Instructions to use hf-tiny-model-private/tiny-random-LEDForQuestionAnswering 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-LEDForQuestionAnswering 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-LEDForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LEDForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LEDForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b525c56497f2f902ca6c48b0867d8da19725d3cf738c83549d9e44ade9a27e8d
- Size of remote file:
- 1.23 MB
- SHA256:
- 7ff713d5d498b2b81c1936245bb76cfcae54a802f7ebbc4076bdcd9d2ca596bd
路
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