| --- |
| 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. |
|
|
