specific-AI/email-agent-triage
A compact BERT email triage classifier distilled with Specific AI. It assigns each email to one of five action-oriented categories so agentic workflows can decide whether to reply, archive, or take no action.
| 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 | Suggested next action |
|---|---|---|
| URGENT | Requires immediate attention (e.g. critical system failure, hard deadline right now). | Reply |
| NEEDS_RESPONSE | A task or response is owed, but it is not a drop-everything emergency. | Reply |
| PROMOTIONAL | Bulk mail, unsolicited promotions, or newsletters. | Archive |
| PERSONAL | Non-business, personal communications. | None |
| FYI | Informational only — the recipient should know, but no reply is required. | None |
Evaluation
Compared against gpt-5.4-mini as a teacher / baseline on the same evaluation set:
| Metric | gpt-5.4-mini | SpecificAI |
|---|---|---|
| Accuracy | 0.693 | 0.720 |
| Precision | 0.810 | 0.763 |
| Recall | 0.693 | 0.720 |
| F1 score | 0.693 | 0.716 |
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-triage"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)
model.eval()
text = """From: ops@example.com
Subject: Production outage
Production is down — please escalate immediately."""
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)
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-triage",
checkpoint="specific-AI/email-agent-triage:bert-base-only.gguf",
lemonade_base_url="http://localhost:13305",
)
text = """From: user@example.com
Subject: Billing question
Please escalate this ticket to billing."""
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 agent triage in production or on-device / CPU deployments
- Routing messages into reply / archive / no-action queues
Out of scope: legal advice, medical triage, or safety-critical decisions without human review. Labels reflect email workflow intent, not sender identity verification.
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.