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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f63f7109cefddaf582725f7c3d0d72108fcdfd69dec09485460e8b5501294f1
- Size of remote file:
- 1.77 MB
- SHA256:
- dd031e34b8b755034ba803ffcd86346d816539a1c0012d39f0d2fc7b23494a1e
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