 0.9160

Results on long forms:

• Precision: 0.8726
• Recall: 0.9260
• F1: 0.8985