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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d63f6748904cf82a857ee5703926ebd673d5626da8a0596cf6474e5761a490d5
- Size of remote file:
- 59.9 MB
- SHA256:
- 09fffaba578375348a6f16125be0cfea9a4125f6abb84c0af2e5ec95567b9d01
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