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 training_args.bin from subhasisj/ar-TAPT-MLM-MiniLM: direct link, hf CLI and curl.
- Browser
- Download file 3.06 kB
-
https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM/resolve/main/training_args.bin
- Command line
-
hf download hf://subhasisj/ar-TAPT-MLM-MiniLM/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/subhasisj/ar-TAPT-MLM-MiniLM/resolve/main/training_args.bin
3.06 kB
- Xet hash:
- 63b6e1c321dee738bf1624fe815a62a83c031fdbbc5d913f531a5e56a743da88
- Size of remote file:
- 3.06 kB
- SHA256:
- 5a9a2ddafd057b768f7851fefe619ac5b4c7173e976daa3a9f48dba1fd1573ec
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