Instructions to use hf-internal-testing/tiny-random-MraForQuestionAnswering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-MraForQuestionAnswering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-MraForQuestionAnswering")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-MraForQuestionAnswering") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-MraForQuestionAnswering") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 563225941a6b609d0f038fc252753341ccfa3ed28aae58e5280cf36af083e331
- Size of remote file:
- 160 kB
- SHA256:
- 23a7c9e57e4c0c0b29f4056d7e9ec91c738dd139cc1e42c30ad76af608dca1b0
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.