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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b4f704344c9196b9389b24da8bc2003e9e0104a414c02430e82e0d0c578460a1
- Size of remote file:
- 4.49 MB
- SHA256:
- 541db7c99fe54baf164cab84504fd4821aff8d9a9611ba0a6fae55b1840a1d85
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.