| # Email-spam-detection |
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| This model detects whether an email message is spam or not spam (ham) using a fine-tuned transformer-based classifier. |
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| ## Model Details |
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| ### Model Description |
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| This is a binary text classification model trained to distinguish spam emails from legitimate (ham) emails. The model is based on a pretrained transformer architecture (e.g., BERT, RoBERTa) and fine-tuned on a labeled email dataset containing both spam and non-spam messages. |
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| - **Model type:** Transformer-based binary classifier |
| - **Language(s) (NLP):** English |
| - **License:** MIT License |
| - **Finetuned from model :** bert-base-uncased (example) |
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| ### Model Sources [optional] |
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| - **Repository:** https://huggingface.co/AventIQ-AI/email-spam-detection |
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| ## Loading the Model |
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| ```python |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification |
| import torch |
| |
| tokenizer = AutoTokenizer.from_pretrained("deepak/email-spam-detection") |
| model = AutoModelForSequenceClassification.from_pretrained("deepak/email-spam-detection") |
| |
| emails = [ |
| "Congratulations! You have won a $1000 gift card. Click here to claim.", |
| "Meeting moved to 3 PM today in the conference room.", |
| ] |
| |
| inputs = tokenizer(emails, padding=True, truncation=True, return_tensors="pt") |
| outputs = model(**inputs) |
| logits = outputs.logits |
| predictions = torch.argmax(logits, dim=-1) |
| print(predictions) # 1 = spam, 0 = ham |
| ``` |
| ## Training Details |
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| The model was trained on a labeled dataset of emails combining public spam corpora such as the Enron Spam dataset and other sources, balanced between spam and ham emails. Data preprocessing included cleaning email text, removing metadata, and tokenization. |
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| ## Training Procedure |
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| Emails were normalized by removing special characters and tokenized using the pretrained tokenizer. |
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| # Training Hyperparameters |
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| Training regime: fine-tuning with fp16 mixed precision on NVIDIA GPUs |
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| - Batch size: 32 |
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| - Learning rate: 2e-5 |
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| - Epochs: 4 |
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| - Speeds, Sizes, Times [optional] |
| - Checkpoint size: ~400MB |
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| - Training time: ~3 hours on 1 GPU |
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| ## Evaluation |
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| Testing Data, Factors & Metrics |
| Testing Data |
| Evaluation was performed on a held-out test split from the same dataset, containing unseen emails. |
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| # Factors |
| - No explicit subpopulation disaggregation. |
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| # Metrics |
| - Accuracy |
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| - Precision |
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| - Recall |
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| - F1-score |
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| ## Results |
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| **Metric** : **Score** |
| **Accuracy** : **0.95** |
| **Precision** : **0.93** |
| **Recall** : **0.92** |
| **F1-score** : **0.925** |
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| ## Model Examination |
| Attention analysis indicates the model focuses on key spam indicators like suspicious URLs, urgent calls to action, and financial keywords. |
