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:
- 655fc242252db4d56794f920ea87d90b203e7b6a07843f5b7cd8cec6c9a66e08
- Size of remote file:
- 921 kB
- SHA256:
- dd60141c54c691ec2f6e0007a5f9b3d15a48dc5c1c8cfc22d3f7e154270d8de9
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