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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7806350dd2dd29c2360ed031c648e0267b5df1edd53814ddfafa8bff212a70f7
- Size of remote file:
- 890 kB
- SHA256:
- 07b5aac9d80582a191cc9a31a2c5a9c9095ded8e2bd35b40c19360fecaa9b2a1
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