Instructions to use CAMeL-Lab/bert-base-arabic-camelbert-ca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CAMeL-Lab/bert-base-arabic-camelbert-ca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="CAMeL-Lab/bert-base-arabic-camelbert-ca")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-ca") model = AutoModelForMaskedLM.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-ca", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CAMeL-Lab/bert-base-arabic-camelbert-ca: direct link, hf CLI and curl.
- Browser
- Download file 439 MB
-
https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/resolve/115790ebf2ad9f402e40b20398827f35369206ee/pytorch_model.bin
- Command line
-
hf download hf://CAMeL-Lab/bert-base-arabic-camelbert-ca@115790ebf2ad9f402e40b20398827f35369206ee/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-ca/resolve/115790ebf2ad9f402e40b20398827f35369206ee/pytorch_model.bin
439 MB
- Xet hash:
- 221c04a2581f2aba71107ab4eab965db8c4b068ecd203c4fe5616d8d1d86b9b5
- Size of remote file:
- 439 MB
- SHA256:
- 8c4f14f11b596eda81eae5f5dc42840a8965bb44228bdc6cb236444527a4c729
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.