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") - Notebooks
- Google Colab
- Kaggle
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- Only trained for binary classification: HAM (not spam) vs SPAM.
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- Performance may degrade on longer texts
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- The model may need fine-tuning for datasets outside SMS messages to maintain accuracy.
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- Only trained for binary classification: HAM (not spam) vs SPAM.
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- Performance may degrade on longer texts like social media messages.
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- The model may need fine-tuning for datasets outside SMS messages to maintain accuracy.
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