Instructions to use ahmed792002/Finetuning_XLNET_Paraphrase_Classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahmed792002/Finetuning_XLNET_Paraphrase_Classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ahmed792002/Finetuning_XLNET_Paraphrase_Classification")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ahmed792002/Finetuning_XLNET_Paraphrase_Classification") model = AutoModelForSequenceClassification.from_pretrained("ahmed792002/Finetuning_XLNET_Paraphrase_Classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from ahmed792002/Finetuning_XLNET_Paraphrase_Classification: direct link, hf CLI and curl.
- Browser
- Download file 469 MB
-
https://huggingface.co/ahmed792002/Finetuning_XLNET_Paraphrase_Classification/resolve/main/model.safetensors
- Command line
-
hf download hf://ahmed792002/Finetuning_XLNET_Paraphrase_Classification/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/ahmed792002/Finetuning_XLNET_Paraphrase_Classification/resolve/main/model.safetensors
469 MB
- Xet hash:
- 7b38fee4c2f6270dd88a596013de21e6814a9a560255fccfd9157de16cbe06a8
- Size of remote file:
- 469 MB
- SHA256:
- f8ad96335da3b4db1ba27710d9658b6ca50b2ab340fe189c2718d03a666ee4c9
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