Instructions to use hf-tiny-model-private/tiny-random-MarkupLMForSequenceClassification 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-MarkupLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-tiny-model-private/tiny-random-MarkupLMForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8bdcf322c439de8ed07dab0bd5ae0fff7c39a6391b7e9f11334e1f9d81131064
- Size of remote file:
- 6.97 MB
- SHA256:
- ba179569c6fe05b9375df7a2c03932afb6de1be93dc8b23a7108503f54d57a26
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