---
language:
- en
license: apache-2.0
tags:
- emotion-detection
- emotional-intelligence
- mental-health
- wellbeing
- psychology
- text-classification
- multi-label-classification
- deberta
- distillation
pipeline_tag: text-classification
library_name: resonance-layer
---

# Resonance — Emotional Intelligence Layer for LLMs

**resonance-layer** sits between your users and your LLM. It reads the emotion behind what someone writes and injects that context before the LLM responds.

```python
from resonance import Resonance

r = Resonance(user_id="your-user-id")
context = r.process("I've been so anxious about this")
llm.chat(system=context.to_prompt(), message=message)
```

That's it. The LLM now knows the emotional state, psychological signals, and longitudinal pattern for this user — before it says a word.

---

## The problem this solves

Text doesn't carry emotion. When someone types *"I'm fine"* or *"whatever, doesn't matter"* — the LLM sees words. It has no idea if that person is exhausted, shutting down, or genuinely okay.

Resonance gives it that context, continuously, per user, without any extra effort from the developer or the user.

---

## What this model detects

This is a custom 86M parameter student model distilled from three specialist teachers, trained on real human emotional expression across 29 datasets (~740K rows).

**18 output heads across 4 groups:**

| Group | Heads |
|---|---|
| Core | Primary emotion (7-class), VAD continuous, CNN local patterns, confidence calibration |
| Frameworks | Secondary emotion (21-class TONE), PERMA Ã--5, Window of Tolerance, reappraisal/suppression, Wise Mind |
| Ethics | Crisis detection, alexithymia screening |
| SDT | Autonomy, competence, relatedness |

All framework scores are **continuous outputs**, not binary flags — grounded in the clinical research each framework comes from.

**What makes it different from general emotion models:**

The three specialist teachers each cover a distinct psychological blind spot:
- **T1 (DeBERTa-v3-large + CNN):** shame/guilt separation with PoliGuilt typing — the distinction most models collapse or miss entirely
- **T2 (XLNet-large):** anger and complex social context
- **T3 (ELECTRA-large):** fear, PERMA wellbeing, and efficiency

---

## Installation

```bash
pip install resonance-layer
```

Weights download automatically on first use (~700MB). Fully local after that — no API call, no data leaving your infrastructure.

---

## Quick start

```python
from resonance import Resonance

r = Resonance(user_id="alice")
result = r.process("I keep second-guessing everything I do")

print(result.primary_emotion)        # e.g. "anxiety"
print(result.vad)                    # valence, arousal, dominance
print(result.perma)                  # PERMA scores across 5 dimensions
print(result.crisis_detected)        # always check this first
print(result.to_prompt())            # ready-made system prompt context
```

**If `crisis_detected` is True, surface a crisis resource immediately.** This is non-negotiable — it's in the ethics documentation and built into the API contract.

---

## Longitudinal learning

Resonance learns in two ways per user:

1. **Passive** — every call to `r.process()` updates the user's local profile. Patterns, suppression signals, regulation style — all accumulate silently. The LLM gets richer context with every conversation.
2. **Active** — explicit corrections (e.g. a user tapping a different emotion chip) feed directly into per-user reinforcement and adjust future detections for them specifically.

Both paths are local. No opt-in required.

---

## Upgrading from v1

```bash
pip install --upgrade resonance-layer
```

No API changes. Drop-in replacement.

---

## Honest limitations

- Shame F1 is the lowest of any class — it's the hardest psychological distinction to learn
- Sadness is weaker than anger, fear, and joy
- Some high-arousal distress returns as surprise
- All gaps are documented in the repo

---

## Architecture

- **Student:** 86M parameter DeBERTa-v3-base, 18 active heads
- **Teachers:** DeBERTa-v3-large+CNN (T1), XLNet-large (T2), ELECTRA-large (T3)
- **Distillation:** Multi-teacher knowledge distillation with per-head weighting, mutual information maximisation, born-again networks, uncertainty-aware loss
- **Training data:** 29 datasets, ~740K rows, 7 locked dataset rules (commercial licence, institutional/peer-reviewed, real human expression, framework-mapped, not too narrow, no gated access, no severe class imbalance)

---

## Links

- **PyPI:** [resonance-layer](https://pypi.org/project/resonance-layer/)
- **GitHub:** [wpferrell/Resonance](https://github.com/wpferrell/Resonance)
- **Docs & landing page:** [resonance-layer.com](https://resonance-layer.com)

---

*Named after Jody. She walks into a room and just knows. That's the standard.*