Text Classification
Transformers
Safetensors
English
distilbert
text-generation-inference
spam-detection
nlp
binary-classification
text-embeddings-inference
Instructions to use kenbaker-gif/Email_Spam_Classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kenbaker-gif/Email_Spam_Classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kenbaker-gif/Email_Spam_Classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kenbaker-gif/Email_Spam_Classifier") model = AutoModelForSequenceClassification.from_pretrained("kenbaker-gif/Email_Spam_Classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad0986fcaaa66d1f3f8c865b6b73c2c30c16e128bd7bd214201c7d637d933003
- Size of remote file:
- 268 MB
- SHA256:
- 6072dfbfae3158d49e5e9d2326ec3b28de1dd554427b744800051220d54556b0
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