What is the rank of a tensor in TF?
Do I need to install tftf-ranking?
- TF-Ranking was presented at premier conferences in Information Retrieval, SIGIR 2019 and ICTIR 2019! The slides are available here. We provide a demo, with no installation required, to get started on using TF-Ranking. This demo runs on a colaboratory notebook, an interactive Python environment.
Does TF-ranking support multi-item scoring architecture?
- The TF-Ranking library supports multi-item scoring architecture, an extension of traditional single-item scoring. As we demonstrate in recent work, multi-item scoring is competitive in its performance to the state-of-the-art learning-to-rank models such as RankNet, MART, and LambdaMART on a public LETOR benchmark.
What loss functions does tftf-ranking support?
- TF-Ranking supports a wide range of standard pointwise, pairwise and listwise loss functions as described in prior work. This ensures that researchers using the TF-Ranking library are able to reproduce and extend previously published baselines, and practitioners can make the most informed choices for their applications.
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