Instructions to use hf-internal-testing/tiny-random-FNetForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-FNetForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-FNetForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-FNetForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-FNetForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5f31f587bd5e09af5232e414a40b9dbe0a3fe8964b21fb84cb024aee4b04eff4
- Size of remote file:
- 4.23 MB
- SHA256:
- c6e8d9170951dda1c294dfe75e516b4f8caf4b62c4c210266935a83eba247579
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.