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:
- 85153cfa8a35ff3f17b516ae25dca0217526efc68eb4c32a98502683160a71ee
- Size of remote file:
- 6.95 MB
- SHA256:
- 04841af507cc1a7783e3ea9af699dd66fea0b0529b75ff2e8471cb430740974c
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