Instructions to use Mahmoud22/MLArabic-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mahmoud22/MLArabic-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Mahmoud22/MLArabic-model")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Mahmoud22/MLArabic-model") model = AutoModelForMaskedLM.from_pretrained("Mahmoud22/MLArabic-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 07a57c80ac04af1d4990db021537337860c2a387c1e79473ab3744102daf0ff7
- Size of remote file:
- 541 MB
- SHA256:
- e03a32b3d87c585bc69bba0bb7c624f6e8bb1eeb4b1dc2e19e4bb7b5281bec4d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.