Token Classification
Transformers
TensorBoard
Arabic
bert
fill-mask
Named Entity Recognition
Arabic NER
Nested NER
Instructions to use SinaLab/ArabicNER-Wojood with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SinaLab/ArabicNER-Wojood with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="SinaLab/ArabicNER-Wojood")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SinaLab/ArabicNER-Wojood") model = AutoModelForMaskedLM.from_pretrained("SinaLab/ArabicNER-Wojood") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da912abc15fff88c5b3b2c7568fced72b74927daf7648b04de78d6f9cbef68c0
- Size of remote file:
- 541 MB
- SHA256:
- 7295a5bf0e3fec438eb0ad996240a40dfed769fa3c12636925289f5a3969300e
ยท
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.