Arabic Flair + fastText Part-of-Speech tagging Model (Egyptian and Levant)

Pretrained Part-of-Speech tagging model built on a joint corpus written in Egyptian and Levantine (Jordanian, Lebanese, Palestinian, Syrian) dialects with code-switching of Egyptian Arabic and English. The model is trained using Flair (forward+backward)and fastText embeddings.

Pretraining Corpora:

This sequence labeling model was pretrained on three corpora jointly:

Usage

from flair.data import Sentence
from flair.models import SequenceTagger
  
tagger = SequenceTagger.load("megantosh/flair-arabic-dialects-codeswitch-egy-lev")
sentence = Sentence('عمرو عادلي أستاذ للاقتصاد السياسي المساعد في الجامعة الأمريكية  بالقاهرة .')
tagger.predict(sentence)
for entity in sentence.get_spans('pos'):
    print(entity)

Due to the right-to-left in left-to-right context, some formatting errors might occur. and your code might appear like this , (link accessed on 2020-10-27)

Scores & Tagset

• F-score (micro): 0.8974
• F-score (macro): 0.5188
• Accuracy (incl. no class): 0.901

Expand details below to show class scores for each tag. Note that tag compounds (a tag made for multiple agglutinated parts of speech) are considered as separate ones.

Citation

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

@unpublished{MMHU21
author = "M. Megahed",
title = "Sequence Labeling Architectures in Diglossia",
year = {2021},
doi = "10.13140/RG.2.2.34961.10084"
url = {https://www.researchgate.net/publication/358956953_Sequence_Labeling_Architectures_in_Diglossia_-_a_case_study_of_Arabic_and_its_dialects}
}