Instructions to use hf-internal-testing/tiny-random-MarkupLMForSequenceClassification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MarkupLMForSequenceClassification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hf-internal-testing/tiny-random-MarkupLMForSequenceClassification")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-MarkupLMForSequenceClassification") model = AutoModelForSequenceClassification.from_pretrained("hf-internal-testing/tiny-random-MarkupLMForSequenceClassification") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 825f5acb183eef36183fca7da2933da87869e433a142f8e65fed95ffb4ec9aa8
- Size of remote file:
- 6.97 MB
- SHA256:
- 0a7d35ec8d1da79740e159bb3cb185df9de630d7a24c139bcc81f2357f3802a1
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