Instructions to use hf-internal-testing/tiny-random-MvpForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MvpForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-MvpForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MvpForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-MvpForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 89a05e0797b71df40d2744b409e0b0ac08e53d90e6716131f68e1727cd4c59bd
- Size of remote file:
- 119 kB
- SHA256:
- e3e971971e9c80fbbf74bc73860f6ba527c1ce659aea313d66e45047d97245e4
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.