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:
- d505fe94d3e062e92110281c2940d31778a74ebce0bff91529aaf204dadc182a
- Size of remote file:
- 4.46 MB
- SHA256:
- 6758b68bfe9e88d3d190b63833b8f6e3ce917333a7114219920ecdb00315a770
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