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UGARIT/
flair_grc_bert_ner

Name: flair_grc_bert_ner
Availability: InStock

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Named Entity Recognition for Ancient Greek

Pretrained NER tagging model for ancient Greek

Scores & Tagset

Training:

	Precision	Recall	F1-score	Support
PER	91.24%	94.45%	92.82%	2127
MISC	80.92%	83.17%	82.03%	933
LOC	86.86%	78.35%	82.38%	388

Evaluation

	Precision	Recall	F1-score	Support
PER	92.00%	86.79%	89.32%	124
MISC	96.43%	87.10%	91.53%	159
LOC	80.00%	84.85%	82.35%	66

F-score (micro) 0.8878
F-score (macro) 0.8574
Accuracy 0.8324

Usage

python

from flair.data import Sentence
from flair.models import SequenceTagger
  
tagger = SequenceTagger.load("UGARIT/flair_grc_bert_ner")
sentence = Sentence('ταῦτα εἴπας ὁ Ἀλέξανδρος παρίζει Πέρσῃ ἀνδρὶ ἄνδρα Μακεδόνα ὡς γυναῖκα τῷ λόγῳ · οἳ δέ , ἐπείτε σφέων οἱ Πέρσαι ψαύειν ἐπειρῶντο , διεργάζοντο αὐτούς .')
tagger.predict(sentence)
for entity in sentence.get_spans('ner'):
    print(entity)

Citation

if you use this model, please consider citing this work:

latex

@unpublished{yousefetal22
author = "Yousef, Tariq and Palladino, Chiara and Jänicke, Stefan",
title = "Transformer-Based Named Entity Recognition for Ancient Greek",
year = {2022},
month = {11},
doi = "10.13140/RG.2.2.34846.61761"
url = {https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects}
}