Instructions to use hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering 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-LongformerForQuestionAnswering 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-LongformerForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-LongformerForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 05c269ebd7bf86947e92a2b63478efa826bc4d21a8da898fd7cf04261e376fd4
- Size of remote file:
- 412 kB
- SHA256:
- fa118b38641bcb3b93ccf1676ebe3b601741f0c6b2b3714297aa1f3779958aba
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.