Instructions to use hf-internal-testing/tiny-random-MarkupLMForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MarkupLMForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-MarkupLMForQuestionAnswering")# Load model directly from transformers import AutoProcessor, AutoModelForQuestionAnswering processor = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-MarkupLMForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-MarkupLMForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c3566dd6cb6fbbe80ae2ce4968b8a732f17d14aaf3d1243411f4aaea6e8352e4
- Size of remote file:
- 6.97 MB
- SHA256:
- 8dff10685b2816ac5b2963b940d6ae26683b35f4c49ddebe6b0291a58cc44bee
路
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