Instructions to use hf-internal-testing/tiny-random-LayoutLMv2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-LayoutLMv2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-LayoutLMv2Model")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-LayoutLMv2Model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d649b1862703169fd9bc8724fc9c463e9ef4b797a83b3b56da5321de131b1918
- Size of remote file:
- 59.9 MB
- SHA256:
- 39685feb5dbb1bd4310387d524c73d9dfb2bc02e3215a2cf375fda62aabf9125
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