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-sosialurin
• Repository: Setur/BRAGD-sosialurin
• 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 paper, not this release model trained on the combined data.

Training Data

The model is based on the BRAGD annotation scheme for Faroese.

Sosialurin-BRAGD

• 6,099 sentences
• about 123k tokens
• 651 unique tags
• each tag decomposed into 73 binary features

The release model Setur/BRAGD was trained on both Sosialurin-BRAGD and OOD-BRAGD. This model was only trained on Sosialurin-BRAGD.

Label Structure

The 73 output dimensions are organized as follows:

• 0–14: Word class
• 15–29: Subcategory
• 30–33: Gender
• 34–36: Number
• 37–41: Case
• 42–43: Article
• 44–45: Proper noun
• 46–50: Degree
• 51–53: Declension
• 54–60: Mood
• 61–63: Voice
• 64–66: Tense
• 67–70: Person
• 71–72: Definiteness

Using the Model

This model predicts feature vectors, not directly formatted BRAGD tags. To get the final BRAGD tag and readable features, you should:

1. run the model,
2. select the most likely word class,
3. activate only the valid feature groups for that word class using constraint_mask.json,
4. map the resulting feature vector back to a BRAGD tag using tag_mappings.json.

Install requirements

pip install numpy torch "transformers==4.57.1" sentencepiece huggingface_hub

Python example

import json
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import XLMRobertaTokenizerFast, XLMRobertaForTokenClassification
model_name = "Setur/BRAGD"
tokenizer = XLMRobertaTokenizerFast.from_pretrained(model_name)
model = XLMRobertaForTokenClassification.from_pretrained(model_name)
model.eval()
# Download decoding assets
constraint_mask_path = hf_hub_download(model_name, "constraint_mask.json")
tag_mappings_path = hf_hub_download(model_name, "tag_mappings.json")
with open(constraint_mask_path, "r", encoding="utf-8") as f:
    raw_mask = json.load(f)
constraint_mask = {int(k): [tuple(x) for x in v] for k, v in raw_mask.items()}
with open(tag_mappings_path, "r", encoding="utf-8") as f:
    raw_map = json.load(f)
features_to_tag = {tuple(map(int, k.split(","))): v for k, v in raw_map.items()}
WORD_CLASS_NAMES = {
    0: "Noun",
    1: "Adjective",
    2: "Pronoun",
    3: "Number",
    4: "Verb",
    5: "Participle",
    6: "Adverb",
    7: "Conjunction",
    8: "Foreign",
    9: "Unanalyzed",
    10: "Abbreviation",
    11: "Web",
    12: "Punctuation",
    13: "Symbol",
    14: "Article",
}
INTERVAL_NAMES = {
    (15, 29): "subcategory",
    (30, 33): "gender",
    (34, 36): "number",
    (37, 41): "case",
    (42, 43): "article",
    (44, 45): "proper_noun",
    (46, 50): "degree",
    (51, 53): "declension",
    (54, 60): "mood",
    (61, 63): "voice",
    (64, 66): "tense",
    (67, 70): "person",
    (71, 72): "definiteness",
}
FEATURE_COLUMNS = [
    "S", "A", "P", "N", "V", "L", "D", "C", "F", "X", "T", "W", "K", "M", "R",
    "D", "B", "E", "I", "P", "Q", "N", "G", "R", "X", "S", "C", "O", "T", "s",
    "M", "F", "N", "g",
    "S", "P", "n",
    "N", "A", "D", "G", "c",
    "A", "a",
    "P", "r",
    "P", "C", "S", "A", "d",
    "S", "W", "e",
    "I", "M", "N", "S", "P", "E", "U",
    "A", "M", "v",
    "P", "A", "t",
    "1", "2", "3", "p",
    "D", "I",
]
def decode_token(logits):
    pred = np.zeros(logits.shape[0], dtype=int)
    # predict word class
    wc = int(np.argmax(logits[:15]))
    pred[wc] = 1
    # predict only valid feature groups for this word class
    for start, end in constraint_mask.get(wc, []):
        group = logits[start:end+1]
        pred[start + int(np.argmax(group))] = 1
    tag = features_to_tag.get(tuple(pred.tolist()), None)
    features = {"word_class": WORD_CLASS_NAMES.get(wc, str(wc))}
    for (start, end), name in INTERVAL_NAMES.items():
        group = pred[start:end+1]
        active = np.where(group == 1)[0]
        if len(active) == 1:
            features[name] = FEATURE_COLUMNS[start + active[0]]
    return tag, features
text = "Hetta er eitt føroyskt dømi"
words = text.split()
enc = tokenizer(
    [words],
    is_split_into_words=True,
    return_tensors="pt",
    padding=True,
    truncation=True,
)
with torch.no_grad():
    logits = model(**enc).logits[0]
word_ids = enc.word_ids(batch_index=0)
seen = set()
for i, word_id in enumerate(word_ids):
    if word_id is None or word_id in seen:
        continue
    seen.add(word_id)
    tag, features = decode_token(logits[i].cpu().numpy())
    print(f"{words[word_id]:15s} {str(tag):10s} {features}")

Example output

Hetta           PDNpSN     {'word_class': 'Pronoun', 'subcategory': 'D', 'gender': 'N', 'number': 'S', 'case': 'N', 'person': 'p'}
er              VNAPS3     {'word_class': 'Verb', 'number': 'S', 'mood': 'N', 'voice': 'A', 'tense': 'P', 'person': '3'}
eitt            RNSNI      {'word_class': 'Article', 'gender': 'N', 'number': 'S', 'case': 'N', 'definiteness': 'I'}
føroyskt        APSNSN     {'word_class': 'Adjective', 'gender': 'N', 'number': 'S', 'case': 'N', 'degree': 'P', 'declension': 'S'}
dømi            SNSNar     {'word_class': 'Noun', 'gender': 'N', 'number': 'S', 'case': 'N', 'article': 'a', 'proper_noun': 'r'}

Files in this Repository

This model repository contains model and decoding files, including:

• model.safetensors
• config.json
• tokenizer files
• constraint_mask.json
• tag_mappings.json :contentReference[oaicite:2]{index=2}

Further Resources

For full training code, data preparation, and paper-related experiments, see the GitHub repository:

https://github.com/Maltoknidepilin/BRAGD.git

Citation

@inproceedings{simonsen2026bragd,
    title={{BRAGD}: Constrained Multi-Label {POS} Tagging for {F}aroese},
    author={Simonsen, Annika and Scalvini, Barbara and Johannesen, Uni and Debess, Iben Nyholm and Einarsson, Hafsteinn and Sn{\ae}bjarnarson, V{\'e}steinn},
    booktitle={Proceedings of the 2026 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2026)},
    year={2026}
}

Authors

Annika Simonsen, Barbara Scalvini, Uni Johannesen, Iben Nyholm Debess, Hafsteinn Einarsson, and Vésteinn Snæbjarnarson

License

This repository is marked as CC BY 4.0 on Hugging Face. :contentReference[oaicite:3]{index=3}