Instructions to use Jeffrey0216/spam_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeffrey0216/spam_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeffrey0216/spam_model")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Jeffrey0216/spam_model") model = AutoModelForSequenceClassification.from_pretrained("Jeffrey0216/spam_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Jeffrey0216/spam_model: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Jeffrey0216/spam_model/resolve/main/training_args.bin
- Command line
-
hf download hf://Jeffrey0216/spam_model/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Jeffrey0216/spam_model/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- af951bb167d9335f89095360890eb05ecdf0608ceadbbbf845ccdef95dc71b2d
- Size of remote file:
- 5.2 kB
- SHA256:
- 6f1186c10635cc22e33bc4a412c109dd272a26009af1102b4f23abdf3f282ffb
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