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RavinduSen/JaneGPT-v2-Janus

sourceHugging Faceapache-2.0updated 1mo agoView on Hugging Face
2likes34downloads
Model Card

JaneGPT v2 Janus - Intent Classification Model

Total Downloads Monthly Downloads

<p align="center"> <a href="https://janus-web-demo.vercel.app/"> <img src="https://img.shields.io/badge/Launch-TempleofJanus-6366f1?style=for-the-badge&logo=vercel" alt="Experience Janus"> </a> </p>

<style> / ---- Hero section ---- / .hero-banner { text-align: center; margin-bottom: 12px; } .hero-banner img { max-width: 100%; border-radius: 12px; }

.subtitle { text-align: center; font-size: 1.15em; color: #6b7a90; margin-bottom: 28px; }

/ ---- Stat cards (mini key-value row) ---- / .stat-row { display: flex; flex-wrap: wrap; gap: 14px; justify-content: center; margin-bottom: 32px; } .stat-card { background: #0f1923; border: 1px solid #1e3048; border-radius: 10px; padding: 14px 22px; min-width: 150px; text-align: center; cursor: default; } .stat-card .stat-value { font-size: 1.65em; font-weight: 700; color: #58a6ff; line-height: 1.15; } .stat-card .stat-label { font-size: 0.82em; color: #8b9ab5; margin-top: 4px; text-transform: uppercase; letter-spacing: 0.5px; }

/ ---- Section headings ---- / .section-heading { font-size: 1.35em; font-weight: 700; color: #e6edf3; border-bottom: 1px solid #1e3048; padding-bottom: 6px; margin-top: 36px; margin-bottom: 16px; }

/ ---- Horizontal bar chart (interactive) ---- / .chart-container { background: #0d1520; border: 1px solid #1a2a3e; border-radius: 12px; padding: 24px 28px 60px 28px; margin-bottom: 24px; display: flex; flex-direction: column; } .chart-title { font-size: 1.1em; font-weight: 700; color: #e2e8f0; margin-bottom: 4px; } .chart-subtitle { font-size: 0.88em; color: #7a8ba3; margin-bottom: 14px; line-height: 1.35; position: relative; z-index: 3; } .bar-group { margin-bottom: 18px; } .bar-label { font-size: 0.92em; color: #c5d0de; margin-bottom: 5px; font-weight: 600; }

/ NEW — replace with this / .bar-group { display: flex; flex-direction: column; align-items: center; flex: 1; } .bar-label { font-size: 0.78em; color: #8ea2b8; margin-top: 6px; text-align: center; font-weight: 600; } .bar-track { background: #162030; border-radius: 8px 8px 0 0; width: 100%; height: 120px; position: relative; display: flex; align-items: flex-end; overflow: hidden; } .bar-fill { width: 100%; border-radius: 6px 6px 0 0; transition: height 0.8s cubic-bezier(.22,.61,.36,1), box-shadow 0.3s ease; position: relative; min-height: 24px; display: flex; align-items: flex-start; justify-content: center; } .bar-fill:hover { box-shadow: 0 0 20px rgba(59, 130, 246, 0.3), inset 0 0 0 999px rgba(255,255,255,0.06); } .bar-value { position: absolute; top: 7px; left: 50%; transform: translateX(-50%); font-size: 0.78em; font-weight: 700; color: #000000; white-space: nowrap; line-height: 1; text-shadow: 0 1px 2px rgba(0,0,0,0.45); z-index: 2; pointer-events: none; } .bar-note { font-size: 0.78em; color: #5c6d82; margin-top: 6px; font-style: italic; }

/ NEW — add this / .charts-row { display: flex; gap: 16px; align-items: stretch; margin-bottom: 24px; } .chart-container-sm { background: #0d1520; border: 1px solid #1a2a3e; border-radius: 12px; padding: 18px 16px; flex: 1; display: flex; flex-direction: column; } .bars-row { display: flex; gap: 8px; align-items: flex-end; min-height: 150px; height: 150px; border-bottom: 1px solid #1e3048; margin-bottom: 8px; margin-top: 10px; } .bars-row-wide { gap: 16px; } .ood-filter-row { margin-bottom: 14px; position: relative; z-index: 4; } .chart-bars { margin-top: 8px; position: relative; z-index: 1; }

@media (max-width: 900px) { .charts-row { flex-direction: column; align-items: stretch; } }

/ ---- Filter buttons (CSS-only via radio hack) ---- / .filter-row { display: flex; flex-wrap: wrap; gap: 8px; margin-bottom: 18px; } .filter-row input[type="radio"] { display: none; } input[name="ood-filter"], input[name="cm-toggle"] { position: absolute; opacity: 0; width: 0; height: 0; pointer-events: none; } .filter-btn { display: inline-block; padding: 5px 14px; font-size: 0.82em; font-weight: 600; border-radius: 20px; border: 1px solid #253649; background: #111c2a; color: #8ea2b8; cursor: pointer; transition: background 0.2s, color 0.2s, border-color 0.2s; user-select: none; } .filter-btn:hover { background: #1a2d42; color: #c5d8e8; } #ood-both:checked ~ .ood-filter-row label[for="ood-both"], #ood-banking77:checked ~ .ood-filter-row label[for="ood-banking77"], #ood-clinc:checked ~ .ood-filter-row label[for="ood-clinc"] { background: #1f6feb; color: #fff; border-color: #1f6feb; } / Bar groups visibility controlled by radio state / .bar-group[data-dataset] { display: flex; } #ood-banking77:checked ~ .chart-bars .bar-group[data-dataset="clinc_oos"] { display: none; } #ood-clinc:checked ~ .chart-bars .bar-group[data-dataset="banking77"] { display: none; } #ood-both:checked ~ .chart-bars .bar-group[data-dataset] { display: flex; }

/ ---- Comparison table ---- / .comparison-table { width: 100%; border-collapse: collapse; font-size: 0.9em; margin-bottom: 16px; } .comparison-table th { background: #111c2a; color: #8ea2b8; font-weight: 700; text-transform: uppercase; font-size: 0.78em; letter-spacing: 0.6px; padding: 10px 14px; border-bottom: 2px solid #1e3048; text-align: left; } .comparison-table td { padding: 9px 14px; border-bottom: 1px solid #152232; color: #c5d0de; } .comparison-table tr:hover td { background: #111e2e; } .tag-janus { display: inline-block; background: #1a3a2a; color: #4ade80; border-radius: 6px; padding: 1px 8px; font-size: 0.85em; font-weight: 600; } .tag-v2 { display: inline-block; background: #1a2a3a; color: #60a5fa; border-radius: 6px; padding: 1px 8px; font-size: 0.85em; font-weight: 600; }

/ ---- Segmented stacked bar (single bar per panel) ---- / .stacked-bar-wrapper { margin-bottom: 12px; position: relative; } .stacked-bar-label { font-size: 0.85em; font-weight: 600; color: #8b9ab5; margin-bottom: 6px; } .stacked-bar { display: flex; height: 38px; border-radius: 8px; overflow: hidden; background: #162030; cursor: default; } .stacked-seg { height: 100%; transition: filter 0.2s ease, flex 0.3s ease; position: relative; } .stacked-seg:hover { filter: brightness(1.4) saturate(1.3); outline: 2px solid #fff; outline-offset: -2px; z-index: 5; } / Tooltip for stacked bar segments / .stacked-tooltip { display: none; position: absolute; left: 50%; transform: translateX(-50%); top: calc(100% + 6px); background: #0f1923; border: 1px solid #253649; border-radius: 8px; padding: 8px 14px; font-size: 0.82em; color: #e2e8f0; z-index: 20; white-space: nowrap; box-shadow: 0 6px 20px rgba(0,0,0,0.5); pointer-events: none; } .stacked-tooltip::before { content: ''; position: absolute; top: -6px; left: 50%; transform: translateX(-50%); border-left: 6px solid transparent; border-right: 6px solid transparent; border-bottom: 6px solid #253649; } .stacked-seg:hover .stacked-tooltip { display: block; } .stacked-tooltip .st-name { font-weight: 700; color: #4ade80; } .stacked-tooltip .st-val { color: #8ea2b8; }

/ ---- Confusion matrix panel toggle ---- / #cm-domain:checked ~ .confusion-toggle label[for="cm-domain"], #cm-action:checked ~ .confusion-toggle label[for="cm-action"] { background: #1f6feb; color: #fff; border-color: #1f6feb; } .confusion-toggle { display: flex; gap: 8px; margin-bottom: 18px; } .cm-panel[data-cm="domain"] { display: block; } .cm-panel[data-cm="action"] { display: none; } #cm-action:checked ~ .cm-panel[data-cm="domain"] { display: none; } #cm-action:checked ~ .cm-panel[data-cm="action"] { display: block; } #cm-domain:checked ~ .cm-panel[data-cm="domain"] { display: block; } #cm-domain:checked ~ .cm-panel[data-cm="action"] { display: none; }

/ ---- Confusion stacked bar ---- / .cm-stacked-bar { display: flex; height: 44px; border-radius: 10px; overflow: visible; background: #162030; position: relative; margin-bottom: 8px; } .cm-seg { height: 100%; position: relative; transition: filter 0.2s ease; cursor: default; / thin separator between segments / border-right: 1px solid rgba(0,0,0,0.35); } .cm-seg:last-child { border-right: none; } .cm-seg:hover { filter: brightness(1.45) saturate(1.3); z-index: 10; } / Tooltip on hover / .cm-seg .cm-tip { display: none; position: absolute; bottom: calc(100% + 8px); left: 50%; transform: translateX(-50%); background: #0f1923; border: 1px solid #253649; border-radius: 8px; padding: 10px 16px; font-size: 0.82em; color: #e2e8f0; z-index: 30; white-space: nowrap; box-shadow: 0 6px 24px rgba(0,0,0,0.55); pointer-events: none; } .cm-seg .cm-tip::after { content: ''; position: absolute; bottom: -6px; left: 50%; transform: translateX(-50%); border-left: 6px solid transparent; border-right: 6px solid transparent; border-top: 6px solid #253649; } .cm-seg:hover .cm-tip { display: block; } .cm-tip .tip-name { font-weight: 700; color: #58a6ff; } .cm-tip .tip-samples { color: #4ade80; } .cm-tip .tip-acc { color: #fbbf24; } .cm-tip .tip-miss { color: #f87171; } .cm-legend { display: flex; flex-wrap: wrap; gap: 10px 18px; margin-top: 10px; margin-bottom: 6px; } .cm-legend-item { display: flex; align-items: center; gap: 6px; font-size: 0.78em; color: #8b9ab5; } .cm-legend-swatch { width: 12px; height: 12px; border-radius: 3px; display: inline-block; }

/ ---- Code blocks ---- / details { background: #0d1520; border: 1px solid #1a2a3e; border-radius: 8px; margin-bottom: 10px; padding: 0; } details summary { cursor: pointer; padding: 12px 18px; font-weight: 600; color: #c5d0de; list-style: none; } details summary::-webkit-details-marker { display: none; } details summary::before { content: "▸ "; color: #58a6ff; } details[open] summary::before { content: "▾ "; } details > div { padding: 0 18px 16px; } </style>

<!-- ===== Hero ===== --> <div class="hero-banner"> <img src="assets/jane-janus-glitch.webp" alt="Jane Janus animated hero banner" width="980" /> </div>

<p class="subtitle">Hierarchical command understanding with state-aware runtime behavior for practical assistant workflows.</p>

<!-- ===== Stat Cards ===== --> <div class="stat-row"> <div class="stat-card"> <div class="stat-value">7.95M</div> <div class="stat-label">Parameters</div> </div> <div class="stat-card"> <div class="stat-value">82</div> <div class="stat-label">Runtime Turns</div> </div> <div class="stat-card"> <div class="stat-value">0</div> <div class="stat-label">Errors</div> </div> <div class="stat-card"> <div class="stat-value">25.3 ms</div> <div class="stat-label">Mean Latency</div> </div> <div class="stat-card"> <div class="stat-value">100%</div> <div class="stat-label">OOD Precision</div> </div> <div class="stat-card"> <div class="stat-value">30.6 MB</div> <div class="stat-label">Checkpoint</div> </div> </div>

🏛️ The Temple of Janus (Web Experience)

We have deployed a dedicated interactive environment to showcase the essence of JaneGPT-v2 Janus.

Note: This is a visual and technical walkthrough; it does not feature a live chat interface.

  • —[🔗 Enter the Experience](https://janus-web-demo.vercel.app/)
  • —Best Viewed On: Desktop (Chrome/Edge) for full hardware-accelerated 3D effects.

Quickstart (2 minutes)

<details open> <summary><strong>Install + first prediction</strong></summary> <div>

bash
pip install -r requirements.txt
python
from janegpt_v2_janus.inference import JaneGPTv3NLU

nlu = JaneGPTv3NLU(
    model_path="weights/janegpt_v2_janus.pt",
    tokenizer_path="weights/tokenizer.json",
)

state = {}
result = nlu.predict("set volume", state=state)
print(result)

if result.get("type") == "command":
    state = nlu.update_state(result, state)

</div> </details>

<details> <summary><strong>Runtime wrapper (recommended for assistant flows)</strong></summary> <div>

python
from runtime.jane_nlu_runtime import JaneNLURuntime

rt = JaneNLURuntime(base_dir=".")
state = {}

out, state = rt.handle_turn("set volume", state)
print(out)  # expected: clarify prompt for missing VALUE

out, state = rt.handle_turn("55", state)
print(out)  # expected: resolved local command

</div> </details>

<details> <summary><strong>Run bundled demos</strong></summary> <div>

bash
python examples/demo_inference.py
python examples/demo_runtime.py
python examples/demo_runtime_suite.py

</div> </details>

What You Get

  • —Single-pass multitask prediction: domain + action + BIO slots.
  • —Runtime-safe clarification loops for missing required slots.
  • —Stateful follow-ups (for example, "that is not enough" after a volume change).
  • —Local command routing with controlled chat fallback.
  • —Compact deployment footprint: ~30.62 MB checkpoint.

Model Architecture

Interactive Architecture Visualization

<style> / ---- Architecture Visualization ---- / .arch-container { background: linear-gradient(135deg, #0a0f1a 0%, #0d1520 100%); border: 1px solid #1a2a3e; border-radius: 12px; padding: 28px; margin-bottom: 24px; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }

.arch-flow { display: flex; flex-direction: column; gap: 12px; }

.arch-layer { background: #0d1520; border: 2px solid #1e3048; border-radius: 10px; padding: 18px; cursor: pointer; transition: all 0.3s ease; position: relative; overflow: hidden; }

.arch-layer::before { content: ''; position: absolute; top: 0; left: 0; right: 0; height: 2px; background: linear-gradient(90deg, #3b82f6, #8b5cf6, #ec4899, transparent); opacity: 0; transition: opacity 0.3s ease; }

.arch-layer:hover { background: #111c2e; border-color: #2d5aa6; box-shadow: 0 8px 24px rgba(59, 130, 246, 0.15); }

.arch-layer:hover::before { opacity: 1; }

.arch-layer-title { font-size: 1.05em; font-weight: 700; color: #58a6ff; margin-bottom: 10px; display: flex; align-items: center; justify-content: center; gap: 8px; text-align: center; }

.arch-layer-icon { display: none; }

.arch-layer-desc { font-size: 0.9em; color: #8b9ab5; line-height: 1.5; max-height: 0; overflow: hidden; transition: max-height 0.3s ease; }

.arch-layer.expanded .arch-layer-desc { max-height: 500px; }

.arch-layer-content { display: grid; grid-template-columns: repeat(auto-fit, minmax(200px, 1fr)); gap: 12px; margin-top: 12px; max-height: 0; overflow: hidden; transition: max-height 0.3s ease; }

.arch-layer.expanded .arch-layer-content { max-height: 600px; }

.arch-spec-item { background: #162030; border: 1px solid #253649; border-radius: 8px; padding: 10px 12px; font-size: 0.82em; text-align: center; }

.arch-spec-label { color: #4ade80; font-weight: 600; display: block; margin-bottom: 4px; }

.arch-spec-value { color: #c5d0de; font-family: 'Monaco', 'Courier New', monospace; }

.arch-arrow { text-align: center; color: #4ade80; font-size: 1.2em; padding: 4px 0; opacity: 0.6; }

.arch-tasks { display: grid; grid-template-columns: repeat(auto-fit, minmax(180px, 1fr)); gap: 12px; margin-top: 12px; }

.arch-task { background: linear-gradient(135deg, #1a2f4a 0%, #0f1f38 100%); border: 1px solid #253649; border-left: 3px solid #3b82f6; border-radius: 8px; padding: 12px; font-size: 0.85em; }

.arch-task.domain { border-left-color: #3b82f6; }

.arch-task.action { border-left-color: #8b5cf6; }

.arch-task.slot { border-left-color: #ec4899; }

.arch-task-name { font-weight: 700; color: #e2e8f0; margin-bottom: 6px; text-align: center; }

.arch-task-detail { color: #8b9ab5; font-size: 0.78em; line-height: 1.4; text-align: center; }

.arch-loss-box { background: #1a2a3a; border: 1px solid #1e3a4f; border-radius: 8px; padding: 14px; margin-top: 12px; font-family: 'Monaco', 'Courier New', monospace; font-size: 0.82em; color: #4ade80; overflow-x: auto; }

.arch-decision { background: #1a3a2a; border-left: 3px solid #10b981; border-radius: 6px; padding: 10px; margin-top: 8px; font-size: 0.82em; color: #a7f3d0; }

@media (max-width: 900px) { .arch-layer-content { grid-template-columns: 1fr; }

.arch-tasks { grid-template-columns: 1fr; } } </style>

<div class="arch-container">

<div class="arch-layer" onclick="this.classList.toggle('expanded')"> <div class="arch-layer-title"> <span class="arch-layer-icon">🔤</span>

  1. 1.Tokenization & Embedding Layer </div> <div class="arch-layer-desc"> Input text is converted to token IDs and projected into a 256-dimensional embedding space. </div> <div class="arch-layer-content"> <div class="arch-spec-item"> <span class="arch-spec-label">Tokenizer</span> <span class="arch-spec-value">BPE, vocab=8,192</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Max Length</span> <span class="arch-spec-value">96 tokens</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Output Shape</span> <span class="arch-spec-value">(batch, 96, 256)</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Embedding</span> <span class="arch-spec-value">8192 → 256 dim</span> </div> </div> </div>

<div class="arch-arrow">↓</div>

<div class="arch-layer" onclick="this.classList.toggle('expanded')"> <div class="arch-layer-title"> <span class="arch-layer-icon"></span>

  1. 1.Transformer Backbone (8 Blocks) </div> <div class="arch-layer-desc"> Bidirectional attention layers with residual connections. Each block processes hidden states through grouped query attention and feed-forward networks. </div> <div class="arch-layer-content"> <div class="arch-spec-item"> <span class="arch-spec-label">Attention Type</span> <span class="arch-spec-value">Grouped Query (GQA)</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Query Heads</span> <span class="arch-spec-value">8 heads</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">KV Heads</span> <span class="arch-spec-value">4 heads (2:1)</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Head Dimension</span> <span class="arch-spec-value">32 (256÷8)</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Position Embedding</span> <span class="arch-spec-value">RoPE</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">FFN Expansion</span> <span class="arch-spec-value">256 → 672 → 256</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">FFN Activation</span> <span class="arch-spec-value">SwiGLU</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Normalization</span> <span class="arch-spec-value">RMSNorm</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Causal Masking</span> <span class="arch-spec-value">OFF (bidirectional)</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Dropout Rate</span> <span class="arch-spec-value">0.1</span> </div> </div> <div class="arch-decision"> Grouped Query Attention reduces KV cache 50% while maintaining quality </div> </div>

<div class="arch-arrow">↓</div>

<div class="arch-layer" onclick="this.classList.toggle('expanded')"> <div class="arch-layer-title"> <span class="arch-layer-icon"></span>

  1. 1.Multi-Task Prediction Heads (Parallel) </div> <div class="arch-layer-desc"> Three independent classification heads process the backbone output simultaneously for domain, action, and slot predictions. </div> <div class="arch-tasks"> <div class="arch-task domain"> <div class="arch-task-name">Domain Head</div> <div class="arch-task-detail"> <strong>Input:</strong> Last token (pooled)<br/> <strong>Arch:</strong> Linear(256) → GELU → Dropout → Linear(10)<br/> <strong>Output:</strong> 10 classes </div> </div> <div class="arch-task action"> <div class="arch-task-name">Action Head</div> <div class="arch-task-detail"> <strong>Input:</strong> Last token (pooled)<br/> <strong>Arch:</strong> Linear(256) → GELU → Dropout → Linear(33)<br/> <strong>Output:</strong> 33 classes </div> </div> <div class="arch-task slot"> <div class="arch-task-name">Slot Head</div> <div class="arch-task-detail"> <strong>Input:</strong> All tokens<br/> <strong>Arch:</strong> Linear(256) → Linear(15 BIO)<br/> <strong>Output:</strong> 15 labels/token </div> </div> </div> </div>

<div class="arch-arrow">↓</div>

<div class="arch-layer" onclick="this.classList.toggle('expanded')"> <div class="arch-layer-title"> <span class="arch-layer-icon"></span>

  1. 1.Output & Post-Processing </div> <div class="arch-layer-desc"> Raw logits are converted to predictions. For slots, BIO tags are decoded into semantic spans. </div> <div class="arch-layer-content"> <div class="arch-spec-item"> <span class="arch-spec-label">Domain Output</span> <span class="arch-spec-value">10 classes</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Action Output</span> <span class="arch-spec-value">33 classes</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Slots Decoder</span> <span class="arch-spec-value">BIO → Spans</span> </div> <div class="arch-spec-item"> <span class="arch-spec-label">Confidence</span> <span class="arch-spec-value">Softmax scores</span> </div> </div> </div>

</div>

Training Objective

<div class="arch-container"> <div class="arch-layer expanded"> <div class="arch-layer-title"> <span class="arch-layer-icon"></span> Weighted Multi-Task Loss </div> <div class="arch-loss-box"> loss = 1.0 × Ldomain + 1.0 × Laction + 1.5 × L_slots

Where: Ldomain = CrossEntropy(domainlogits, domainlabels) Laction = CrossEntropy(actionlogits, actionlabels) Lslots = CrossEntropy(slotlogits, slotlabels) with ignoreindex=-100 (padding) </div> <div class="arch-decision"> Slot weight (1.5×) reflects higher complexity of sequence tagging vs. classification </div> </div> </div>

Architecture Specifications

ComponentConfigurationDetails
Backbone TypeTransformer (GPT-style)Bidirectional, non-causal attention
Vocabulary Size8,192BPE tokenization
Embedding Dim256Token + Rotary Position embeddings
Attention Heads8 Query, 4 KVGrouped Query Attention (GQA) for efficiency
Head Dimension32per headdim = embeddim / num_heads
Transformer Blocks8 LayersEach with Attn + FFN + Residuals
Feed-Forward Hidden672SwiGLU gate activation
Position EncodingRoPERotary Position Embeddings (theta=10000)
NormalizationRMSNormPre-layer normalization
Max Sequence Length96 tokensApproximately 60-80 words
Dropout Rate0.1Applied during training
Total Parameters7,949,626All trainable
Parameter BreakdownBackbone: 7.80M, Task Heads: 146KEfficient multitask design

Task Configuration

TaskTypeClassesArchitecture
Domain ClassificationSequence-level10 domainsPooled → Linear(256) → GELU → Linear(10)
Action ClassificationSequence-level33 actionsPooled → Linear(256) → GELU → Linear(33)
Slot TaggingToken-level15 BIO labelsPer-token → Linear(256) → Linear(15)

Benchmark Results

<div class="charts-row">

<div class="chart-container-sm"> <div class="chart-title">Runtime reliability</div> <div class="chart-subtitle">82-turn suite</div> <div class="bars-row"> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:100%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">82</span></div></div> <div class="bar-label">Turns</div> </div> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:81.7%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">67</span></div></div> <div class="bar-label">Local</div> </div> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:14.6%; background:linear-gradient(0deg,#ec4899,#f472b6);"><span class="bar-value">12</span></div></div> <div class="bar-label">Clarify</div> </div> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:1%; background:linear-gradient(0deg,#9ca3af,#d1d5db);"><span class="bar-value">0</span></div></div> <div class="bar-label">Errors</div> </div> </div> <div class="bar-note">fair_benchmarks.json</div> </div>

<div class="chart-container-sm"> <div class="chart-title">Predict latency</div> <div class="chart-subtitle">CUDA · batch=1 · lower is better</div> <div class="bars-row"> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:63.3%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">25.3ms</span></div></div> <div class="bar-label">P·mean</div> </div> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:86.5%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">34.6ms</span></div></div> <div class="bar-label">P·p95</div> </div> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:88.4%; background:linear-gradient(0deg,#ec4899,#f472b6);"><span class="bar-value">35.4ms</span></div></div> <div class="bar-label">Fwd·mean</div> </div> <div class="bar-group"> <div class="bar-track"><div class="bar-fill" style="height:91.8%; background:linear-gradient(0deg,#a78bfa,#c4b5fd);"><span class="bar-value">36.7ms</span></div></div> <div class="bar-label">Fwd·p95</div> </div> </div> <div class="bar-note">janusmodelreport.json</div> </div>

</div>

<div class="chart-container"> <div class="chart-title">OOD rejection quality</div> <div class="chart-subtitle">Schema-agnostic · hover values</div>

<input type="radio" name="ood-filter" id="ood-both" checked /> <input type="radio" name="ood-filter" id="ood-banking77" /> <input type="radio" name="ood-filter" id="ood-clinc" /> <div class="ood-filter-row filter-row"> <label class="filter-btn" for="ood-both">Both</label> <label class="filter-btn" for="ood-banking77">BANKING77</label> <label class="filter-btn" for="ood-clinc">CLINC</label> </div> <div class="chart-bars"> <div class="bars-row bars-row-wide"> <div class="bar-group" data-dataset="banking77"> <div class="bar-track"><div class="bar-fill" style="height:87.8%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">87.8%</span></div></div> <div class="bar-label">B77 F1</div> </div> <div class="bar-group" data-dataset="banking77"> <div class="bar-track"><div class="bar-fill" style="height:100%; background:linear-gradient(0deg,#ec4899,#f472b6);"><span class="bar-value">100%</span></div></div> <div class="bar-label">B77 Prec</div> </div> <div class="bar-group" data-dataset="banking77"> <div class="bar-track"><div class="bar-fill" style="height:78.25%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">78.3%</span></div></div> <div class="bar-label">B77 Rec</div> </div> <div class="bar-group" data-dataset="clincoos"> <div class="bar-track"><div class="bar-fill" style="height:79.23%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">79.2%</span></div></div> <div class="bar-label">CL F1</div> </div> <div class="bar-group" data-dataset="clincoos"> <div class="bar-track"><div class="bar-fill" style="height:100%; background:linear-gradient(0deg,#ec4899,#f472b6);"><span class="bar-value">100%</span></div></div> <div class="bar-label">CL Prec</div> </div> <div class="bar-group" data-dataset="clincoos"> <div class="bar-track"><div class="bar-fill" style="height:65.6%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">65.6%</span></div></div> <div class="bar-label">CL Rec</div> </div> </div> </div> <div class="bar-note">fairbenchmarks.json</div> </div>

</div>

Comprehensive Benchmark Summary

<div class="chart-container"> <div class="chart-title">Full Benchmark Evidence</div> <div class="chart-subtitle">All values from real holdout evaluations — no synthetic or inflated numbers</div>

<table class="comparison-table"> <thead> <tr> <th>Metric</th> <th>Detail</th> <th><span class="tag-v2">Jane v2</span></th> <th><span class="tag-janus">Janus</span></th> </tr> </thead> <tbody> <tr> <td><strong>Speed (mean latency)</strong></td> <td>CUDA, batch=1</td> <td>31.60 ms</td> <td><strong>25.31 ms</strong></td> </tr> <tr> <td><strong>Throughput</strong></td> <td>CUDA, single GPU</td> <td>32 pred/sec</td> <td>Stable across 82 turns, 0 errors</td> </tr> <tr> <td><strong>OOD F1</strong></td> <td>BANKING77</td> <td><strong>94.31%</strong></td> <td>87.80%</td> </tr> <tr> <td><strong>OOD F1</strong></td> <td>CLINC OOS</td> <td><strong>89.16%</strong></td> <td>79.23%</td> </tr> <tr> <td><strong>OOD Precision</strong></td> <td>BANKING77</td> <td>99.35%</td> <td><strong>100.00%</strong></td> </tr> <tr> <td><strong>OOD Precision</strong></td> <td>CLINC OOS</td> <td>99.14%</td> <td><strong>100.00%</strong></td> </tr> <tr> <td><strong>OOD Recall</strong></td> <td>BANKING77</td> <td><strong>89.75%</strong></td> <td>78.25%</td> </tr> <tr> <td><strong>OOD Recall</strong></td> <td>CLINC OOS</td> <td><strong>81.00%</strong></td> <td>65.60%</td> </tr> <tr> <td><strong>Validation Accuracy</strong></td> <td>Domain (best epoch)</td> <td>—</td> <td><strong>99.83%</strong></td> </tr> <tr> <td><strong>Validation Accuracy</strong></td> <td>Action (best epoch)</td> <td>—</td> <td><strong>99.87%</strong></td> </tr> <tr> <td><strong>Validation Accuracy</strong></td> <td>Domain+Action pair (best epoch)</td> <td>—</td> <td><strong>99.83%</strong></td> </tr> <tr> <td><strong>Slot Extraction F1</strong></td> <td>All 15 slot types</td> <td>—</td> <td><strong>1.000 (100%)</strong></td> </tr> <tr> <td><strong>Training Loss</strong></td> <td>Epoch 1 → 4</td> <td>—</td> <td>0.060 → 0.020 → 0.002 → 0.001</td> </tr> <tr> <td><strong>Validation Loss</strong></td> <td>Epoch 1 → 3</td> <td>—</td> <td>0.0153 → 0.0116 → 0.0115 (stable)</td> </tr> <tr> <td><strong>Runtime Reliability</strong></td> <td>82-turn conversation test</td> <td>—</td> <td><strong>0 errors, 0 crashes</strong></td> </tr> <tr> <td><strong>Domain Confusion</strong></td> <td>10 domains</td> <td>—</td> <td>99%+ per-domain, minimal cross-confusion</td> </tr> <tr> <td><strong>Action Confusion</strong></td> <td>33 actions</td> <td>—</td> <td>Perfect diagonal, no action commonly confused</td> </tr> </tbody> </table> </div>

Live Output Shapes (click to expand)

<details> <summary><strong>Command output</strong></summary> <div>

json
{
  "type": "command",
  "domain": "apps",
  "action": "launch",
  "slots": {
    "APP_NAME": {
      "text": "chrome",
      "start": 5,
      "end": 11,
      "confidence": 0.999
    }
  },
  "confidence": 0.97,
  "route": "local"
}

</div> </details>

<details> <summary><strong>Clarification output</strong></summary> <div>

json
{
  "type": "clarify",
  "question": "What value should I set it to?",
  "debug": {
    "domain": "volume",
    "action": "set",
    "reason": "missing_VALUE"
  }
}

</div> </details>

<details> <summary><strong>Label schema</strong></summary> <div>

  • —Domains (10): volume, brightness, media, apps, browser, productivity, screen, window, system, conversation
  • —Actions (33): up, down, set, mute, unmute, play, pause, next, previous, launch, close, switch, search, setreminder, screenshot, read, explain, undo, quit, chat, minimize, maximize, restore, focus, copy, paste, cut, lock, sleep, wifion, wifioff, bluetoothon, bluetooth_off
  • —Slot labels (BIO, 15): VALUE, APPNAME, QUERY, DURATION, TIME, WINDOWNAME, TEXT

</div> </details>

Visual Benchmark Evidence

<p align="center"> <img src="reports/lossperepoch.png" alt="Train and validation loss" width="860" /> </p>

<p align="center"> <img src="reports/trainlosssmoothed.png" alt="Smoothed train loss" width="860" /> </p>

<p align="center"> <img src="reports/valslotf1.png" alt="Validation slot F1" width="860" /> </p>

Confusion Matrix — Interactive Breakdown

<!-- FIX: radio inputs must be SIBLINGS of both .confusion-toggle AND .cm-panel divs so the CSS sibling combinator (~) can reach them all from the same parent level --> <div class="chart-container"> <div class="chart-title">Per-Class True vs Predicted</div> <div class="chart-subtitle">Single stacked bar per head — segment width = sample ratio. Hover any segment for details.</div>

<input type="radio" name="cm-toggle" id="cm-domain" checked /> <input type="radio" name="cm-toggle" id="cm-action" /> <div class="confusion-toggle"> <label class="filter-btn" for="cm-domain">Domains (10)</label> <label class="filter-btn" for="cm-action">Actions (33)</label> </div>

<div class="cm-panel" data-cm="domain">

<div class="stacked-bar-label">Domain Sample Distribution &mdash; 3,110 total samples &mdash; hover each segment</div> <div class="cm-stacked-bar"> <div class="cm-seg" style="flex:430; background:#3b82f6; border-radius:10px 0 0 10px;"><div class="cm-tip"><span class="tip-name">volume</span><br/><span class="tip-samples">430 samples (13.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:250; background:#f59e0b;"><div class="cm-tip"><span class="tip-name">brightness</span><br/><span class="tip-samples">250 samples (8.0%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:340; background:#10b981;"><div class="cm-tip"><span class="tip-name">media</span><br/><span class="tip-samples">340 samples (10.9%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:250; background:#ef4444;"><div class="cm-tip"><span class="tip-name">apps</span><br/><span class="tip-samples">250 samples (8.0%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:120; background:#8b5cf6;"><div class="cm-tip"><span class="tip-name">browser</span><br/><span class="tip-samples">120 samples (3.9%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:340; background:#ec4899;"><div class="cm-tip"><span class="tip-name">productivity</span><br/><span class="tip-samples">340 samples (10.9%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:120; background:#14b8a6;"><div class="cm-tip"><span class="tip-name">screen</span><br/><span class="tip-samples">120 samples (3.9%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:340; background:#f97316;"><div class="cm-tip"><span class="tip-name">window</span><br/><span class="tip-samples">340 samples (10.9%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:580; background:#6366f1;"><div class="cm-tip"><span class="tip-name">system</span><br/><span class="tip-samples">580 samples (18.6%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:340; background:#06b6d4; border-radius:0 10px 10px 0;"><div class="cm-tip"><span class="tip-name">conversation</span><br/><span class="tip-samples">340 samples (10.9%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> </div>

<div class="cm-legend"> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#3b82f6;"></span>volume</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#f59e0b;"></span>brightness</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#10b981;"></span>media</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#ef4444;"></span>apps</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#8b5cf6;"></span>browser</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#ec4899;"></span>productivity</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#14b8a6;"></span>screen</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#f97316;"></span>window</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#6366f1;"></span>system</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#06b6d4;"></span>conversation</div> </div>

<div class="bar-note">Source: validation set confusion matrix — segment widths proportional to sample count</div> </div>

<div class="cm-panel" data-cm="action">

<div class="stacked-bar-label">Action Sample Distribution &mdash; 3,205 total samples &mdash; hover each segment</div> <div class="cm-stacked-bar"> <div class="cm-seg" style="flex:170; background:#3b82f6; border-radius:10px 0 0 10px;"><div class="cm-tip"><span class="tip-name">up</span><br/><span class="tip-samples">170 samples (5.3%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:165; background:#2563eb;"><div class="cm-tip"><span class="tip-name">down</span><br/><span class="tip-samples">165 samples (5.1%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:170; background:#1d4ed8;"><div class="cm-tip"><span class="tip-name">set</span><br/><span class="tip-samples">170 samples (5.3%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#7c3aed;"><div class="cm-tip"><span class="tip-name">mute</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#6d28d9;"><div class="cm-tip"><span class="tip-name">unmute</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#10b981;"><div class="cm-tip"><span class="tip-name">play</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#059669;"><div class="cm-tip"><span class="tip-name">pause</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#047857;"><div class="cm-tip"><span class="tip-name">next</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#065f46;"><div class="cm-tip"><span class="tip-name">previous</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#ef4444;"><div class="cm-tip"><span class="tip-name">launch</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#dc2626;"><div class="cm-tip"><span class="tip-name">close</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#b91c1c;"><div class="cm-tip"><span class="tip-name">switch</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#f59e0b;"><div class="cm-tip"><span class="tip-name">search</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#d97706;"><div class="cm-tip"><span class="tip-name">setreminder</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#ec4899;"><div class="cm-tip"><span class="tip-name">screenshot</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#db2777;"><div class="cm-tip"><span class="tip-name">read</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#be185d;"><div class="cm-tip"><span class="tip-name">explain</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#14b8a6;"><div class="cm-tip"><span class="tip-name">undo</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#0d9488;"><div class="cm-tip"><span class="tip-name">quit</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#0f766e;"><div class="cm-tip"><span class="tip-name">chat</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#f97316;"><div class="cm-tip"><span class="tip-name">minimize</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#ea580c;"><div class="cm-tip"><span class="tip-name">maximize</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#c2410c;"><div class="cm-tip"><span class="tip-name">restore</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#6366f1;"><div class="cm-tip"><span class="tip-name">focus</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#4f46e5;"><div class="cm-tip"><span class="tip-name">copy</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#4338ca;"><div class="cm-tip"><span class="tip-name">paste</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#06b6d4;"><div class="cm-tip"><span class="tip-name">cut</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#0891b2;"><div class="cm-tip"><span class="tip-name">lock</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#0e7490;"><div class="cm-tip"><span class="tip-name">sleep</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#84cc16;"><div class="cm-tip"><span class="tip-name">wifion</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#65a30d;"><div class="cm-tip"><span class="tip-name">wifioff</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#a855f7;"><div class="cm-tip"><span class="tip-name">bluetoothon</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> <div class="cm-seg" style="flex:90; background:#9333ea; border-radius:0 10px 10px 0;"><div class="cm-tip"><span class="tip-name">bluetooth_off</span><br/><span class="tip-samples">90 samples (2.8%)</span><br/><span class="tip-acc">Accuracy: 100%</span><br/><span class="tip-miss">Misclassified: 0</span></div></div> </div>

<div class="cm-legend"> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#3b82f6;"></span>up</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#2563eb;"></span>down</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#1d4ed8;"></span>set</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#7c3aed;"></span>mute</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#6d28d9;"></span>unmute</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#10b981;"></span>play</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#059669;"></span>pause</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#047857;"></span>next</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#065f46;"></span>previous</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#ef4444;"></span>launch</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#dc2626;"></span>close</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#b91c1c;"></span>switch</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#f59e0b;"></span>search</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#d97706;"></span>setreminder</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#ec4899;"></span>screenshot</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#db2777;"></span>read</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#be185d;"></span>explain</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#14b8a6;"></span>undo</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#0d9488;"></span>quit</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#0f766e;"></span>chat</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#f97316;"></span>minimize</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#ea580c;"></span>maximize</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#c2410c;"></span>restore</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#6366f1;"></span>focus</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#4f46e5;"></span>copy</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#4338ca;"></span>paste</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#06b6d4;"></span>cut</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#0891b2;"></span>lock</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#0e7490;"></span>sleep</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#84cc16;"></span>wifion</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#65a30d;"></span>wifioff</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#a855f7;"></span>bluetoothon</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#9333ea;"></span>bluetooth_off</div> </div>

<div class="bar-note">Source: validation set confusion matrix — segment widths proportional to sample count</div> </div>

</div>

<details> <summary><strong>View original confusion matrix images</strong></summary> <div>

<p align="center"> <img src="reports/confusiondomainval.png" alt="Domain confusion matrix" width="860" /> </p>

<p align="center"> <img src="reports/confusionactionval.png" alt="Action confusion matrix" width="860" /> </p>

</div> </details>

<details> <summary><strong>Additional diagnostics</strong></summary> <div>

<p align="center"> <img src="reports/lr_schedule.png" alt="Learning rate schedule" width="860" /> </p>

<p align="center"> <img src="reports/epoch_time.png" alt="Epoch time profile" width="860" /> </p>

<p align="center"> <img src="reports/trainlossraw.png" alt="Raw training loss" width="860" /> </p>

</div> </details>

Upload-Ready Layout

text
.
|- README.md
|- .gitattributes
|- LICENSE
|- requirements.txt
|- assets/
|  |- jane-janus-glitch.webp
|- janegpt_v2_janus/
|  |- __init__.py
|  |- architecture.py
|  |- dataset.py
|  |- inference.py
|  |- labels.py
|  |- multitask.py
|- runtime/
|  |- jane_nlu_runtime.py
|- examples/
|  |- demo_inference.py
|  |- demo_runtime.py
|  |- demo_runtime_suite.py
|- weights/
|  |- janegpt_v2_janus.pt
|  |- tokenizer.json
|- reports/
|  |- fair_benchmarks.json
|  |- fair_benchmarks.md
|  |- janus_model_report.json
|  |- janus_model_report.md
|  |- public_benchmarks.json
|  |- *.png benchmark visuals

Limitations

  • —English-focused command language.
  • —Command NLU model, not an open-domain generative chatbot.
  • —MASSIVE and SNIPS mapped-intent accuracy is excluded from headline claims because mapping coverage is partial.

Use Cases

  • —Virtual assistant command routing
  • —Smart home intent classification
  • —Voice command understanding
  • —Chatbot intent detection
  • —Edge device deployment (small enough for embedded systems)

Part of the JANE Project

JANE — a fully offline, privacy-first AI voice assistant.

🔗 JANE AI Assistant on GitHub 🔗 JaneGPT-v2 on GitHub

Created By

Ravindu Senanayake

Built from scratch — architecture, tokenizer, and training pipeline designed and implemented by the author.

![GitHub](https://github.com/Ravindu-S)

License

Apache-2.0 (see LICENSE).