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 training_args.bin from ahmed792002/Finetuning_XLNET_Paraphrase_Classification: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/ahmed792002/Finetuning_XLNET_Paraphrase_Classification/resolve/main/training_args.bin
- Command line
-
hf download hf://ahmed792002/Finetuning_XLNET_Paraphrase_Classification/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ahmed792002/Finetuning_XLNET_Paraphrase_Classification/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 3fdd22c468a6bbcb04efbf1405f40fdb5217ba4621d39fac6dcb161acb12bf14
- Size of remote file:
- 5.37 kB
- SHA256:
- b9903d57b2418646e98ee5f1ca8dbd2556578e61f50055646a3800500a8d50ac
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