Instructions to use hf-tiny-model-private/tiny-random-DebertaForMaskedLM 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-DebertaForMaskedLM 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-DebertaForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-DebertaForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39c114c9c4edcbb9ceaed5e755c9f9b03671bdd4bae32133a47df7a0735d1769
- Size of remote file:
- 355 kB
- SHA256:
- 0d20c10f52d3c6cbfb6f48296f727ef502c30491860815fd264b1805db26c3b8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.