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:
- 85f3b7bf0a6e24502995390f6f1c77623baef384088c01f37fd2d6da90c9d65a
- Size of remote file:
- 16 MB
- SHA256:
- de9ceee819a05e1135836606347f650f30bfba9b0e486c65b471df4329c4a5dc
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.