specific-AI/email-agent-phishing-detection
A compact BERT phishing detector distilled with Specific AI. It classifies email content as phishing or not, for use in email agents and security-aware inbox workflows.
| Task | Single-label text classification |
| Base model | bert-base-uncased |
| Training data | ~15,000 examples |
| License | MIT |
Input format
Examples were trained on emails formatted as plain text with From, Subject,
and body (blank line between the headers and the body):
From: <from>
Subject: <subject>
<body>
Pass inputs in this same shape at inference time for best results.
Labels
| Label | Meaning |
|---|---|
| True | Phishing detected |
| False | Phishing was not detected |
Evaluation
Compared against gpt-5.4-mini as a teacher / baseline on the same evaluation set:
| Metric | gpt-5.4-mini | SpecificAI |
|---|---|---|
| Accuracy | 0.971 | 0.975 |
| Precision | 0.976 | 0.975 |
| Recall | 0.971 | 0.975 |
| F1 score | 0.972 | 0.975 |
Repository contents
This card ships both a full Hugging Face checkpoint and GGUF-ready artifacts:
- Full
BertForSequenceClassificationweights (model.safetensors) + tokenizer - Head layers as NumPy files (
pooler_*.npy,classifier_*.npy) for GGUF / Lemonade fusion - Encoder GGUF:
bert-base-only.gguf(CLS pooling; use with raw / unnormalized embeddings)
Quick start — Transformers
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_id = "specific-AI/email-agent-phishing-detection"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()
text = """From: security@paypa1-support.com
Subject: Your account will be locked
Verify your password at http://example-phish.test/login to keep access."""
inputs = tokenizer(text, return_tensors="pt", truncation=True)
with torch.no_grad():
logits = model(**inputs).logits
pred = model.config.id2label[int(logits.argmax(-1))]
print(pred) # "True" or "False"
Quick start — Lemonade + specific-ai-tools
When running the GGUF encoder through Lemonade Server:
pip install specific-ai-tools
from specific_ai_tools.embedding_heads import LemonadeEmbeddingClassifier
classifier = LemonadeEmbeddingClassifier(
lemonade_model_name="user.email-agent-phishing-detection",
checkpoint="specific-AI/email-agent-phishing-detection:bert-base-only.gguf",
lemonade_base_url="http://localhost:13305",
)
text = """From: noreply@secure-mail-alert.com
Subject: Reset your password now
Click here to reset your password immediately."""
result = classifier.predict_one(text)
print(result.predicted_labels, result.predicted_confidences)
See the Specific AI toolkit docs for llama-cpp and other embedding backends.
Intended use
- Email / inbox agents that need a fast on-device or CPU phishing signal
- Pre-filter or assistive scoring alongside other security controls
Out of scope: sole authority for blocking, quarantine, or legal determinations. Treat outputs as a high-throughput classifier signal and keep human / policy review in the loop for high-impact actions.
About Us
Specific AI is the automatic SLM distillation platform that turns task prompts into production-grade small language models in days — not weeks — so your subject matter experts can ship models without waiting on scarce data-science bandwidth.
We help enterprises move agentic AI from prototype to production with SLMs that are typically 1,000×–10,000× smaller than teacher LLMs, run in milliseconds on CPUs or edge devices, and deliver the same or better task quality at a fraction of the cost — self-hosted on your cloud or downloaded for your own inference stack.
Prompt → Distill → Deploy. Bring your prompt and data, drop them into Specific AI, and get a validated small model ready to test and ship.
Ready to create SLMs at scale? Visit specific.ai.
License
MIT — see LICENSE.
Copyright (C) 2026 Specific AI Inc. All rights reserved.