Instructions to use hf-tiny-model-private/tiny-random-MarkupLMForQuestionAnswering 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-MarkupLMForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-MarkupLMForQuestionAnswering")# Load model directly from transformers import AutoProcessor, AutoModelForQuestionAnswering processor = AutoProcessor.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-MarkupLMForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da96ef0713f2ea27c5670d16062dd285387c08435911e3c06248545af7b56b2a
- Size of remote file:
- 6.94 MB
- SHA256:
- b45300155d63bc44336b81ea774be2db60455c72783bb9b6ada64c254697aca6
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