Instructions to use hf-internal-testing/tiny-random-AlbertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-AlbertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-internal-testing/tiny-random-AlbertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-AlbertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-internal-testing/tiny-random-AlbertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e16df601f97962b9d06896161c48d2d10c41ed89d0591e96f2b9936d17fa5ba
- Size of remote file:
- 16 MB
- SHA256:
- 8603f9af42c9e1b537743cf340e657eedb77458bc3175584b202406af778121a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.