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
File size: 669 Bytes
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"activation": "gelu",
"architectures": [
"DistilBertForSequenceClassification"
],
"attention_dropout": 0.1,
"dim": 768,
"dropout": 0.1,
"dtype": "float32",
"hidden_dim": 3072,
"id2label": {
"0": "HAM",
"1": "SPAM"
},
"initializer_range": 0.02,
"label2id": {
"0": "HAM",
"1": "SPAM"
},
"max_position_embeddings": 512,
"model_type": "distilbert",
"n_heads": 12,
"n_layers": 6,
"pad_token_id": 0,
"problem_type": "single_label_classification",
"qa_dropout": 0.1,
"seq_classif_dropout": 0.2,
"sinusoidal_pos_embds": false,
"tie_weights_": true,
"transformers_version": "4.56.1",
"vocab_size": 30522
}
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