transformers4rec.torch.model package

Submodules

transformers4rec.torch.model.head module

transformers4rec.torch.model.model module

transformers4rec.torch.model.prediction_task module

class transformers4rec.torch.model.prediction_task.BinaryClassificationPrepareBlock

Bases: transformers4rec.torch.block.base.BuildableBlock

build(input_size) → transformers4rec.torch.block.base.SequentialBlock

class transformers4rec.torch.model.prediction_task.BinaryClassificationTask(target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Union[transformers4rec.torch.block.base.BlockBase, transformers4rec.torch.block.base.BuildableBlock]] = None, loss=BCELoss(), metrics=(BinaryPrecision(), BinaryRecall(), BinaryAccuracy()), summary_type='first')

Bases: transformers4rec.torch.model.base.PredictionTask

DEFAULT_LOSS = BCELoss()

DEFAULT_METRICS = (BinaryPrecision(), BinaryRecall(), BinaryAccuracy())

training: bool

class transformers4rec.torch.model.prediction_task.RegressionPrepareBlock

Bases: transformers4rec.torch.block.base.BuildableBlock

build(input_size) → transformers4rec.torch.block.base.SequentialBlock

class transformers4rec.torch.model.prediction_task.RegressionTask(target_name: Optional[str] = None, task_name: Optional[str] = None, task_block: Optional[Union[transformers4rec.torch.block.base.BlockBase, transformers4rec.torch.block.base.BuildableBlock]] = None, loss=MSELoss(), metrics=(MeanSquaredError()), summary_type='first')

Bases: transformers4rec.torch.model.base.PredictionTask

DEFAULT_LOSS = MSELoss()

DEFAULT_METRICS = (MeanSquaredError(),)

training: bool

class transformers4rec.torch.model.prediction_task.NextItemPredictionTask(loss: torch.nn.modules.module.Module = NLLLoss(), metrics: Iterable[torchmetrics.metric.Metric] = (NDCGAt(), AvgPrecisionAt(), RecallAt()), task_block: Optional[Union[transformers4rec.torch.block.base.BlockBase, transformers4rec.torch.block.base.BuildableBlock]] = None, task_name: str = 'next-item', weight_tying: bool = False, softmax_temperature: float = 1, padding_idx: int = 0, target_dim: Optional[int] = None)

Bases: transformers4rec.torch.model.base.PredictionTask

This block performs item prediction task for session and sequential-based models. It requires a body containing a masking schema to use for training and target generation. For the supported masking schemes, please refers to: https://nvidia-merlin.github.io/Transformers4Rec/main/model_definition.html#sequence-masking

Parameters

• loss (torch.nn.Module) – Loss function to use. Defaults to NLLLos.

• metrics (Iterable[torchmetrics.Metric]) – List of ranking metrics to use for evaluation.

• task_block – Module to transform input tensor before computing predictions.

• task_name (str, optional) – Name of the prediction task, if not provided a name will be automatically constructed based on the target-name & class-name.

• weight_tying (bool) – The item id embedding table weights are shared with the prediction network layer.

• softmax_temperature (float) – Softmax temperature, used to reduce model overconfidence, so that softmax(logits / T). Value 1.0 reduces to regular softmax.

• padding_idx (int) – pad token id.

• target_dim (int) – vocabulary size of item ids

DEFAULT_METRICS = (NDCGAt(), AvgPrecisionAt(), RecallAt())

build(body, input_size, device=None, inputs=None, task_block=None, pre=None)

Build method, this is called by the Head.

forward(inputs: torch.Tensor, targets=None, training=False, testing=False, **kwargs)

remove_pad_3d(inp_tensor, non_pad_mask)

calculate_metrics(predictions, targets) → Dict[str, torch.Tensor]

compute_metrics()

training: bool

class transformers4rec.torch.model.prediction_task.NextItemPredictionPrepareBlock(target_dim: int, weight_tying: bool = False, item_embedding_table: Optional[torch.nn.modules.module.Module] = None, softmax_temperature: float = 0)

Bases: transformers4rec.torch.block.base.BuildableBlock

build(input_size) → transformers4rec.torch.block.base.Block

Module contents