Instructions to use ydshieh/tiny-random-AlbertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ydshieh/tiny-random-AlbertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ydshieh/tiny-random-AlbertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ydshieh/tiny-random-AlbertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("ydshieh/tiny-random-AlbertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 692acf3180b080d0007c5f8d99038f51c776856587fed3047e2598ab58bd8a2c
- Size of remote file:
- 1.08 MB
- SHA256:
- 0e2d9db5e7f1f10d323302f2d53cf75f15d0235ffa987d9e38eadcd461c6b291
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