Instructions to use hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple") model = AutoModelForQuestionAnswering.from_pretrained("hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6130a3f12b9b3de8dc7b4f04ed7808ce9e333dc4edf7827771e189d3eac152fa
- Size of remote file:
- 4.38 MB
- SHA256:
- ae610c096e2625f8db90b31a80e68a423abfe8f8189cdd1061c28ac103a85a90
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