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specific-AI/email-agent-phishing-detection

sourceHugging Facemitupdated 2mo agoView on Hugging Face

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Model Card

specific-AI/email-agent-phishing-detection

A compact BERT phishing detector distilled with [Specific AI](https://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):

text

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 BertForSequenceClassification weights (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

python

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:

bash

pip install specific-ai-tools

python

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](https://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](https://specific.ai).

License

MIT — see LICENSE.