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:
- 4ec232ce34e705ae840c1cb71f99cb2a4a84c9bce635f70c39b426a47cc9ef80
- Size of remote file:
- 349 kB
- SHA256:
- d76a4123b8c35b045b017c744f417b601b3cf2fa18f90020207eb36270276621
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.