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:
- 87ebbb0ca71520efa633fa1d2bc8b9999f9530e95cc43123c99ba4c1ebede8a0
- Size of remote file:
- 16 MB
- SHA256:
- 9b02f55d110a66badfea49b8f16d49b96cc9b0977a7b2cd7bbb9f2f79de9c5ca
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