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radekosmulski authored Jun 29, 2023
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# Merlin Changelog

<!-- This is a template to copy/paste when starting the changelog for a new release:
## [release]
### NVTabular
#### Major Changes
#### Added
#### Deprecated/Removed
#### Fixed Bugs
### Models
#### Major Changes
#### Added
#### Deprecated/Removed
#### Fixed Bugs
### Transformers4Rec
#### Major Changes
#### Added
#### Deprecated/Removed
#### Fixed Bugs
### Core
#### Major Changes
#### Added
#### Deprecated/Removed
#### Fixed Bugs
### Systems
#### Major Changes
#### Added
#### Deprecated/Removed
#### Fixed Bugs
### Dataloader
#### Major Changes
#### Added
#### Deprecated/Removed
#### Fixed Bugs
-->

## [23.06]

### NVTabular

#### Major Changes

* Moved some functionality from NVTabular to `merlin-core`, but left alias implace for import backwards compatibility. Some examples are `LambdaOp`, `AddMetadataOp`, `StatOperator`, `WorkflowNode`, and others. [#1823](https://github.com/NVIDIA-Merlin/NVTabular/pull/1823), [#1825](https://github.com/NVIDIA-Merlin/NVTabular/pull/1825)
* Updated `Categorify` to correctly handle nulls [#1836](https://github.com/NVIDIA-Merlin/NVTabular/pull/1836).

#### Added

* Added support for retrieving subworkflows using get_subworkflow API. Returns a subgraph wrapped in a new workflow object. [#1842](https://github.com/NVIDIA-Merlin/NVTabular/pull/1842)

#### Deprecated/Removed

* Removed the `nvtabular.inference` module. This functionality now exists in `merlin-systems` [#1822](https://github.com/NVIDIA-Merlin/NVTabular/pull/1822)
#### Fixed Bugs

### Models

#### Added

* Add support of transformer-based retrieval models [#1128](https://github.com/NVIDIA-Merlin/models/pull/1128)

### Merlin

#### Added

* Improvements in Quick-start for ranking example [#1014](https://github.com/NVIDIA-Merlin/Merlin/pull/1014)
* In `preprocessing.py`, added support to target encoding features, configurable through these new CLI arguments: `--target_encoding_features`, `--target_encoding_targets`, `--target_encoding_kfold`, `--target_encoding_smoothing`.
* In `ranking.py`: added support to select some columns to keep (`--keep_columns`) or remove (`--ignore_columns`) from at dataloading / training / evaluation.

#### Fixed Bugs

* Fixed in Quick-start for ranking example [#1017](https://github.com/NVIDIA-Merlin/Merlin/pull/1017):
* Fixed `preprocessing.py`, which was not standardizing and tagging continuous columns properly
* Fixed Wide&Deep and DeepFM models to use the updated API

### Transformers4Rec

#### Added

* Improved docstring coverage [#706](https://github.com/NVIDIA-Merlin/Transformers4Rec/pull/706)

#### Fixed Bugs

* Add support for providing a scalar cut-off in metrics, and Fix recall@1 that results higher than the upper cut-offs sometimes. [#720](https://github.com/NVIDIA-Merlin/Transformers4Rec/pull/720)
* Fix the CLM performance mismatch between model evaluation and manual inference [#723](https://github.com/NVIDIA-Merlin/Transformers4Rec/pull/723)
* Fixed OOM issues when evaluating/predicting [#721](https://github.com/NVIDIA-Merlin/Transformers4Rec/pull/721)
* API breaking notice: This fix changes the default output of `trainer.predict()` API, that returns a `PredictionOutput` object with a predictions property. Before this change, when the `predict_top_k` option was not set (default) the predictions property was as 2D tensor (batch size, item cardinality) with the scores for all the items. As now we set `T4RecTrainingArguments.predict_top_k` by default, the predictions property returns a tuple with `(top-100 predicted item ids, top-100 prediction scores)`.

### Core

#### Major Changes

* Merged NVTabular Operator base class with Base Operator in core.

#### Added

* Migrated some operators from NVTabular to core, allowing use in `merlin-systems`. (i.e. `LambdaOp` - changed to user defined function (UDF) and add metadata operator).
* Created subgraph operator to allow for recall and use of parts of a graph

### Systems

#### Added

* Added Test cases to debut functional support for core operators in systems ensembles
* Added API to retrieve sub ensembles.

## [23.05]

### NVTabular
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