TensorFlow Ranking v0.3.3

This is the 0.3.3 release of TensorFlow Ranking. It depends on tf-models-official >= 2.4.0 and tensorflow-serving-api>= 2.0.0, < 3.0.0. It is compatible with tensorflow==2.4.1. All of these packages will be installed as required packages when installing tensorflow-ranking.

The main changes in this release contain the Document Interaction Network (DIN) layer and layers for training Keras models using Functional API. The new components are listed below:

• Document Interaction Network: See paper.

  • Building Keras ranking models for DIN using Keras Preprocessing Layers.
    • Native Keras training: An example client to showcase such a model using model.fit().
    • Estimator based training: Another example client to showcase training a DIN model as an Estimator.
  • tfr.keras.layers.DocumentInteractionAttention: A keras layer to model cross-document interactions. Applies cross-document attention across valid examples identified using a mask.

• Keras Layers: for easy transformation of context and example features and related utilities.

  • tfr.keras.layers.FlattenList: Flattens the batch_size dimension and the list_size dimension for the example_features and expands list_size times for the context_features.
  • tfr.keras.layers.ConcatFeatures: Concatenates context features and example features in a listwise manner.
  • tfr.keras.layers.RestoreList: Output layer to restore batch_size dimension and list_size dimension for the output shape of logits.

• Others

  • tfr.keras.metrics.get(metric_key): Add a get metric factory for keras metrics.
  • Masking support in tfr.data: Add support for parsing a boolean mask tensor which indicates number of valid examples via mask_feature_name argument in tfr.data._RankingDataParser and all associated input data parsing and serving_input_fn builders.