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UGARIT/flair_grc_multi_ner
flair_grc_multi_ner is a token classification model from UGARIT. Use it when you need labels on individual words, such as names. It is set up for flair.
Pretrained NER tagging model for ancient Greek
Downloads · 30 days
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From the Hugging Face model README
Pretrained NER tagging model for ancient Greek
| Precision | Recall | F1-score | Support | |
|---|---|---|---|---|
| PER | 93.39% | 96.33% | 94.84% | 2127 |
| MISC | 84.69% | 92.50% | 88.42% | 933 |
| LOC | 89.55% | 77.32% | 82.99% | 388 |
| Precision | Recall | F1-score | Support | |
|---|---|---|---|---|
| PER | 90.48% | 91.94% | 91.20% | 124 |
| MISC | 89.29% | 94.34% | 91.74% | 159 |
| LOC | 82.69% | 65.15% | 72.88% | 66 |
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)
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@unpublished{yousefetal22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/365131651_Transformer-Based_Named_Entity_Recognition_for_Ancient_Greek}
}