BRAGD: Constrained Multi Label POS Tagging for Faroese BRAGD is a Faroese POS and morphological tagging model based on ScandiBERT. It predicts a 73 dimensional binary feature vector for each token, covering word class, subcategory, gender, number, case, article, proper noun status, degree, declension, mood, voice, tense, person, and definiteness. This Hugging Face repository contains a fine tuned XLMRobertaForTokenClassification checkpoint with 73 output labels , along with the decoding files constraint mask.json and tag mappings.json . The repository is currently published as a Transformers/XLM RoBERTa safetensors model under Setur/BRAGD . Model Details Model name: BRAGD Repository: Setur/BRAGD Architecture: XLMRobertaForTokenClassification Base model: vesteinn/ScandiBERT Task: Faroese POS + morphological tagging Output format: 73 binary features per token, decoded into BRAGD tags Performance In the accompanying paper, the constrained multi label BRAGD model achieves: 97.5% composite tag accuracy on the Sosialurin BRAGD corpus (10 fold cross validation) 96.2% composite tag accuracy on OOD BRAGD out of domain data These numbers describe the evaluated research setup reported in the…
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