Instructions to use subhasisj/ar-TAPT-MLM-MiniLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhasisj/ar-TAPT-MLM-MiniLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="subhasisj/ar-TAPT-MLM-MiniLM")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("subhasisj/ar-TAPT-MLM-MiniLM") model = AutoModelForMaskedLM.from_pretrained("subhasisj/ar-TAPT-MLM-MiniLM", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from subhasisj/ar-TAPT-MLM-MiniLM: direct link, hf CLI and curl.
- Browser
- Download file 472 MB
-
https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://subhasisj/ar-TAPT-MLM-MiniLM/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM/resolve/main/pytorch_model.bin
472 MB
- Xet hash:
- effdd33890f6bc0e2faedd9066d0fe40686e3d661e48811f54b35ccc0db8619f
- Size of remote file:
- 472 MB
- SHA256:
- 0cf828a63c09f299dd51098dfea3eb08468303d17a2832755cd1ac9b4338ef80
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