specific-AI/email-agent-triage

specific-AI/email-agent-triage

A compact BERT email triage classifier distilled with [Specific AI](https://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.

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

Evaluation

Compared against gpt-5.4-mini as a teacher / baseline on the same evaluation set:

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

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](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.

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