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README.md

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+---
+license: mit
+language:
+- en
+tags:
+- discourse-analysis
+- deberta-v3
+- onnx
+- on-device
+- browser-inference
+library_name: onnxruntime
+pipeline_tag: text-classification
+---
+
+# Pnyx Habermas - Discourse Legibility Model (v3)
+
+Named after Jurgen Habermas's *Theory of Communicative Action* (1981). This model makes discourse structure legible by extracting two validity dimensions from text.
+
+## Model
+
+- **Base**: `cross-encoder/nli-deberta-v3-small` (141M params)
+- **Format**: ONNX, FP16 (271 MB)
+- **Performance**: F1 0.974 (0.977 claim risk, 0.972 argument quality)
+- **Inference**: ONNX Runtime Web (WASM) for in-browser use
+
+## Outputs
+
+Two binary classification heads:
+
+| Head | Validity Claim | Description |
+|------|---------------|-------------|
+| `claim_risk` | *Wahrheit* (Truth) | Are unsupported assertions present? |
+| `argument_quality` | *Richtigkeit* (Rightness) | Is reasoning/evidence present? |
+
+Apply softmax to each head's logits. The `[1]` index gives the positive class probability.
+
+## Usage
+
+```js
+import * as ort from 'onnxruntime-web';
+import { AutoTokenizer } from '@huggingface/transformers';
+
+const tokenizer = await AutoTokenizer.from_pretrained('onblueroses/pnyx-habermas');
+const session = await ort.InferenceSession.create('model.onnx');
+
+const { input_ids, attention_mask } = tokenizer(text, {
+  padding: true, truncation: true, max_length: 256, return_tensors: 'np',
+});
+
+const output = await session.run({
+  input_ids: new ort.Tensor('int64', input_ids.data, input_ids.dims),
+  attention_mask: new ort.Tensor('int64', attention_mask.data, attention_mask.dims),
+});
+```
+
+## Training
+
+- 10K balanced samples (2,500 per cell) + 453 boundary examples (5x oversampled)
+- Focal loss (gamma=2) + label smoothing (0.05)
+- 5 epochs, lr=5e-6, batch size 32
+- Trained on T4 GPU via Modal
+
+## Part of Pnyx
+
+This model powers the SEE layer of [Pnyx](https://github.com/onblueroses/pnyx), a listening infrastructure for public discourse built for the Agora Hackathon x TUM.ai E-Lab (April 2026).