Instructions to use hf-tiny-model-private/tiny-random-AlbertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-AlbertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-AlbertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-AlbertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42f9c540450375b7b2ac014354f17e2c6514302353443f8ec3839baef859584b
- Size of remote file:
- 31.9 MB
- SHA256:
- 3a6ad7facf7717c81885bd939c3334b6839a827731da735c72be7057a454999f
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