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:
- e9da936721262ec97f25ecac75f5cbfe8663c8970173e2f0b19722d9c817917c
- Size of remote file:
- 32.2 MB
- SHA256:
- 6458aaab4ae31f348be01ebd6bece806e599c8e66eb90f3ad660e4e0ed4dc1ed
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.