Instructions to use Etelis/TUF_XLNET_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/TUF_XLNET_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/TUF_XLNET_5E")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/TUF_XLNET_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/TUF_XLNET_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Etelis/TUF_XLNET_5E: direct link, hf CLI and curl.
- Browser
- Download file 469 MB
-
https://huggingface.co/Etelis/TUF_XLNET_5E/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Etelis/TUF_XLNET_5E/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Etelis/TUF_XLNET_5E/resolve/main/pytorch_model.bin
469 MB
- Xet hash:
- 7ad1dd4a1fe6e1fc1fcb3d194d7812b8152e33201525770626783dbc4ab990f0
- Size of remote file:
- 469 MB
- SHA256:
- 7b5f428b08f2552bf28e78fc2100656105f9dc30d51e5bbac0b20ab7fa098a23
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