Azlo Sentiment
Beyond simple happy/sad. An AI trained to detect 150+ complex emotional states, from 'Sonder' to 'Cognitive Dissonance'.
Awaiting Input Stream...
Enter text and click analyze to begin
Need more access? Get the API Key.
Model Architecture
- MiniLM-L12-v2 (Transformer)
- MiniLM-L12-v2
- Parameter Count
- 33 Million (Quantized)
- Output Vectors
- 10-Dimensional Vector
- Architecture
- Regression Head
Inference
- Execution Provider
- ONNX Runtime (WASM)
- Optimization
- Constant Folding
- Tokenizer
- WordPiece (30k vocab)
- Avg. Latency
- ~40ms
Training Strategy
- Loss Function
- Smooth L1 + Cosine Sim
- Regression Head
- Linear + LayerNorm
- Pooling
- Mean Pooling Strategy
- Scaler
- RobustScaler
About the Azlo Sentiment Engine
10-Dimensional Vector Analysis
The Azlo Sentiment Engine goes beyond basic "positive" or "negative" labels. It utilizes a 10-dimensional output vector trained on millions of data points to map text into a complex psychological coordinate system. This allows the model to distinguish between 150+ distinct states, such as the difference between "Loneliness" and "Solitude", or "Joy" and "Catharsis".
Complex State Detection
Most AI models are trained on basic emotions. Azlo is trained on nuances. It can detect specific phenomena like Cognitive Dissonance (conflicting beliefs), Hiraeth (nostalgic longing), Schadenfreude, and Deep Work states. This makes it uniquely suited for analyzing literary text, brand sentiment, and complex social interactions.
Mathematical Precision
By using Cosine Similarity and Smooth L1 Loss during training, the engine minimizes the distance between predicted emotional vectors and the ground truth. The result is a highly accurate, "distance-based" understanding of emotion, where the model calculates how close your text is to a specific feeling, rather than just guessing a category.