---
license: mit
language:
  - en
library_name: transformers
pipeline_tag: text-classification
tags:
  - email
  - phishing
  - classification
  - bert
  - specific-ai
  - gguf
base_model: google-bert/bert-base-uncased
---

# 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](LICENSE).

Copyright (C) 2026 Specific AI Inc. All rights reserved.