Instructions to use messalti/MagharibiBERT-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use messalti/MagharibiBERT-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="messalti/MagharibiBERT-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("messalti/MagharibiBERT-v1") model = AutoModelForMaskedLM.from_pretrained("messalti/MagharibiBERT-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 957a439f246ef257a499d8fe50665f2e7b565e44e824815e305c86807443bda8
- Size of remote file:
- 652 MB
- SHA256:
- 921890f87b89ea3322992e2854e82c3670abe5e3fc773688a78dad60b3fe33c4
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