Instructions to use L-NLProc/PredEx_XLNet_Large_Pred with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use L-NLProc/PredEx_XLNet_Large_Pred with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="L-NLProc/PredEx_XLNet_Large_Pred")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("L-NLProc/PredEx_XLNet_Large_Pred") model = AutoModel.from_pretrained("L-NLProc/PredEx_XLNet_Large_Pred") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2f4a6b08c6fff07596541539b519f2c0f4e6ab10653b1b598c28208a546b10d5
- Size of remote file:
- 1.44 GB
- SHA256:
- 9849b835384dee288694079091ce9166f8fc7a1e3982c85f7fd0b04462f00ebf
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