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
# 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")Quick Links
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-MarkupLMForQuestionAnswering")