Text Classification
Transformers
Safetensors
Indonesian
bert
spam-detection
indonesian
twitter
retrained
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use nahiar/spam-detection-bert-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nahiar/spam-detection-bert-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="nahiar/spam-detection-bert-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("nahiar/spam-detection-bert-v1", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("nahiar/spam-detection-bert-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from nahiar/spam-detection-bert-v1: direct link, hf CLI and curl.
- Browser
- Download file 733 kB
-
https://huggingface.co/nahiar/spam-detection-bert-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://nahiar/spam-detection-bert-v1/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/nahiar/spam-detection-bert-v1/resolve/main/tokenizer.json
733 kB
File too large to display, you can check the raw version instead.