Instructions to use hf-tiny-model-private/tiny-random-LayoutLMForMaskedLM 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-LayoutLMForMaskedLM 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-LayoutLMForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0d4952e7f484ce1bc0e3f8bc6f72c8ca2fe65bace69061ba621bcb99aa0e11f
- Size of remote file:
- 900 kB
- SHA256:
- a8f26fe32ed223a2b8cd53a6e043406a591a57dfe90fc47284687ef6801b3b68
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