Text Classification
Transformers
Safetensors
distilbert
spam-detection
multilingual
security
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use IntegralBender/distilbert-spam-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IntegralBender/distilbert-spam-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="IntegralBender/distilbert-spam-detector")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("IntegralBender/distilbert-spam-detector") model = AutoModelForSequenceClassification.from_pretrained("IntegralBender/distilbert-spam-detector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from IntegralBender/distilbert-spam-detector: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/IntegralBender/distilbert-spam-detector/resolve/main/training_args.bin
- Command line
-
hf download hf://IntegralBender/distilbert-spam-detector/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/IntegralBender/distilbert-spam-detector/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 5c338ecaaf5769a65c4db6002787933c474e5d53468855d0cc1581f6e2328800
- Size of remote file:
- 5.91 kB
- SHA256:
- 610aab6cef80da2594d7850dadec59cadd1e8668e166a307ad8509d83c968f83
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