Instructions to use hf-tiny-model-private/tiny-random-DebertaV2ForMaskedLM 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-DebertaV2ForMaskedLM 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-DebertaV2ForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-DebertaV2ForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 795ce9c9d0102101611be386011725c6e438367fc35d83fa99fa6f94455afeb4
- Size of remote file:
- 17.1 MB
- SHA256:
- fcb3beef729b684ede2ad8a608943aa4aeddbe92f5b877ec674ed584a4c50843
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