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 # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple: direct link, hf CLI and curl.
- Browser
- Download file 4.4 MB
-
https://huggingface.co/hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/hf-tiny-model-private/tiny-random-XLNetForQuestionAnsweringSimple/resolve/main/pytorch_model.bin
4.4 MB
- Xet hash:
- 28cca2a7b90260f872c41a4b6ffbdd4bbe6d3532ca0d71e58dba98047e984274
- Size of remote file:
- 4.4 MB
- SHA256:
- ca0f233f8767780f035d0e4eafe6d1501158f7ab61060d40707a5937e59fda3c
路
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