Instructions to use Etelis/CR_XLNET_5E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Etelis/CR_XLNET_5E with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Etelis/CR_XLNET_5E")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Etelis/CR_XLNET_5E") model = AutoModelForSequenceClassification.from_pretrained("Etelis/CR_XLNET_5E", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Etelis/CR_XLNET_5E: direct link, hf CLI and curl.
- Browser
- Download file 469 MB
-
https://huggingface.co/Etelis/CR_XLNET_5E/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Etelis/CR_XLNET_5E/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Etelis/CR_XLNET_5E/resolve/main/pytorch_model.bin
469 MB
- Xet hash:
- 166e7c3c16d60d2b5fdcadd952264ae62e63176aadce00f018834f980e7c3814
- Size of remote file:
- 469 MB
- SHA256:
- 4c1e5700d2b6f5b8604af3bfa3c3071d6b65d8d6b355152a71a3378c3c645969
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