language:
- grc
base_model:
- pranaydeeps/Ancient-Greek-BERT
tags:
- token-classification
inference:
parameters:
aggregation_strategy: first
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
Data
We trained the models on available annotated corpora in Ancient Greek. There are only two sizeable annotated datasets in Ancient Greek, which are currently un- der release: the first one by Berti 2023, consists of a fully annotated text of Athenaeus’ Deipnosophists, developed in the context of the Digital Athenaeus project. The second one by Foka et al. 2020, is a fully annotated text of Pausanias’ Periegesis Hellados, developed in the context of the Digital Periegesis project. In addition, we used smaller corpora annotated by students and scholars on Recogito: the Odyssey annotated by Kemp 2021; a mixed corpus including excerpts from the Library attributed to Apollodorus and from Strabo’s Geography, annotated by Chiara Palladino; Book 1 of Xenophon’s Anabasis, created by Thomas Visser; and Demos- thenes’ Against Neaira, created by Rachel Milio.
Training Dataset
| Person | Location | NORP | MISC | |
|---|---|---|---|---|
| Odyssey | 2.469 | 698 | 0 | 0 |
| Deipnosophists | 14.921 | 2.699 | 5.110 | 3.060 |
| Pausanias | 10.205 | 8.670 | 4.972 | 0 |
| Other Datasets | 3.283 | 2.040 | 1.089 | 0 |
| Total | 30.878 | 14.107 | 11.171 | 3.060 |
Validation Dataset
| Person | Location | NORP | MISC | |
|---|---|---|---|---|
| Xenophon | 1.190 | 796 | 857 | 0 |
Results
| Class | Metric | Test | Validation |
|---|---|---|---|
| LOC | precision | 82.92% | 87.10% |
| recall | 81.30% | 87.10% | |
| f1 | 82.11% | 87.10% | |
| MISC | precision | 80.43% | 0 |
| recall | 70.04% | 0 | |
| f1 | 74.87% | 0 | |
| NORP | precision | 87.10% | 92.82% |
| recall | 90.81% | 93.42% | |
| f1 | 88.92% | 93.12% | |
| PER | precision | 92.61% | 95.52% |
| recall | 92.94% | 95.21% | |
| f1 | 92.77% | 95.37% | |
| Overall | precision | 88.92% | 92.63% |
| recall | 88.82% | 92.79% | |
| f1 | 88.87% | 92.71% | |
| Accuracy | 97.28% | 98.42% |
Usage
This colab notebook contains the necessary code to use the model.
from transformers import pipeline
# create pipeline for NER
ner = pipeline('ner', model="UGARIT/grc-ner-bert", aggregation_strategy = 'first')
ner("ταῦτα εἴπας ὁ Ἀλέξανδρος παρίζει Πέρσῃ ἀνδρὶ ἄνδρα Μακεδόνα ὡς γυναῖκα τῷ λόγῳ · οἳ δέ , ἐπείτε σφέων οἱ Πέρσαι ψαύειν ἐπειρῶντο , διεργάζοντο αὐτούς .")
Output
[{'entity_group': 'PER',
'score': 0.9999349,
'word': 'αλεξανδρος',
'start': 14,
'end': 24},
{'entity_group': 'NORP',
'score': 0.9369563,
'word': 'περση',
'start': 33,
'end': 38},
{'entity_group': 'NORP',
'score': 0.60742134,
'word': 'μακεδονα',
'start': 51,
'end': 59},
{'entity_group': 'NORP',
'score': 0.9900457,
'word': 'περσαι',
'start': 105,
'end': 111}]