Instructions to use hf-internal-testing/tiny-random-T5ForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-T5ForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-T5ForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-T5ForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-T5ForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 406a36dd13469e0e8ec9499c3ec694cc6d5847adae27c4cc93ce4df20cbd0aac
- Size of remote file:
- 4.49 MB
- SHA256:
- 2c65bb57b8b822ed8cd7f31dd2ada0b8790258644b5c2a97f3b69cc930330a4e
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.