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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9314b7d2cf4c5a5bd7e355ad2a12dc8b3aff8618d74f8f78bc85e102195019ad
- Size of remote file:
- 328 kB
- SHA256:
- 8da2c49b93ae843b9fecf436f70036530d3462ceca27c2a219d83be9441c59ea
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