Instructions to use hf-tiny-model-private/tiny-random-RoCBertForMaskedLM 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-RoCBertForMaskedLM 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-RoCBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 307ded5185c0878ad584ed2e6a96669800686d89c15366457e9b94753247858a
- Size of remote file:
- 3.05 MB
- SHA256:
- 583b42c79e2011494c3bdc5fa6c9f6543c8211b48c2167c5948fcad8c1937b56
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