Instructions to use hf-tiny-model-private/tiny-random-MarkupLMModel 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-MarkupLMModel 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-MarkupLMModel")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 96c447c4c795ef435ccba9dac45110f16854d304a60bba8ff63a2be88ddaedf3
- Size of remote file:
- 6.97 MB
- SHA256:
- 401444ec72903ac1067027c75d4a6f70fdbb2b8cd1a8f9594e4641b36d04be34
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