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:
- 4a258311607c26bc1f5342457e1b71f5e380ce61d1071ebf3d25870a7d9d4f12
- Size of remote file:
- 3.45 kB
- SHA256:
- 6f68cac1d614badca6f38666f178e2257c909e91a6fdc38871f9fdcee9f3e1c6
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