Instructions to use hf-tiny-model-private/tiny-random-XmodForMaskedLM 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-XmodForMaskedLM 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-XmodForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XmodForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-XmodForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08cc8765fb106ddc80e68016a4f3cfc91476d82b02123e33cab8d6a9c18ad9cf
- Size of remote file:
- 33.2 MB
- SHA256:
- 10b39cc461215ac187e24e1c9b8f9bc7b9f0dee1767b9b8e5feb6d032c748ed1
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