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:
- 845673a8c6ac08341cf452d0c7042d2abb59418e6ea8efffeaf34e01d755eb7e
- Size of remote file:
- 6.97 MB
- SHA256:
- 16307e9da433562637850544d9acc3973d7ec69ba6428bed9349e86ecbdc338d
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