Instructions to use hf-tiny-model-private/tiny-random-SqueezeBertForQuestionAnswering 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-SqueezeBertForQuestionAnswering 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-SqueezeBertForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-SqueezeBertForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c4c50de4689bf44e984c57e27f159fbda2c3252b15047fd35c31f211e665141e
- Size of remote file:
- 347 kB
- SHA256:
- 4ee40af91d753201e78a7370da2e511d1df8f5ba81146b391643cb1670b876b3
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