Instructions to use hf-tiny-model-private/tiny-random-MarkupLMForTokenClassification 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-MarkupLMForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-tiny-model-private/tiny-random-MarkupLMForTokenClassification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMForTokenClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 408e31652f677f7f4c254f990bbf519fd0588c8c3ef472ad11aa1e7b0110df39
- Size of remote file:
- 6.94 MB
- SHA256:
- f2d10f6c094dd75c97dca1833fa7115677c6335a2c1ed33f99f3e32c2ebe1f0d
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