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
Upload folder using huggingface_hub
Browse files- config.json +2 -2
- model.safetensors +1 -1
config.json
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},
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"initializer_range": 0.02,
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"label2id": {
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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},
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"initializer_range": 0.02,
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"label2id": {
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"0": "HAM",
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"1": "SPAM"
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},
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"max_position_embeddings": 512,
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"model_type": "distilbert",
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 267832560
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