Token Classification
Transformers
Safetensors
Moroccan Arabic
Arabic
bert
part-of-speech
pos-tagging
moroccan-darija
darija
low-resource-languages
Eval Results (legacy)
Instructions to use TypicaAI/DarijaPOSTagger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TypicaAI/DarijaPOSTagger with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="TypicaAI/DarijaPOSTagger")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("TypicaAI/DarijaPOSTagger") model = AutoModelForTokenClassification.from_pretrained("TypicaAI/DarijaPOSTagger") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e4d383d01bdb2a4d9ae048dd9d891c610b7135cc417f974b9f186b2b90235845
- Size of remote file:
- 588 MB
- SHA256:
- 5aecf7b48f451db5a7826e8a942bec6246c87439e4bab9bff0ea603094e26858
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.