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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5edc961d546d1d87578e7084e0550345b9773823ad9715564880de03ad2ee3ef
- Size of remote file:
- 371 kB
- SHA256:
- be3db88f0e2a3f948be5efe61aaf557bf48a766d4460c1699584186675fb7c50
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