ArabicNER-Wojood
Collection
4 items โข Updated โข 1
Token Classification
Transformers
TensorBoard
Arabic
bert
fill-mask
Named Entity Recognition
Arabic NER
Nested NER
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("SinaLab/ArabicNER-Wojood")
model = AutoModelForMaskedLM.from_pretrained("SinaLab/ArabicNER-Wojood")Quick Links
Wojood - Nested/Flat Arabic NER Models
Wojood is a corpus for Arabic nested Named Entity Recognition (NER). Nested entities occur when one entity mention is embedded inside another entity mention. 550K tokens (MSA and dialect) This repo contains the source-code to train Wojood nested NER.
Online Demo You can try our model using the demo link below
https://sina.birzeit.edu/wojood/
https://arxiv.org/abs/2205.09651
https://huggingface.co/aubmindlab/bert-base-arabertv2/tree/main
Models
- Nested NER (main branch), with micro-F1 score of 0.909551
- Flat NER (flat branch), with micro-F1 score 0.883847
Google Colab Notebooks
You can test our model using our Google Colab notebooks
- Train flat NER: https://gist.github.com/mohammedkhalilia/72c3261734d7715094089bdf4de74b4a
- Evaluate your model using flat NER model: https://gist.github.com/mohammedkhalilia/c807eb1ccb15416b187c32a362001665
- Train nested NER: https://gist.github.com/mohammedkhalilia/a4d83d4e43682d1efcdf299d41beb3da
- Evaluate your data using nested NER model: https://gist.github.com/mohammedkhalilia/9134510aa2684464f57de7934c97138b
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SinaLab/ArabicNER-Wojood")