TensorFlow Ranking Files

This is the 0.5.1 release of TensorFlow Ranking. We provide new ranking losses, metrics, layers, and pipeline based on the latest research progresses in Learning to Rank and Unbiased Ranking. We also update the API reference on www.tensorflow.org/ranking and on Github docs. The new changes include:

• Ranking losses added in tfr.keras.losses (github.com):
• PairwiseMSELoss: Implement a pairwise mean squared error loss.
• OrdinalLoss: Implement a pointwise multi-head ordinal regression on ordered multilabel.
• MixtureEMLoss: Implement a listwise Expectation-Maximization algorithm on a mixture model, introduced in Revisiting two tower models for unbiased learning to rank.
• Lambda weights for Lambda losses added in tfr.keras.losses (github.com):
• NDCGLambdaWeightV2: Implement an NDCG-based lambda weight for lambda losses, introduced in On Optimizing Top-K Metrics for Neural Ranking Models.
• LabelDiffLambdaWeight: Implement a lambda weight based on the absolute difference of two labels.
• Ranking metric added in tfr.keras.metrics (github.com):
• HitsMetric: Implement Hits@k metric.
• Ranking layer added in tfr.keras.layers (github.com):
• Bilinear: A layer to implement a bilinear interaction of two vectors, used in Revisiting two tower models for unbiased learning to rank.
• Ranking pipeline added in tfr.keras.pipeline (github.com):
• MultiObjectivePipeline: A pipeline to apply multi-objective losses, used in Scale Calibration of Deep Ranking Models.
• API reference updated on www.tensorflow.org/ranking and consistently on Github docs.

Dependencies: The following packages will be installed as required when installing tensorflow-ranking. tensorflow-serving-api>= 2.0.0, < 3.0.0 tensorflow>=2.7.0.