Instructions to use hf-tiny-model-private/tiny-random-ReformerForQuestionAnswering 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-ReformerForQuestionAnswering 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-ReformerForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-ReformerForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-ReformerForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1894f01a9e390a67ef098e62c945fe2308e87e1b026b6e0b3436eabd05ef506
- Size of remote file:
- 363 kB
- SHA256:
- 873e9fd3297f931a620dac77374341f32ca9184d4de27e5d7c109de598bebf3f
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