Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use rbc33/spam_not_spam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rbc33/spam_not_spam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rbc33/spam_not_spam")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rbc33/spam_not_spam") model = AutoModelForSequenceClassification.from_pretrained("rbc33/spam_not_spam") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- edee4f5f1cf0acce238b03aaca5d4177e038f35e7634c37704b0c0820c7da48b
- Size of remote file:
- 5.3 kB
- SHA256:
- bf59812819858377961b4723783ef808f7dc5f8bb5b11133574be4f203231a98
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