Instructions to use hf-tiny-model-private/tiny-random-LukeForMaskedLM 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-LukeForMaskedLM 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-LukeForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LukeForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-LukeForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c3fe5052e5432583c82bd5edc971ab12767ae20ccbbfa402e0cc17c56ddc335
- Size of remote file:
- 7.02 MB
- SHA256:
- f5fae3178beb66032b7d546e399d604048bedb8ca95af3629b54f2e29128d092
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.