InertialAI-Embed-Alpha

A unified embedding model that encodes text and time-series into a shared semantic space—enabling cross-modal search and retrieval.

InertialAI-Embed-Alpha Visualization
1024
Max Dims
2048
Max Tokens
0.855
TS P@1
Text + Time-Series
Input Modalities

Cross-Modal Search

Search time-series patterns using natural language. InertialAI-Embed-Alpha is the first production-ready embedding model that unifies text and time-series in a shared semantic space.

Built on InertialAI-Alpha and fine-tuned with contrastive learning, it enables applications impossible with single-modality approaches—find sensor anomalies with queries like "sudden temperature spike followed by system failure" or cluster patient data by clinical descriptions.

Specifications

Base ModelInertialAI-Alpha
Max Dimensions1024
Recommended Dims256
Max Text Length2048 tokens
Max TS Length2048 tokens
Matryoshka SupportYes

Matryoshka Embeddings

Trade dimensionality for efficiency. Smaller dimensions retain high quality.

1024
100%
256
~99%
128
~95%

Benchmark Results

Competitive with specialized models while uniquely supporting cross-modal retrieval.

Text Similarity (MTEB)

TaskOursMiniLM
STS Average0.7590.790
ArguAna0.5430.502
SciFact0.6680.645

Time-Series Retrieval (P@1)

BenchmarkOursCHRONOS
UCR (Univariate)0.8550.856
UEA (Multivariate)0.6250.601

Cross-Modal Retrieval (TimeMMD)

TaskP@1P@5Random
Text → Time-Series0.5350.9150.431
Time-Series → Text0.5190.9000.431

24% above random baseline. The only model enabling this capability.

Use Cases

Healthcare Monitoring

Search patient monitoring data using clinical descriptions. Query "irregular heartbeat preceding cardiac event" to retrieve similar ECG patterns. Achieves 0.785 P@1 on epilepsy detection.

Financial Analysis

Retrieve market patterns matching analyst commentary. Find historical periods described as "rapid drawdown followed by consolidation."

Unified Observability

Query logs and metrics in a single search. Find "database connection pool exhaustion" across log lines and metric spikes. Cross-modal P@1: 0.535.

Industrial Monitoring

Search sensor data using maintenance reports. Query "gradual temperature increase with vibration anomaly" across factory equipment.

Ready to build with cross-modal embeddings?