Instructions to use CAMeL-Lab/bert-base-arabic-camelbert-msa 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-msa 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-msa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa") model = AutoModelForMaskedLM.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa") - Inference
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f8cf674813af639f6a26d0c7af73a0bbb5cdf7d70fd794364cfc372193ef635
- Size of remote file:
- 437 MB
- SHA256:
- 703e3c2d446e2bcbb16d22a2fe045239e77b029a0f87fce9464516aedf94b9a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.