Instructions to use hf-tiny-model-private/tiny-random-NezhaForQuestionAnswering 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-NezhaForQuestionAnswering 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-NezhaForQuestionAnswering")# Load model directly from transformers import AutoModelForQuestionAnswering model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-NezhaForQuestionAnswering", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4edc6c0b6b543a96392d1b7bda63bf910af6a530939224b95d597883694156eb
- Size of remote file:
- 2.91 MB
- SHA256:
- 517ce24e902533089fd3ce1534ecd67e1c08e497f8286e0c5a9c7789a2225181
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