Instructions to use hf-internal-testing/tiny-random-LayoutLMModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LayoutLMModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-LayoutLMModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LayoutLMModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8d21f75762c7af37edc3bd89d6fb7797e90edf4a2ca634275acb5f651d0088b
- Size of remote file:
- 977 kB
- SHA256:
- 76ed65a5bd941cd7688310a4535ad8f9790590ffee497b894dab7ff891b16f2e
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