Verskyt: Tversky Neural Networks¶

Verskyt (pronounced “ver-SKIT”) is a Python library for Tversky Neural Networks (TNNs) built on three design principles: Modularity, Introspection, and Extensibility. Verskyt provides PyTorch-compatible TNN implementations alongside tools for model introspection and prototype analysis.

TNNs are psychologically plausible deep learning models based on differentiable Tversky similarity that operate by projecting inputs into a learned feature space (Ω), where similarity to explicit prototypes (Π) is computed.

Design Principles:

🔧 Modularity: Clean, composable components that integrate with PyTorch
🔍 Introspection: Tools for examining model internals and decision processes
🚀 Extensibility: Built for researchers to modify and develop TNN architectures

Key Features:

🧠 Psychologically-Plausible Similarity: Based on Tversky’s feature-based similarity theory
🔥 Non-linear Capability: Single layer can solve XOR (impossible for linear layers)
📈 Performance Gains: Up to 24.7% accuracy improvement on complex datasets
⚡ Parameter Efficiency: Fewer parameters with better performance
🔌 Drop-in Compatibility: Easy replacement for nn.Linear layers

Quick Start¶

from verskyt import TverskyProjectionLayer
import torch

# Create a layer (replaces nn.Linear(128, 10))
layer = TverskyProjectionLayer(
    in_features=128,      # Dimensionality of the input vector
    num_prototypes=10,    # Corresponds to output classes
    num_features=256      # Dimensionality of the internal learned feature space (Ω)
)

# Forward pass
x = torch.randn(32, 128)
output = layer(x)  # shape: [32, 10]

Installation¶

pip install verskyt

Contents¶

API Reference

API Reference

Citation¶

If you use Verskyt in your research, please cite both the original Tversky Neural Network paper and this library.

1. Foundational Paper:

@article{doumbouya2025tversky,
  title={Tversky Neural Networks: Psychologically Plausible Deep Learning with Differentiable Tversky Similarity},
  author={Doumbouya, Moussa Koulako Bala and Jurafsky, Dan and Manning, Christopher D.},
  journal={arXiv preprint arXiv:2506.11035},
  year={2025}
}

2. This Library (Verskyt):

We recommend citing the specific version of the software you used. You can get a persistent DOI for each version from Zenodo.

@software{smith_2025_verskyt,
  author       = {Smith, Jeff},
  title        = {{Verskyt: A versatile toolkyt for Tversky Neural Networks}},
  month        = aug,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v0.2.4},
  doi          = {10.5281/zenodo.17014431},
  url          = {https://doi.org/10.5281/zenodo.17014431}
}