Instructions to use subhasisj/Ar-Mulitlingula-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/Ar-Mulitlingula-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/Ar-Mulitlingula-MiniLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/Ar-Mulitlingula-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/Ar-Mulitlingula-MiniLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload pytorch_model.bin with git-lfs
Browse files- pytorch_model.bin +3 -0
pytorch_model.bin
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