ayjays132/EMOTIONVERSE-2

🌌 EmotionVerse-2: Galactic Emotional Intelligence

Apache-2.0 BERT-base encoder Multi-Task (6 heads) Plutchik + Circumplex F1 Macro: 0.951 Coherence Index: 0.97 HF Transformers

EmotionVerse-2 unifies psychologically-grounded labeling (Plutchik) with a valence-arousal manifold, neural plasticity, and memory consolidation. It doesn’t just tag text—it resolves why it feels the way it does, across interlocking tasks that share a single encoder for maximal transfer.

✨ Revolutionary Emotional Paradigm

A single BERT encoder feeds six specialized heads—Primary, Secondary, Meta, Sentiment, Interaction, and Context—trained end-to-end with dynamic loss weighting. The result is emergent cross-task awareness: subtle shifts, mixed feelings, and psychologically consistent outputs.

🚀 Unified Multi-Task Engine. One encoder; six heads. Joint optimization learns shared affective features while preserving task-specific finesse.
🧬 Plutchik × Russell Fusion. Labels live in a combined categorical + circumplex space (valence, arousal, intensity), preserving family hierarchies and psychological distances.
🔗 Adaptive Loss Balancing. Dynamic task weights + focal components lift rare emotions without destabilizing common ones.
📊 Holistic Metrics. Per-task precision/recall/F1 plus a cross-task Emotion Coherence Index (valence-arousal similarity across heads).

View architecture diagram (SVG)

🏆 Grand Performance Showcase

The evaluation deliberately pits generalist GoEmotions models against a specialist trained on Plutchik’s eight core emotions. The “unfairness” is instructive: it exposes the cost of vocabulary mismatch and the power of a psychologically aligned label space.

🧠 Emotional Lexicons: Why Translation Fails

Missing Concepts. GoEmotions lacks dedicated pathways for Plutchik categories like Trust or Anticipation as defined here.
Forced Guesses. Inputs such as “I have complete faith in your plan” map to “Approval/Admiration,” not “Trust.” Close ≠ correct when precision matters.
Semantic Misalignment. The learned geometry of the label space differs; a round peg in a square hole.

Model	F1 Macro
EMOTIONVERSE-2	0.951 🌟
GoEmotions-RoBERTa	0.024 🔻
GoEmotions-BERT	0.012 📉
DistilBERT-Emotion	0.007 👻

💎 Nearly-Perfect Diagonal (Confusion Matrix)

Diagonal dominance indicates surgical separation of Plutchik classes; off-diagonals remain low and psychologically adjacent when present.

Cross-task capability radar (SVG)

🔬 Architecture & Emotional Data Synthesis

Core Modules

Neural Plasticity Gate. Reweights hidden states conditioned on intensity and local valence/arousal; supports context-sensitive adaptation.
Amygdala-Inspired Intensifier. Saturating non-linearity emphasizes high-arousal signals without drowning low-arousal nuance.
GRU Memory Consolidator. Injects sequential emotional context to stabilize predictions across turns or sentences.
Circumplex Injector. Concatenates/merges valence-arousal (+ intensity) vectors into token-level or pooled embeddings.

Training Setup

Backbone: BERT-base uncased.
Optimization: AdamW, linear warmup/decay, mixed precision.
Loss: Weighted sum across heads with dynamic task weights; focal term for rare labels; label smoothing for stability.
Regularization: Dropout, stochastic depth on head adapters (if enabled), gradient clipping.

🚀 Psychologically-Informed Embeddings

Emotion labels are embedded vectors, not IDs. Selected dimensions:

Valence/Arousal Anchors. First two dims: positivity/negativity and activation.
Intensity Signal. Third dim calibrated from dataset statistics.
Family Centroids. Pull toward Plutchik family centers to preserve conceptual proximity.
Relationship Pull. Secondary affinities (e.g., Joy↔Anticipation) maintain a smooth manifold.

📊 Evaluation & Performance Horizons

Primary Head: Accuracy, Top-2 recall.
Secondary & Meta: Macro/Weighted F1.
Interaction & Context: Precision/Recall for communicative style cues.
Sentiment: Mixed-class accuracy with polarity overlap analysis.
Emotion Coherence Index: Cross-head valence/arousal agreement.

🛠️ Usage & Integration

Quickstart (Transformers)

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

name = "ayjays132/EmotionVerse-2"
tok  = AutoTokenizer.from_pretrained(name)
mdl  = AutoModelForSequenceClassification.from_pretrained(name)
text = "I’m thrilled yet anxious about tomorrow’s launch."
batch = tok(text, return_tensors="pt", truncation=True, padding=True)
with torch.no_grad():
    out = mdl(**batch)

Example: Plutchik mapping & softmax

import torch.nn.functional as F

plutchik_labels = ["joy","trust","anticipation","surprise","anger","sadness","fear","disgust"]
plutchik_logits = out.logits[:, :len(plutchik_labels)]  # slice depends on export
probs = F.softmax(plutchik_logits, dim=-1)[0].tolist()
for lbl, p in sorted(zip(plutchik_labels, probs), key=lambda x: -x[1]):
    print(f"{lbl:12s} {p:.3f}")

Batch inference with coherence check

def coherence(valence_arousal_primary, valence_arousal_sentiment):
    # cosine similarity across valence/arousal heads
    a, b = valence_arousal_primary, valence_arousal_sentiment
    return (a @ b) / ((a.norm()+1e-8) * (b.norm()+1e-8))

📂 EmotionVerse Dataset: Cosmic Fuel

The EmotionVerse dataset anchors this model with 3K+ entries annotated across six dimensions, including meta-emotional and contextual narratives.

📚 Reproducibility Snippets

Minimal training loop sketch

from torch.optim import AdamW
from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()
optim  = AdamW(mdl.parameters(), lr=3e-5, weight_decay=0.01)
for step, batch in enumerate(train_loader):
    with autocast():
        out = mdl(**{k:v.to(mdl.device) for k,v in batch.items()})
        # combine task losses (weights dynamically updated)
        loss = out.loss  # if model returns weighted sum
    scaler.scale(loss).backward()
    scaler.step(optim); scaler.update(); optim.zero_grad()

Evaluation (macro F1) sketch

from sklearn.metrics import f1_score

  all_y, all_p = [], []

for batch in val_loader:
    with torch.no_grad():
        out = mdl(**{k:v.to(mdl.device) for k,v in batch.items()})
    logits = out.logits[:, :8]  # primary head slice
    preds  = logits.argmax(-1).cpu().tolist()
    all_p += preds; all_y += batch["labels"].tolist()
print("F1 Macro:", f1_score(all_y, all_p, average="macro"))

📜 Licensing

Released under the Apache 2.0 License. Use, modify, and ship with confidence.

🙏 Acknowledgements

Thanks to Hugging Face (transformers, datasets) and the research community advancing affective computing, psychological modeling, and trustworthy NLP.