High-performance multi-threaded ENT-Rank version of RankLips that supports mini-batched learning. The implementation focuses on coordinate ascent to optimize for mean-average precision, which is one of the strongest models for model combination in information retrieval.
ENT-Rank-lips is designed to work with trec_eval file formats for defining runs (run format) and relevance data (qrel format). The features will be taken from the score and/or reciprocal rank of each input file. The filename of an input run (in the directory) will be used as a feature name. If you want to train a model and predict on a different test set, make sure that the input runs for test features are using exactly the sane filename. We recommend to create different directories for training and test sets.
ENT-Rank-lips is implemented in GHC Haskell, but we also provide Linux binaries. ENT-Rank-lips is released AS-IS under the BSD-3-Clause open source license.
Authors: Laura Dietz and Ben Gamari.
The latest release is v1.1.
See instructions on PDF.