Instructions to use hf-internal-testing/tiny-random-XLNetForQuestionAnsweringSimple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-XLNetForQuestionAnsweringSimple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-internal-testing/tiny-random-XLNetForQuestionAnsweringSimple")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-XLNetForQuestionAnsweringSimple") model = AutoModelForQuestionAnswering.from_pretrained("hf-internal-testing/tiny-random-XLNetForQuestionAnsweringSimple") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 48fa0b3dcf0bab4a915476b82ebfc0843592da4e3f2420185731246feb0aff7d
- Size of remote file:
- 4.38 MB
- SHA256:
- 9b06b3dc10d1923cfbf094123bb6eea8579f60760d2e4e36d56b03bfe6a86310
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.