flair_grc_bert_ner / README.md
UGARIT's picture
Update README.md
971ad46 verified
metadata
language:
  - grc
tags:
  - flair
  - token-classification
widget:
  - text: >-
      ταῦτα εἴπας ὁ Ἀλέξανδρος παρίζει Πέρσῃ ἀνδρὶ ἄνδρα Μακεδόνα ὡς γυναῖκα τῷ
      λόγῳ · οἳ δέ , ἐπείτε σφέων οἱ Πέρσαι ψαύειν ἐπειρῶντο , διεργάζοντο
      αὐτούς .

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

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:

@unpublished{yousefetal22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}
}