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:
- 455ec8d74c0bb1d2b4d62ace9b0e4e145e2eeaee508e215ade6968ce41724ac3
- Size of remote file:
- 909 kB
- SHA256:
- cfee9b55aa9244020400c3d32b1fbfdda9d1280c47022fb8556f02cdf27bcc13
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