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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SinaLab/ArabicNER-Wojood") model = AutoModelForMaskedLM.from_pretrained("SinaLab/ArabicNER-Wojood", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SinaLab/ArabicNER-Wojood: direct link, hf CLI and curl.
- Browser
- Download file 541 MB
-
https://huggingface.co/SinaLab/ArabicNER-Wojood/resolve/refs%2Fpr%2F2/flax_model.msgpack
- Command line
-
hf download hf://SinaLab/ArabicNER-Wojood@refs/pr/2/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SinaLab/ArabicNER-Wojood/resolve/refs%2Fpr%2F2/flax_model.msgpack
541 MB
- Xet hash:
- da912abc15fff88c5b3b2c7568fced72b74927daf7648b04de78d6f9cbef68c0
- Size of remote file:
- 541 MB
- SHA256:
- 7295a5bf0e3fec438eb0ad996240a40dfed769fa3c12636925289f5a3969300e
ยท
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