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JaneGPT-v2-Janus
RavinduSen/JaneGPT-v2-Janus
JaneGPT-v2-Janus is a text classification model from RavinduSen. Use it when you need a label for a piece of text. It is set up for pytorch. The card lists the license as apache-2.0.
All-time downloads
447
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Parameters
7.9M
132 MB on disk
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2
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.pt32.1 MB · 47%
At a glance
- Task
- Text Classification
- Library
- pytorch
- License
- apache-2.0
- Model type
- janegpt_v2_janus
- Access
- Public
- Created
- Apr 12, 2026
- Updated
- Oct 7, 2026
- SHA
- 362c0359
Try a prompt
Model card
From the Hugging Face model README
JaneGPT v2 Janus - Intent Classification Model
<p align="center"> <a href="https://janus-web-demo.vercel.app/"> <img src="https://img.shields.io/badge/Launch-Temple_of_Janus-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">15.4 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>Update (Oct 2026): improved release. Higher accuracy and several bug fixes. The tokenizer now normalises casing, so capitalised speech-to-text output is handled like lowercase text; the model was retrained on cleaner, broader data; and the runtime handles wake phrases, cancelled questions and "close it"-style follow-ups. Use the new
weights/tokenizer.jsontogether with the new weights.
🏛️ 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
- 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>pip install -r requirements.txt
from janegpt_v2_janus import JaneGPTJanusNLU
nlu = JaneGPTJanusNLU(
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)
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
python examples/demo_inference.py
python examples/demo_runtime.py
python examples/demo_runtime_suite.py
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: ~30.61 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. 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> 2. 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> 3. 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> 4. 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 × L_domain + 1.0 × L_action + 1.5 × L_slotsWhere: L_domain = CrossEntropy(domain_logits, domain_labels) L_action = CrossEntropy(action_logits, action_labels) L_slots = CrossEntropy(slot_logits, slot_labels) with ignore_index=-100 (padding) </div> <div class="arch-decision"> Slot weight (1.5×) reflects higher complexity of sequence tagging vs. classification </div>
</div> </div>Architecture Specifications
| Component | Configuration | Details |
|---|---|---|
| Backbone Type | Transformer (GPT-style) | Bidirectional, non-causal attention |
| Vocabulary Size | 8,192 | BPE tokenization (NFKC + lowercase normalizer) |
| Embedding Dim | 256 | Token + Rotary Position embeddings |
| Attention Heads | 8 Query, 4 KV | Grouped Query Attention (GQA) for efficiency |
| Head Dimension | 32 | per head_dim = embed_dim / num_heads |
| Transformer Blocks | 8 Layers | Each with Attn + FFN + Residuals |
| Feed-Forward Hidden | 672 | SwiGLU gate activation |
| Position Encoding | RoPE | Rotary Position Embeddings (theta=10000) |
| Normalization | RMSNorm | Pre-layer normalization |
| Max Sequence Length | 96 tokens | Approximately 60-80 words |
| Dropout Rate | 0.1 | Applied during training |
| Total Parameters | 7,949,626 | All trainable |
| Parameter Breakdown | Backbone: 7.80M, Task Heads: 146K | Efficient multitask design |
Task Configuration
| Task | Type | Classes | Architecture |
|---|---|---|---|
| Domain Classification | Sequence-level | 10 domains | Pooled → Linear(256) → GELU → Linear(10) |
| Action Classification | Sequence-level | 33 actions | Pooled → Linear(256) → GELU → Linear(33) |
| Slot Tagging | Token-level | 15 BIO labels | Per-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:13.4%; background:linear-gradient(0deg,#ec4899,#f472b6);"><span class="bar-value">11</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:38.4%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">15.4ms</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:59.3%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">23.7ms</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:55.6%; background:linear-gradient(0deg,#ec4899,#f472b6);"><span class="bar-value">22.2ms</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:71.7%; background:linear-gradient(0deg,#a78bfa,#c4b5fd);"><span class="bar-value">28.7ms</span></div></div> <div class="bar-label">Fwd·p95</div> </div> </div> <div class="bar-note">janus_model_report.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:97.30%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">97.3%</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:94.75%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">94.8%</span></div></div> <div class="bar-label">B77 Rec</div> </div> <div class="bar-group" data-dataset="clinc_oos"> <div class="bar-track"><div class="bar-fill" style="height:94.18%; background:linear-gradient(0deg,#3b82f6,#60a5fa);"><span class="bar-value">94.2%</span></div></div> <div class="bar-label">CL F1</div> </div> <div class="bar-group" data-dataset="clinc_oos"> <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="clinc_oos"> <div class="bar-track"><div class="bar-fill" style="height:89.00%; background:linear-gradient(0deg,#8b5cf6,#a78bfa);"><span class="bar-value">89.0%</span></div></div> <div class="bar-label">CL Rec</div> </div> </div> </div> <div class="bar-note">fair_benchmarks.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>15.37 ms</strong></td> </tr> <tr> <td><strong>Throughput</strong></td> <td>CUDA, single GPU</td> <td>32 pred/sec</td> <td><strong>65 pred/sec</strong>, 0 errors across 82 turns</td> </tr> <tr> <td><strong>OOD F1</strong></td> <td>BANKING77</td> <td>94.31%</td> <td><strong>97.30%</strong></td> </tr> <tr> <td><strong>OOD F1</strong></td> <td>CLINC OOS</td> <td>89.16%</td> <td><strong>94.18%</strong></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>89.75%</td> <td><strong>94.75%</strong></td> </tr> <tr> <td><strong>OOD Recall</strong></td> <td>CLINC OOS</td> <td>81.00%</td> <td><strong>89.00%</strong></td> </tr> <tr> <td><strong>Validation Accuracy</strong></td> <td>Domain (best epoch)</td> <td>—</td> <td><strong>98.87%</strong></td> </tr> <tr> <td><strong>Validation Accuracy</strong></td> <td>Action (best epoch)</td> <td>—</td> <td><strong>98.47%</strong></td> </tr> <tr> <td><strong>Validation Accuracy</strong></td> <td>Domain+Action pair (best epoch)</td> <td>—</td> <td><strong>98.41%</strong></td> </tr> <tr> <td><strong>Slot Extraction F1</strong></td> <td>All 15 slot types</td> <td>—</td> <td><strong>0.993 (99.33%)</strong></td> </tr> <tr> <td><strong>Training Loss</strong></td> <td>Epoch 1 → 6</td> <td>—</td> <td>0.570 → 0.054 → 0.019 → 0.009 → 0.007 → 0.006</td> </tr> <tr> <td><strong>Validation Loss</strong></td> <td>Epoch 1 → 6</td> <td>—</td> <td>0.1992 → 0.1086 → 0.1283 → 0.1877 → 0.1434 → 0.1301 (selected epoch)</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>94.7%+ per-domain, minimal cross-confusion</td> </tr> <tr> <td><strong>Action Confusion</strong></td> <td>33 actions</td> <td>—</td> <td>Strong diagonal: 25 of 33 actions at 99%+, lowest minimize at 90.6%</td> </tr> </tbody> </table> </div>Live Output Shapes (click to expand)
<details> <summary><strong>Command output</strong></summary> <div>{
"type": "command",
"domain": "apps",
"action": "launch",
"slots": {
"APP_NAME": {
"text": "chrome",
"start": 5,
"end": 11,
"confidence": 0.999
}
},
"confidence": 0.97,
"route": "local"
}
{
"type": "clarify",
"question": "What value should I set it to?",
"debug": {
"domain": "volume",
"action": "set",
"reason": "missing_VALUE"
}
}
- 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, set_reminder, screenshot, read, explain, undo, quit, chat, minimize, maximize, restore, focus, copy, paste, cut, lock, sleep, wifi_on, wifi_off, bluetooth_on, bluetooth_off
- Slot labels (BIO, 15): VALUE, APP_NAME, QUERY, DURATION, TIME, WINDOW_NAME, TEXT
Visual Benchmark Evidence
<p align="center"> <img src="reports/loss_per_epoch.png" alt="Train and validation loss" width="860" /> </p> <p align="center"> <img src="reports/train_loss_smoothed.png" alt="Smoothed train loss" width="860" /> </p> <p align="center"> <img src="reports/val_slot_f1.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 — 3,265 total samples — hover each segment</div> <div class="cm-stacked-bar"> <div class="cm-seg" style="flex:453; background:#3b82f6; border-radius:10px 0 0 10px;"><div class="cm-tip"><span class="tip-name">volume</span><br/><span class="tip-samples">453 samples (13.9%)</span><br/><span class="tip-acc">Accuracy: 99.8%</span><br/><span class="tip-miss">Misclassified: 1</span></div></div> <div class="cm-seg" style="flex:349; background:#f59e0b;"><div class="cm-tip"><span class="tip-name">brightness</span><br/><span class="tip-samples">349 samples (10.7%)</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:372; background:#10b981;"><div class="cm-tip"><span class="tip-name">media</span><br/><span class="tip-samples">372 samples (11.4%)</span><br/><span class="tip-acc">Accuracy: 99.5%</span><br/><span class="tip-miss">Misclassified: 2</span></div></div> <div class="cm-seg" style="flex:266; background:#ef4444;"><div class="cm-tip"><span class="tip-name">apps</span><br/><span class="tip-samples">266 samples (8.1%)</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:102; background:#8b5cf6;"><div class="cm-tip"><span class="tip-name">browser</span><br/><span class="tip-samples">102 samples (3.1%)</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:384; background:#ec4899;"><div class="cm-tip"><span class="tip-name">productivity</span><br/><span class="tip-samples">384 samples (11.8%)</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:68; background:#14b8a6;"><div class="cm-tip"><span class="tip-name">screen</span><br/><span class="tip-samples">68 samples (2.1%)</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:234; background:#f97316;"><div class="cm-tip"><span class="tip-name">window</span><br/><span class="tip-samples">234 samples (7.2%)</span><br/><span class="tip-acc">Accuracy: 97.9%</span><br/><span class="tip-miss">Misclassified: 5</span></div></div> <div class="cm-seg" style="flex:542; background:#6366f1;"><div class="cm-tip"><span class="tip-name">system</span><br/><span class="tip-samples">542 samples (16.6%)</span><br/><span class="tip-acc">Accuracy: 99.4%</span><br/><span class="tip-miss">Misclassified: 3</span></div></div> <div class="cm-seg" style="flex:495; background:#06b6d4; border-radius:0 10px 10px 0;"><div class="cm-tip"><span class="tip-name">conversation</span><br/><span class="tip-samples">495 samples (15.2%)</span><br/><span class="tip-acc">Accuracy: 94.7%</span><br/><span class="tip-miss">Misclassified: 26</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 — 3,265 total samples — hover each segment</div> <div class="cm-stacked-bar"> <div class="cm-seg" style="flex:234; background:#3b82f6; border-radius:10px 0 0 10px;"><div class="cm-tip"><span class="tip-name">up</span><br/><span class="tip-samples">234 samples (7.2%)</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:227; background:#2563eb;"><div class="cm-tip"><span class="tip-name">down</span><br/><span class="tip-samples">227 samples (7.0%)</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:195; background:#1d4ed8;"><div class="cm-tip"><span class="tip-name">set</span><br/><span class="tip-samples">195 samples (6.0%)</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:85; background:#7c3aed;"><div class="cm-tip"><span class="tip-name">mute</span><br/><span class="tip-samples">85 samples (2.6%)</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:61; background:#6d28d9;"><div class="cm-tip"><span class="tip-name">unmute</span><br/><span class="tip-samples">61 samples (1.9%)</span><br/><span class="tip-acc">Accuracy: 98.4%</span><br/><span class="tip-miss">Misclassified: 1</span></div></div> <div class="cm-seg" style="flex:137; background:#10b981;"><div class="cm-tip"><span class="tip-name">play</span><br/><span class="tip-samples">137 samples (4.2%)</span><br/><span class="tip-acc">Accuracy: 98.5%</span><br/><span class="tip-miss">Misclassified: 2</span></div></div> <div class="cm-seg" style="flex:91; background:#059669;"><div class="cm-tip"><span class="tip-name">pause</span><br/><span class="tip-samples">91 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:71; background:#047857;"><div class="cm-tip"><span class="tip-name">next</span><br/><span class="tip-samples">71 samples (2.2%)</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:73; background:#065f46;"><div class="cm-tip"><span class="tip-name">previous</span><br/><span class="tip-samples">73 samples (2.2%)</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:87; background:#ef4444;"><div class="cm-tip"><span class="tip-name">launch</span><br/><span class="tip-samples">87 samples (2.7%)</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:92; background:#dc2626;"><div class="cm-tip"><span class="tip-name">close</span><br/><span class="tip-samples">92 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:87; background:#b91c1c;"><div class="cm-tip"><span class="tip-name">switch</span><br/><span class="tip-samples">87 samples (2.7%)</span><br/><span class="tip-acc">Accuracy: 98.9%</span><br/><span class="tip-miss">Misclassified: 1</span></div></div> <div class="cm-seg" style="flex:102; background:#f59e0b;"><div class="cm-tip"><span class="tip-name">search</span><br/><span class="tip-samples">102 samples (3.1%)</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:192; background:#d97706;"><div class="cm-tip"><span class="tip-name">set_reminder</span><br/><span class="tip-samples">192 samples (5.9%)</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:68; background:#ec4899;"><div class="cm-tip"><span class="tip-name">screenshot</span><br/><span class="tip-samples">68 samples (2.1%)</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:64; background:#db2777;"><div class="cm-tip"><span class="tip-name">read</span><br/><span class="tip-samples">64 samples (2.0%)</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:66; background:#be185d;"><div class="cm-tip"><span class="tip-name">explain</span><br/><span class="tip-samples">66 samples (2.0%)</span><br/><span class="tip-acc">Accuracy: 95.5%</span><br/><span class="tip-miss">Misclassified: 3</span></div></div> <div class="cm-seg" style="flex:107; background:#14b8a6;"><div class="cm-tip"><span class="tip-name">undo</span><br/><span class="tip-samples">107 samples (3.3%)</span><br/><span class="tip-acc">Accuracy: 96.3%</span><br/><span class="tip-miss">Misclassified: 4</span></div></div> <div class="cm-seg" style="flex:52; background:#0d9488;"><div class="cm-tip"><span class="tip-name">quit</span><br/><span class="tip-samples">52 samples (1.6%)</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:313; background:#0f766e;"><div class="cm-tip"><span class="tip-name">chat</span><br/><span class="tip-samples">313 samples (9.6%)</span><br/><span class="tip-acc">Accuracy: 90.7%</span><br/><span class="tip-miss">Misclassified: 29</span></div></div> <div class="cm-seg" style="flex:53; background:#f97316;"><div class="cm-tip"><span class="tip-name">minimize</span><br/><span class="tip-samples">53 samples (1.6%)</span><br/><span class="tip-acc">Accuracy: 90.6%</span><br/><span class="tip-miss">Misclassified: 5</span></div></div> <div class="cm-seg" style="flex:52; background:#ea580c;"><div class="cm-tip"><span class="tip-name">maximize</span><br/><span class="tip-samples">52 samples (1.6%)</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:56; background:#c2410c;"><div class="cm-tip"><span class="tip-name">restore</span><br/><span class="tip-samples">56 samples (1.7%)</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:73; background:#6366f1;"><div class="cm-tip"><span class="tip-name">focus</span><br/><span class="tip-samples">73 samples (2.2%)</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:55; background:#4f46e5;"><div class="cm-tip"><span class="tip-name">copy</span><br/><span class="tip-samples">55 samples (1.7%)</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:57; background:#4338ca;"><div class="cm-tip"><span class="tip-name">paste</span><br/><span class="tip-samples">57 samples (1.7%)</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:80; background:#06b6d4;"><div class="cm-tip"><span class="tip-name">cut</span><br/><span class="tip-samples">80 samples (2.5%)</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:75; background:#0891b2;"><div class="cm-tip"><span class="tip-name">lock</span><br/><span class="tip-samples">75 samples (2.3%)</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:100; background:#0e7490;"><div class="cm-tip"><span class="tip-name">sleep</span><br/><span class="tip-samples">100 samples (3.1%)</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:78; background:#84cc16;"><div class="cm-tip"><span class="tip-name">wifi_on</span><br/><span class="tip-samples">78 samples (2.4%)</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:66; background:#65a30d;"><div class="cm-tip"><span class="tip-name">wifi_off</span><br/><span class="tip-samples">66 samples (2.0%)</span><br/><span class="tip-acc">Accuracy: 92.4%</span><br/><span class="tip-miss">Misclassified: 5</span></div></div> <div class="cm-seg" style="flex:59; background:#a855f7;"><div class="cm-tip"><span class="tip-name">bluetooth_on</span><br/><span class="tip-samples">59 samples (1.8%)</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:57; background:#9333ea; border-radius:0 10px 10px 0;"><div class="cm-tip"><span class="tip-name">bluetooth_off</span><br/><span class="tip-samples">57 samples (1.7%)</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>set_reminder</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>wifi_on</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#65a30d;"></span>wifi_off</div> <div class="cm-legend-item"><span class="cm-legend-swatch" style="background:#a855f7;"></span>bluetooth_on</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/confusion_domain_val.png" alt="Domain confusion matrix" width="860" /> </p> <p align="center"> <img src="reports/confusion_action_val.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/train_loss_raw.png" alt="Raw training loss" width="860" /> </p> </div> </details>Upload-Ready Layout
.
|- 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.
License
Apache-2.0 (see LICENSE).
- Task
- Text Classification
- Library
- pytorch
- Type
- janegpt_v2_janus
- License
- apache-2.0
- Languages
- en
- Created
- Apr 12, 2026
- Updated
- Oct 7, 2026