Instructions to use hf-internal-testing/tiny-random-MarkupLMForTokenClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MarkupLMForTokenClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="hf-internal-testing/tiny-random-MarkupLMForTokenClassification")# Load model directly from transformers import AutoProcessor, AutoModelForTokenClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-MarkupLMForTokenClassification") model = AutoModelForTokenClassification.from_pretrained("hf-internal-testing/tiny-random-MarkupLMForTokenClassification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- efa5ecc8c390ffc69d0d2df9d0c2172f5fe97abe9b34c092ee8e293487b20ea5
- Size of remote file:
- 6.97 MB
- SHA256:
- afe9722d85c14ac081b35c0d25f69f77fc0857269a7bd0ac67da1acc5447157a
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