Instructions to use SI2M-Lab/DarijaBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SI2M-Lab/DarijaBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SI2M-Lab/DarijaBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SI2M-Lab/DarijaBERT") model = AutoModelForMaskedLM.from_pretrained("SI2M-Lab/DarijaBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Add Moroccan Arabic (Darija) tag to metadata.
Verified signaturef7eae86
-
Update README.md
9fa9761
-
Update README.md
f88ae61
-
Update README.md
2c1f411
-
Update README.md
ad385e8 -
Update README.md
149181b -
Update README.md
e71dd13 -
Update README.md
331e57f -
Upload tokenizer_config.json
99311f6 -
Delete tokenizer_config.json
6fb16b3 -
Upload config.json
f43c2fc -
Upload pytorch_model.bin with git-lfs
f7c1f3c -
Delete config.json
4600a8f -
Delete pytorch_model.bin
8ed9634 -
add tokenizer
8b5b5b2 -
add model
ac5c08b -
Delete vocab.txt
1a686bc -
Delete tokenizer_config.json
e9d8d45 -
Delete tokenizer.json
5ed0fba -
Delete special_tokens_map.json
606e500 -
Delete pytorch_model.bin
979b2f6 -
Delete config.json
7d1bb17 -
Create README.md
55fc5d7 -
add tokenizer
6de7b49 -
add model
e835c34 -
initial commit
b9b1c21