Instructions to use hf-tiny-model-private/tiny-random-LayoutLMModel 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-LayoutLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-LayoutLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-LayoutLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a6d25ea732987a2f79ca563e6e6ae48d2405441f1a73ae2f354ae62ca9d1017d
- Size of remote file:
- 1.01 MB
- SHA256:
- 1e95455730530ce8cb5906d7314b5a9afd458ffa695b5f8e5752df0d44232cd8
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