Instructions to use hf-internal-testing/tiny-random-MT5ForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MT5ForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-MT5ForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MT5ForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-MT5ForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b42ece02f6f710a7927ec326184b78a6746781a1a2d2aaaef3ef2379192ba542
- Size of remote file:
- 32.2 MB
- SHA256:
- 8818af0d036daa7fb1d007a9ccb51cb9dbf93f637b6b446ef613468b0c24d64c
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.