v0.7.7

Bump version.

PiperOrigin-RevId: 859376966

v0.7.6

Add 'tf-keras' (Keras 2) to requirements.txt and pip_install.sh.

PiperOrigin-RevId: 837268927

Unified embeddings, take 2

Release TFRS v0.7.5.

This is a no-op for the TFRS library but should allow us to push new releases to PyPI.

PiperOrigin-RevId: 836781110

Unified embeddings

Unified embeddings.

v0.7.3

Changed

• The Retrieval task now accepts a list of factorized metrics instead of a
  single optional metric.

Added

• tfrs.experimental.optimizers.ClippyAdagrad: a new optimizer based on
  tf.keras.optimizers.Adagrad that is able to improve training stability.
• tfrs.metrics.FactorizedTopK now accepts sample weights which are used to
  compute weighted top k metrics.

v0.7.2

• Improved support for using TPUEmbedding under parameter server strategy.

v0.7.1

Release fixes.

v0.7.0

[0.7.0][2022-07-07]

A number of changes to make factorized top-K metric computation more accurate
and less prone to user error.

Changed

• tfrs.layers.embedding.TPUEmbedding now supports input features with
  dynamic shape. batch_size argument is deprecated and no longer required.

• tfrs.layers.embedding.TPUEmbedding now supports running on different
  versions of TPU.

• Pinned TensorFlow to >= 2.9.0 which works with Scann 1.2.7.

• tfrs.tasks.Ranking.call now accepts a compute_batch_metrics argument to
  allow switching off batch metric computation. Following this change,
  'compute_metrics'argument does not impact computation of batch metrics.

Breaking changes

• tfrs.metrics.FactorizedTopK requires the candidate ids for positive
  candidates to be supplied when using approximate top-K sources. Each top-K
  layer now has an exact method to broadcast its ability to return exact or
  approximate top-K results.
• Removed metrics constructor parameter for tfrs.metrics.FactorizedTopK.
  FactorizedTopK only makes sense with top-k metrics, and this change
  enforces this.
• Replaced the k constructor argument in tfrs.metrics.FactorizedTopK with
  ks: a list of k values at which to compute the top k metric.

Changed

• The tfrs.metrics.FactorizedTopK metric can now compute candidate-id based
  metrics when given the true_candidate_ids argument in its call method.

Added

• The Retrieval task now also accepts a loss_metrics argument.

v0.6.0

Changed

• Pinned TensorFlow to >= 2.6.0, which works with Scann 1.2.3.

Breaking changes

• TopK layer indexing API changed. Indexing with datasets is now done via
  the index_from_dataset method. This change reduces the possibility of
  misaligning embeddings and candidate identifiers when indexing via
  indeterministic datasets.

v0.5.2

Fixed

• Fixed error in default arguments to tfrs.experimental.models.Ranking
  (#311).
• Fix TPUEmbedding layer to use named parameters.

Added

• Added batch_metrics to tfrs.tasks.Retrieval for measuring how good the
  model is at picking out the true candidate for a query from other candidates
  in the batch.
• Added tfrs.experimental.layers.embedding.PartialTPUEmbedding layer, which
  uses tfrs.layers.embedding.TPUEmbedding for large embedding lookups and
  tf.keras.layers.Embedding for smaller embedding lookups.