Text Classification
Transformers
Safetensors
Indonesian
gemma2
spam-detection
indonesian
chatbot
security
Instructions to use nahiar/spam-analysis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahiar/spam-analysis with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nahiar/spam-analysis")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nahiar/spam-analysis") model = AutoModelForSequenceClassification.from_pretrained("nahiar/spam-analysis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from nahiar/spam-analysis: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/nahiar/spam-analysis/resolve/main/training_args.bin
- Command line
-
hf download hf://nahiar/spam-analysis/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/nahiar/spam-analysis/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 3032f398db681e39bd0906ca0b2278b4b932674c4fd0872c9509b8eef468c4a6
- Size of remote file:
- 5.24 kB
- SHA256:
- 8eaec6f34a68f510bd8431a5cb87c76516e24cf3a56ed62a67e867da5ae0a9d1
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