Instructions to use Tejas3/Xlnet_base_80 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tejas3/Xlnet_base_80 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Tejas3/Xlnet_base_80")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Tejas3/Xlnet_base_80") model = AutoModelForSequenceClassification.from_pretrained("Tejas3/Xlnet_base_80", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Tejas3/Xlnet_base_80: direct link, hf CLI and curl.
- Browser
- Download file 469 MB
-
https://huggingface.co/Tejas3/Xlnet_base_80/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Tejas3/Xlnet_base_80/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Tejas3/Xlnet_base_80/resolve/main/pytorch_model.bin
469 MB
- Xet hash:
- 51c8f6c9c4a78d96f6e366f42128113226e978f999b31f606293bb328bc35595
- Size of remote file:
- 469 MB
- SHA256:
- 0f1b5c107c50816f4cd57228a13acd1c7135dd3299a3d21f4669909be33a0293
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