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:
- 22264b6035a5088c706a094498d2d47858afdd4e485754e078a6fe738e55530a
- Size of remote file:
- 1.01 MB
- SHA256:
- 2df4040a57bf7c953604b00b1709b75d3e9f12bb8cac451c4ff1d04346e687e7
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