HangJang/C4-Cognitive-Classifier-Heavy

C4 Cognitive Classifier — Heavy (mDeBERTa-v3-base)

High-accuracy 27-class cognitive state classifier.

This is the "heavy" variant of the C4-Cognitive-Classifier. It uses a larger multilingual encoder (mDeBERTa-v3-base) and is significantly more accurate than the light DistilBERT variant, at the cost of larger size and slower inference.

Variants

Variant	Repository	Base model	ONNX size	Mean val accuracy	Best for
light	HangJang/C4-Cognitive-Classifier-v1	DistilBERT	~416 MB	~85.9%	Laptops, fast SaaS, first tries
heavy	this repo	mDeBERTa-v3-base	~1.1 GB	~96.5%	Best accuracy, multilingual text, research

State space (Z₃³)

The model classifies text into three independent axes:

Axis	ID	Values	Meaning
Time (t)	0	Past, Present, Future	Temporal orientation of thought
Scale (s)	1	Concrete, Abstract, Meta	Level of abstraction
Agency (a)	2	Self, Other, System	Relational stance

The full space is 3 × 3 × 3 = 27 cognitive states.

Performance

Measured on the c4factory validation split:

Head	Accuracy
Time (t)	98.1%
Scale / Dimension (s)	97.1%
Agency / Identity (a)	94.2%
Mean	96.5%

Files

• c4_mdeberta_v2.onnx — ONNX inference model (~1.1 GB)
• tokenizer.json, tokenizer_config.json, spm.model — mDeBERTa-v3-base tokenizer
• config.json — base model config

Usage

Direct ONNX inference

import onnxruntime as ort
import numpy as np
from transformers import AutoTokenizer

session = ort.InferenceSession("c4_mdeberta_v2.onnx")
tokenizer = AutoTokenizer.from_pretrained("HangJang/C4-Cognitive-Classifier-Heavy")

text = "Я думаю о будущем и строю планы на многие годы вперёд."
tokens = tokenizer(text, return_tensors="np", padding=True, truncation=True, max_length=256)

outputs = session.run(None, {
    "input_ids": tokens["input_ids"],
    "attention_mask": tokens["attention_mask"],
})

t = int(np.argmax(outputs[0][0]))  # Time
s = int(np.argmax(outputs[1][0]))  # Scale (dimension)
a = int(np.argmax(outputs[2][0]))  # Agency (identity)

print(f"C4 State: ({t}, {s}, {a})")

With Deep Self Mirror (DSM)

# CLI
dsm me ~/Downloads/conversations.json --c4-model heavy

# Web UI
streamlit run dsm_web.py
# Then choose "Heavy — best accuracy" in Model settings.

Notes

• The tokenizer comes from microsoft/mdeberta-v3-base and is included here for a self-contained download.
• Quantized (INT8) versions were tested but degraded accuracy substantially, so this release keeps FP32 weights for maximum quality.
• Multilingual: the mDeBERTa-v3-base encoder supports English, Russian, and 100+ languages.

Citation

@misc{c4_cognitive_classifier_v1,
  title = {C4-Cognitive-Classifier: Z₃³ Cognitive Topology from Natural Language},
  author = {Selyutin, I.G.},
  year = {2026},
  url = {https://huggingface.co/HangJang/C4-Cognitive-Classifier-v1}
}