{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Entmax provides PyTorch implementations of entmax and entmax losses\u2014sparse probability mappings that generalize softmax and cross-entropy, including exact algorithms for 1.5-entmax and 2-entmax (sparsemax) and bisection-based methods for adaptive, learned sparsity.","skillfed_tags":["sparse-attention","pytorch-extension","research-oriented"],"use_cases":["Build sparse attention mechanisms in transformer models where only a subset of positions need non-zero attention weights.","Implement multi-label classification with sparse output distributions instead of dense softmax probabilities.","Learn adaptive sparsity patterns by computing gradients with respect to the alpha parameter during training.","Replace softmax in sequence-to-sequence models to reduce computational cost and improve interpretability through explicit sparsity.","Experiment with sparse transformers and other architectures that benefit from learned, structured sparsity in attention."],"what_it_does":"Entmax is a PyTorch library that implements entmax\u2014a family of sparse probability mappings that generalize softmax by introducing sparsity. Instead of softmax's dense output where all values are non-zero, entmax can produce sparse distributions where many values become exactly zero, useful for attention mechanisms and multi-label classification. The package includes exact partial-sort algorithms for common cases (1.5-entmax and 2-entmax/sparsemax) and a general bisection-based algorithm for arbitrary alpha values, plus support for gradients with respect to alpha itself for learned, adaptive sparsity.\n\nThe library depends only on torch and installs as a pure Python wheel with low friction. It is designed for researchers and practitioners building sparse sequence models, adaptive transformers, and other architectures where sparse attention or sparse probability distributions are beneficial. The code is well-documented with examples and academic citations, though maintenance is dormant\u2014the last release was in February 2024.","worth_installing":"Yes, if you need sparse probability mappings or attention in PyTorch. The package is well-designed, permissively licensed, and has low install friction. Dormant maintenance is a minor concern\u2014check compatibility with your PyTorch version before adopting in production, and verify that the bisection algorithms meet your numerical stability requirements. For research and prototyping, it is a solid choice."},"id":"entmax","links":{"html":"https://skillfed.io/packages/entmax","md":"https://skillfed.io/packages/entmax.md","pypi":"https://pypi.org/project/entmax/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-02-07","license_spdx":null,"license_treatment":"permissive","name":"entmax","python_support":"supports_current","summary":"The entmax mapping and its loss, a family of sparse alternatives to softmax."},"popularity":{"monthly_downloads":105484,"position":12696,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.3"}