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tgupj/
tiny-router

onnx

tgupj/tiny-router-data

microsoft/deberta-v3-small

quantized

mit

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tiny-router

tiny-router is a compact experimental multi-head routing classifier for short, domain-neutral messages with optional interaction context. It predicts four separate signals that downstream systems or agents can use for update handling, action routing, memory policy, and prioritization.

What it predicts

relation_to_previous: new | follow_up | correction | confirmation | cancellation | closure
actionability: none | review | act
retention: ephemeral | useful | remember
urgency: low | medium | high

The model emits these heads independently at inference time, plus calibrated confidences and an overall_confidence.

Intended use

Route short user messages into lightweight automation tiers.
Detect whether a message updates prior context or starts something new.
Decide whether action is required, review is safer, or no action is needed.
Separate disposable details from short-term useful context and longer-term memory candidates.
Prioritize items by urgency.

Good use cases:

routing message-like requests in assistants or productivity tools
triaging follow-ups, corrections, confirmations, and closures
conservative automation with review fallback

Not good use cases:

fully autonomous high-stakes action without guardrails
domains that need expert reasoning or regulated decisions

Training data

This checkpoint was trained on the synthetic dataset split in:

data/synthetic/train.jsonl
data/synthetic/validation.jsonl
data/synthetic/test.jsonl

The data follows a structured JSONL schema with:

current_text
optional interaction.previous_text
optional interaction.previous_action
optional interaction.previous_outcome
optional interaction.recency_seconds
four label heads under labels

Model details

Base encoder: microsoft/deberta-v3-small
Architecture: encoder-only multitask classifier
Pooling: learned attention pooling
Structured features:
- canonicalized previous_action embedding
- previous_outcome embedding
- learned projection of log1p(recency_seconds)
Head structure:
- dependency-aware multitask heads
- later heads condition on learned summaries of earlier head predictions
Calibration:
- post-hoc per-head temperature scaling fit on validation logits

This checkpoint was trained with:

batch_size = 32
epochs = 20
max_length = 128
encoder_lr = 2e-5
head_lr = 1e-4
dropout = 0.1
pooling_type = attention
use_head_dependencies = true

Current results

Held-out test results from artifacts/tiny-router/eval.json:

macro_average_f1 = 0.7848
exact_match = 0.4570
automation_safe_accuracy = 0.6230
automation_safe_coverage = 0.5430
ECE = 0.3440

Per-head macro F1:

relation_to_previous = 0.8415
actionability = 0.7982
retention = 0.7809
urgency = 0.7187

Ablations:

current_text_only = 0.7058
current_plus_previous_text = 0.7478
full_interaction = 0.7848

Interpretation:

interaction context helps
actionability and urgency are usable but still imperfect
high-confidence automation is possible only with conservative thresholds

Limitations

The benchmark is task-specific and internal to this repo.
The dataset is synthetic, so distribution shift to real product traffic is likely.
Label quality on subtle boundaries still matters a lot.
Confidence calibration is improved but not strong enough to justify broad unattended automation.

Example inference

json

{
  "relation_to_previous": { "label": "correction", "confidence": 0.94 },
  "actionability": { "label": "act", "confidence": 0.97 },
  "retention": { "label": "useful", "confidence": 0.76 },
  "urgency": { "label": "medium", "confidence": 0.81 },
  "overall_confidence": 0.87
}

How to load

This repo uses a custom checkpoint format. Load it with this project:

python

from tiny_router.io import load_checkpoint
from tiny_router.runtime import get_device

device = get_device(requested_device="cpu")
model, tokenizer, config = load_checkpoint("artifacts/tiny-router", device=device)

Or run inference with:

bash

uv run python predict.py \
  --model-dir artifacts/tiny-router \
  --input-json '{"current_text":"Actually next Monday","interaction":{"previous_text":"Set a reminder for Friday","previous_action":"created_reminder","previous_outcome":"success","recency_seconds":45}}' \
  --pretty