Instructions to use hf-internal-testing/tiny-random-RobertaForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RobertaForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-RobertaForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RobertaForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-RobertaForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 49d64c6e2780106b7a4bebb6eaee2b19b951b12d40dd3a97f59b4122829dc0d7
- Size of remote file:
- 349 kB
- SHA256:
- 559157640100d5348d5554f2d1a278588ab168d28819899dfd54e632aed4221a
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.