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Torchrec Models

Torchrec provides the architecture for two popular recsys models; DeepFM and DLRM (Deep Learning Recommendation Model).

Along with the overall model, the individual architectures of each layer are also provided (e.g. SparseArch, DenseArch, InteractionArch, and OverArch).

Examples can be found within each model.

The following notation is used throughout the documentation for the models:

  • F: number of sparse features

  • D: embedding_dimension of sparse features

  • B: batch size

  • num_features: number of dense features

torchrec.models.deepfm

class torchrec.models.deepfm.DenseArch(in_features: int, hidden_layer_size: int, embedding_dim: int)

Bases: Module

Processes the dense features of DeepFMNN model. Output layer is sized to the embedding_dimension of the EmbeddingBagCollection embeddings.

Parameters:
  • in_features (int) – dimensionality of the dense input features.

  • hidden_layer_size (int) – sizes of the hidden layers in the DenseArch.

  • embedding_dim (int) – the same size of the embedding_dimension of sparseArch.

  • device (torch.device) – default compute device.

Example:

B = 20
D = 3
in_features = 10
dense_arch = DenseArch(in_features=10, hidden_layer_size=10, embedding_dim=D)
dense_embedded = dense_arch(torch.rand((B, 10)))
forward(features: Tensor) Tensor
Parameters:

features (torch.Tensor) – size B X num_features.

Returns:

an output tensor of size B X D.

Return type:

torch.Tensor

training: bool
class torchrec.models.deepfm.FMInteractionArch(fm_in_features: int, sparse_feature_names: List[str], deep_fm_dimension: int)

Bases: Module

Processes the output of both SparseArch (sparse_features) and DenseArch (dense_features) and apply the general DeepFM interaction according to the external source of DeepFM paper: https://arxiv.org/pdf/1703.04247.pdf

The output dimension is expected to be a cat of dense_features, D.

Parameters:
  • fm_in_features (int) – the input dimension of dense_module in DeepFM. For example, if the input embeddings is [randn(3, 2, 3), randn(3, 4, 5)], then the fm_in_features should be: 2 * 3 + 4 * 5.

  • sparse_feature_names (List[str]) – length of F.

  • deep_fm_dimension (int) – output of the deep interaction (DI) in the DeepFM arch.

Example:

D = 3
B = 10
keys = ["f1", "f2"]
F = len(keys)
fm_inter_arch = FMInteractionArch(sparse_feature_names=keys)
dense_features = torch.rand((B, D))
sparse_features = KeyedTensor(
    keys=keys,
    length_per_key=[D, D],
    values=torch.rand((B, D * F)),
)
cat_fm_output = fm_inter_arch(dense_features, sparse_features)
forward(dense_features: Tensor, sparse_features: KeyedTensor) Tensor
Parameters:
  • dense_features (torch.Tensor) – tensor of size B X D.

  • sparse_features (KeyedJaggedTensor) – KJT of size F * D X B.

Returns:

an output tensor of size B X (D + DI + 1).

Return type:

torch.Tensor

training: bool
class torchrec.models.deepfm.OverArch(in_features: int)

Bases: Module

Final Arch - simple MLP. The output is just one target.

Parameters:

in_features (int) – the output dimension of the interaction arch.

Example:

B = 20
over_arch = OverArch()
logits = over_arch(torch.rand((B, 10)))
forward(features: Tensor) Tensor
Parameters:

features (torch.Tensor) –

Returns:

an output tensor of size B X 1.

Return type:

torch.Tensor

training: bool
class torchrec.models.deepfm.SimpleDeepFMNN(num_dense_features: int, embedding_bag_collection: EmbeddingBagCollection, hidden_layer_size: int, deep_fm_dimension: int)

Bases: Module

Basic recsys module with DeepFM arch. Processes sparse features by learning pooled embeddings for each feature. Learns the relationship between dense features and sparse features by projecting dense features into the same embedding space. Learns the interaction among those dense and sparse features by deep_fm proposed in this paper: https://arxiv.org/pdf/1703.04247.pdf

The module assumes all sparse features have the same embedding dimension (i.e, each EmbeddingBagConfig uses the same embedding_dim)

The following notation is used throughout the documentation for the models:

  • F: number of sparse features

  • D: embedding_dimension of sparse features

  • B: batch size

  • num_features: number of dense features

Parameters:
  • num_dense_features (int) – the number of input dense features.

  • embedding_bag_collection (EmbeddingBagCollection) – collection of embedding bags used to define SparseArch.

  • hidden_layer_size (int) – the hidden layer size used in dense module.

  • deep_fm_dimension (int) – the output layer size used in deep_fm’s deep interaction module.

Example:

B = 2
D = 8

eb1_config = EmbeddingBagConfig(
    name="t1", embedding_dim=D, num_embeddings=100, feature_names=["f1", "f3"]
)
eb2_config = EmbeddingBagConfig(
    name="t2",
    embedding_dim=D,
    num_embeddings=100,
    feature_names=["f2"],
)

ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
sparse_nn = SimpleDeepFMNN(
    embedding_bag_collection=ebc, hidden_layer_size=20, over_embedding_dim=5
)

features = torch.rand((B, 100))

#     0       1
# 0   [1,2] [4,5]
# 1   [4,3] [2,9]
# ^
# feature
sparse_features = KeyedJaggedTensor.from_offsets_sync(
    keys=["f1", "f3"],
    values=torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]),
    offsets=torch.tensor([0, 2, 4, 6, 8]),
)

logits = sparse_nn(
    dense_features=features,
    sparse_features=sparse_features,
)
forward(dense_features: Tensor, sparse_features: KeyedJaggedTensor) Tensor
Parameters:
  • dense_features (torch.Tensor) – the dense features.

  • sparse_features (KeyedJaggedTensor) – the sparse features.

Returns:

logits with size B X 1.

Return type:

torch.Tensor

training: bool
class torchrec.models.deepfm.SparseArch(embedding_bag_collection: EmbeddingBagCollection)

Bases: Module

Processes the sparse features of the DeepFMNN model. Does embedding lookups for all EmbeddingBag and embedding features of each collection.

Parameters:

embedding_bag_collection (EmbeddingBagCollection) – represents a collection of pooled embeddings.

Example:

eb1_config = EmbeddingBagConfig(
    name="t1", embedding_dim=3, num_embeddings=10, feature_names=["f1"]
)
eb2_config = EmbeddingBagConfig(
    name="t2", embedding_dim=4, num_embeddings=10, feature_names=["f2"]
)

ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])

#     0       1        2  <-- batch
# 0   [0,1] None    [2]
# 1   [3]    [4]    [5,6,7]
# ^
# feature
features = KeyedJaggedTensor.from_offsets_sync(
    keys=["f1", "f2"],
    values=torch.tensor([0, 1, 2, 3, 4, 5, 6, 7]),
    offsets=torch.tensor([0, 2, 2, 3, 4, 5, 8]),
)

sparse_arch(features)
forward(features: KeyedJaggedTensor) KeyedTensor
Parameters:

features (KeyedJaggedTensor) –

Returns:

an output KJT of size F * D X B.

Return type:

KeyedJaggedTensor

training: bool

torchrec.models.dlrm

class torchrec.models.dlrm.DLRM(embedding_bag_collection: EmbeddingBagCollection, dense_in_features: int, dense_arch_layer_sizes: List[int], over_arch_layer_sizes: List[int], dense_device: Optional[device] = None)

Bases: Module

Recsys model from “Deep Learning Recommendation Model for Personalization and Recommendation Systems” (https://arxiv.org/abs/1906.00091). Processes sparse features by learning pooled embeddings for each feature. Learns the relationship between dense features and sparse features by projecting dense features into the same embedding space. Also, learns the pairwise relationships between sparse features.

The module assumes all sparse features have the same embedding dimension (i.e. each EmbeddingBagConfig uses the same embedding_dim).

The following notation is used throughout the documentation for the models:

  • F: number of sparse features

  • D: embedding_dimension of sparse features

  • B: batch size

  • num_features: number of dense features

Parameters:
  • embedding_bag_collection (EmbeddingBagCollection) – collection of embedding bags used to define SparseArch.

  • dense_in_features (int) – the dimensionality of the dense input features.

  • dense_arch_layer_sizes (List[int]) – the layer sizes for the DenseArch.

  • over_arch_layer_sizes (List[int]) – the layer sizes for the OverArch. The output dimension of the InteractionArch should not be manually specified here.

  • dense_device (Optional[torch.device]) – default compute device.

Example:

B = 2
D = 8

eb1_config = EmbeddingBagConfig(
   name="t1", embedding_dim=D, num_embeddings=100, feature_names=["f1", "f3"]
)
eb2_config = EmbeddingBagConfig(
   name="t2",
   embedding_dim=D,
   num_embeddings=100,
   feature_names=["f2"],
)

ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
model = DLRM(
   embedding_bag_collection=ebc,
   dense_in_features=100,
   dense_arch_layer_sizes=[20],
   over_arch_layer_sizes=[5, 1],
)

features = torch.rand((B, 100))

#     0       1
# 0   [1,2] [4,5]
# 1   [4,3] [2,9]
# ^
# feature
sparse_features = KeyedJaggedTensor.from_offsets_sync(
   keys=["f1", "f3"],
   values=torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]),
   offsets=torch.tensor([0, 2, 4, 6, 8]),
)

logits = model(
   dense_features=features,
   sparse_features=sparse_features,
)
forward(dense_features: Tensor, sparse_features: KeyedJaggedTensor) Tensor
Parameters:
  • dense_features (torch.Tensor) – the dense features.

  • sparse_features (KeyedJaggedTensor) – the sparse features.

Returns:

logits.

Return type:

torch.Tensor

training: bool
class torchrec.models.dlrm.DLRMTrain(dlrm_module: DLRM)

Bases: Module

nn.Module to wrap DLRM model to use with train_pipeline.

DLRM Recsys model from “Deep Learning Recommendation Model for Personalization and Recommendation Systems” (https://arxiv.org/abs/1906.00091). Processes sparse features by learning pooled embeddings for each feature. Learns the relationship between dense features and sparse features by projecting dense features into the same embedding space. Also, learns the pairwise relationships between sparse features.

The module assumes all sparse features have the same embedding dimension (i.e, each EmbeddingBagConfig uses the same embedding_dim)

Parameters:
  • dlrm_module – DLRM module (DLRM or DLRM_Projection or DLRM_DCN) to be used in

  • training

Example:

ebc = EmbeddingBagCollection(config=ebc_config)
dlrm_module = DLRM(
   embedding_bag_collection=ebc,
   dense_in_features=100,
   dense_arch_layer_sizes=[20],
   over_arch_layer_sizes=[5, 1],
)
dlrm_model = DLRMTrain(dlrm_module)
forward(batch: Batch) Tuple[Tensor, Tuple[Tensor, Tensor, Tensor]]
Parameters:

batch – batch used with criteo and random data from torchrec.datasets

Returns:

Tuple[loss, Tuple[loss, logits, labels]]

training: bool
class torchrec.models.dlrm.DLRM_DCN(embedding_bag_collection: EmbeddingBagCollection, dense_in_features: int, dense_arch_layer_sizes: List[int], over_arch_layer_sizes: List[int], dcn_num_layers: int, dcn_low_rank_dim: int, dense_device: Optional[device] = None)

Bases: DLRM

Recsys model with DCN modified from the original model from “Deep Learning Recommendation Model for Personalization and Recommendation Systems” (https://arxiv.org/abs/1906.00091). Similar to DLRM module but has DeepCrossNet https://arxiv.org/pdf/2008.13535.pdf as the interaction layer.

The module assumes all sparse features have the same embedding dimension (i.e. each EmbeddingBagConfig uses the same embedding_dim).

The following notation is used throughout the documentation for the models:

  • F: number of sparse features

  • D: embedding_dimension of sparse features

  • B: batch size

  • num_features: number of dense features

Parameters:
  • embedding_bag_collection (EmbeddingBagCollection) – collection of embedding bags used to define SparseArch.

  • dense_in_features (int) – the dimensionality of the dense input features.

  • dense_arch_layer_sizes (List[int]) – the layer sizes for the DenseArch.

  • over_arch_layer_sizes (List[int]) – the layer sizes for the OverArch. The output dimension of the InteractionArch should not be manually specified here.

  • dcn_num_layers (int) – the number of DCN layers in the interaction.

  • dcn_low_rank_dim (int) – the dimensionality of low rank approximation used in the dcn layers.

  • dense_device (Optional[torch.device]) – default compute device.

Example:

B = 2
D = 8

eb1_config = EmbeddingBagConfig(
   name="t1", embedding_dim=D, num_embeddings=100, feature_names=["f1", "f3"]
)
eb2_config = EmbeddingBagConfig(
   name="t2",
   embedding_dim=D,
   num_embeddings=100,
   feature_names=["f2"],
)

ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
model = DLRM_DCN(
   embedding_bag_collection=ebc,
   dense_in_features=100,
   dense_arch_layer_sizes=[20, D],
   dcn_num_layers=2,
   dcn_low_rank_dim=8,
   over_arch_layer_sizes=[5, 1],
)

features = torch.rand((B, 100))

#     0       1
# 0   [1,2] [4,5]
# 1   [4,3] [2,9]
# ^
# feature
sparse_features = KeyedJaggedTensor.from_offsets_sync(
   keys=["f1", "f3"],
   values=torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]),
   offsets=torch.tensor([0, 2, 4, 6, 8]),
)

logits = model(
   dense_features=features,
   sparse_features=sparse_features,
)
sparse_arch: SparseArch
training: bool
class torchrec.models.dlrm.DLRM_Projection(embedding_bag_collection: EmbeddingBagCollection, dense_in_features: int, dense_arch_layer_sizes: List[int], over_arch_layer_sizes: List[int], interaction_branch1_layer_sizes: List[int], interaction_branch2_layer_sizes: List[int], dense_device: Optional[device] = None)

Bases: DLRM

Recsys model modified from the original model from “Deep Learning Recommendation Model for Personalization and Recommendation Systems” (https://arxiv.org/abs/1906.00091). Similar to DLRM module but has additional MLPs in the interaction layer (along 2 branches).

The module assumes all sparse features have the same embedding dimension (i.e. each EmbeddingBagConfig uses the same embedding_dim).

The following notation is used throughout the documentation for the models:

  • F: number of sparse features

  • D: embedding_dimension of sparse features

  • B: batch size

  • num_features: number of dense features

Parameters:
  • embedding_bag_collection (EmbeddingBagCollection) – collection of embedding bags used to define SparseArch.

  • dense_in_features (int) – the dimensionality of the dense input features.

  • dense_arch_layer_sizes (List[int]) – the layer sizes for the DenseArch.

  • over_arch_layer_sizes (List[int]) – the layer sizes for the OverArch. The output dimension of the InteractionArch should not be manually specified here.

  • interaction_branch1_layer_sizes (List[int]) – the layer sizes for first branch of interaction layer. The output dimension must be a multiple of D.

  • interaction_branch2_layer_sizes (List[int]) – the layer sizes for second branch of interaction layer. The output dimension must be a multiple of D.

  • dense_device (Optional[torch.device]) – default compute device.

Example:

B = 2
D = 8

eb1_config = EmbeddingBagConfig(
   name="t1", embedding_dim=D, num_embeddings=100, feature_names=["f1", "f3"]
)
eb2_config = EmbeddingBagConfig(
   name="t2",
   embedding_dim=D,
   num_embeddings=100,
   feature_names=["f2"],
)

ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
model = DLRM_Projection(
   embedding_bag_collection=ebc,
   dense_in_features=100,
   dense_arch_layer_sizes=[20, D],
   interaction_branch1_layer_sizes=[3*D+D, 4*D],
   interaction_branch2_layer_sizes=[3*D+D, 4*D],
   over_arch_layer_sizes=[5, 1],
)

features = torch.rand((B, 100))

#     0       1
# 0   [1,2] [4,5]
# 1   [4,3] [2,9]
# ^
# feature
sparse_features = KeyedJaggedTensor.from_offsets_sync(
   keys=["f1", "f3"],
   values=torch.tensor([1, 2, 4, 5, 4, 3, 2, 9]),
   offsets=torch.tensor([0, 2, 4, 6, 8]),
)

logits = model(
   dense_features=features,
   sparse_features=sparse_features,
)
sparse_arch: SparseArch
training: bool
class torchrec.models.dlrm.DenseArch(in_features: int, layer_sizes: List[int], device: Optional[device] = None)

Bases: Module

Processes the dense features of DLRM model.

Parameters:
  • in_features (int) – dimensionality of the dense input features.

  • layer_sizes (List[int]) – list of layer sizes.

  • device (Optional[torch.device]) – default compute device.

Example:

B = 20
D = 3
dense_arch = DenseArch(10, layer_sizes=[15, D])
dense_embedded = dense_arch(torch.rand((B, 10)))
forward(features: Tensor) Tensor
Parameters:

features (torch.Tensor) – an input tensor of dense features.

Returns:

an output tensor of size B X D.

Return type:

torch.Tensor

training: bool
class torchrec.models.dlrm.InteractionArch(num_sparse_features: int)

Bases: Module

Processes the output of both SparseArch (sparse_features) and DenseArch (dense_features). Returns the pairwise dot product of each sparse feature pair, the dot product of each sparse features with the output of the dense layer, and the dense layer itself (all concatenated).

Note

The dimensionality of the dense_features (D) is expected to match the dimensionality of the sparse_features so that the dot products between them can be computed.

Parameters:

num_sparse_features (int) –

Example:

D = 3
B = 10
keys = ["f1", "f2"]
F = len(keys)
inter_arch = InteractionArch(num_sparse_features=len(keys))

dense_features = torch.rand((B, D))
sparse_features = torch.rand((B, F, D))

#  B X (D + F + F choose 2)
concat_dense = inter_arch(dense_features, sparse_features)
forward(dense_features: Tensor, sparse_features: Tensor) Tensor
Parameters:
  • dense_features (torch.Tensor) – an input tensor of size B X D.

  • sparse_features (torch.Tensor) – an input tensor of size B X F X D.

Returns:

an output tensor of size B X (D + F + F choose 2).

Return type:

torch.Tensor

training: bool
class torchrec.models.dlrm.InteractionDCNArch(num_sparse_features: int, crossnet: Module)

Bases: Module

Processes the output of both SparseArch (sparse_features) and DenseArch (dense_features). Returns the output of a Deep Cross Net v2 https://arxiv.org/pdf/2008.13535.pdf with a low rank approximation for the weight matrix. The input and output sizes are the same for this interaction layer (F*D + D).

Note

The dimensionality of the dense_features (D) is expected to match the dimensionality of the sparse_features so that the dot products between them can be computed.

Parameters:

num_sparse_features (int) –

Example:

D = 3
B = 10
keys = ["f1", "f2"]
F = len(keys)
DCN = LowRankCrossNet(
    in_features = F*D+D,
    dcn_num_layers = 2,
    dnc_low_rank_dim = 4,
)
inter_arch = InteractionDCNArch(
    num_sparse_features=len(keys),
    crossnet=DCN,
)

dense_features = torch.rand((B, D))
sparse_features = torch.rand((B, F, D))

#  B X (F*D + D)
concat_dense = inter_arch(dense_features, sparse_features)
forward(dense_features: Tensor, sparse_features: Tensor) Tensor
Parameters:
  • dense_features (torch.Tensor) – an input tensor of size B X D.

  • sparse_features (torch.Tensor) – an input tensor of size B X F X D.

Returns:

an output tensor of size B X (F*D + D).

Return type:

torch.Tensor

training: bool
class torchrec.models.dlrm.InteractionProjectionArch(num_sparse_features: int, interaction_branch1: Module, interaction_branch2: Module)

Bases: Module

Processes the output of both SparseArch (sparse_features) and DenseArch (dense_features). Return Y*Z and the dense layer itself (all concatenated) where Y is the output of interaction branch 1 and Z is the output of interaction branch 2. Y and Z are of size Bx(F1xD) and Bx(DxF2) respectively for some F1 and F2.

Note

The dimensionality of the dense_features (D) is expected to match the dimensionality of the sparse_features so that the dot products between them can be computed. The output dimension of the 2 interaction branches should be a multiple of D.

Parameters:
  • num_sparse_features (int) –

  • interaction_branch1 (nn.Module) – MLP module for the first branch of interaction layer

  • interaction_branch2 (nn.Module) – MLP module for the second branch of interaction layer

Example:

D = 3
B = 10
keys = ["f1", "f2"]
F = len(keys)
# Assume last layer of
I1 = DenseArch(
    in_features= 3 * D + D,
    layer_sizes=[4*D, 4*D], # F1 = 4
    device=dense_device,
)
I2 = DenseArch(
    in_features= 3 * D + D,
    layer_sizes=[4*D, 4*D], # F2 = 4
    device=dense_device,
)
inter_arch = InteractionProjectionArch(
                num_sparse_features=len(keys),
                interaction_branch1 = I1,
                interaction_branch2 = I2,
            )

dense_features = torch.rand((B, D))
sparse_features = torch.rand((B, F, D))

#  B X (D + F1 * F2)
concat_dense = inter_arch(dense_features, sparse_features)
forward(dense_features: Tensor, sparse_features: Tensor) Tensor
Parameters:
  • dense_features (torch.Tensor) – an input tensor of size B X D.

  • sparse_features (torch.Tensor) – an input tensor of size B X F X D.

Returns:

an output tensor of size B X (D + F1 * F2)) where F1*D and F2*D are the output dimensions of the 2 interaction MLPs.

Return type:

torch.Tensor

training: bool
class torchrec.models.dlrm.OverArch(in_features: int, layer_sizes: List[int], device: Optional[device] = None)

Bases: Module

Final Arch of DLRM - simple MLP over OverArch.

Parameters:
  • in_features (int) – size of the input.

  • layer_sizes (List[int]) – sizes of the layers of the OverArch.

  • device (Optional[torch.device]) – default compute device.

Example:

B = 20
D = 3
over_arch = OverArch(10, [5, 1])
logits = over_arch(torch.rand((B, 10)))
forward(features: Tensor) Tensor
Parameters:

features (torch.Tensor) –

Returns:

size B X layer_sizes[-1]

Return type:

torch.Tensor

training: bool
class torchrec.models.dlrm.SparseArch(embedding_bag_collection: EmbeddingBagCollection)

Bases: Module

Processes the sparse features of DLRM. Does embedding lookups for all EmbeddingBag and embedding features of each collection.

Parameters:

embedding_bag_collection (EmbeddingBagCollection) – represents a collection of pooled embeddings.

Example:

eb1_config = EmbeddingBagConfig(
   name="t1", embedding_dim=3, num_embeddings=10, feature_names=["f1"]
)
eb2_config = EmbeddingBagConfig(
   name="t2", embedding_dim=4, num_embeddings=10, feature_names=["f2"]
)

ebc = EmbeddingBagCollection(tables=[eb1_config, eb2_config])
sparse_arch = SparseArch(embedding_bag_collection)

#     0       1        2  <-- batch
# 0   [0,1] None    [2]
# 1   [3]    [4]    [5,6,7]
# ^
# feature
features = KeyedJaggedTensor.from_offsets_sync(
   keys=["f1", "f2"],
   values=torch.tensor([0, 1, 2, 3, 4, 5, 6, 7]),
   offsets=torch.tensor([0, 2, 2, 3, 4, 5, 8]),
)

sparse_embeddings = sparse_arch(features)
forward(features: KeyedJaggedTensor) Tensor
Parameters:

features (KeyedJaggedTensor) – an input tensor of sparse features.

Returns:

tensor of shape B X F X D.

Return type:

torch.Tensor

property sparse_feature_names: List[str]
training: bool
torchrec.models.dlrm.choose(n: int, k: int) int

Simple implementation of math.comb for Python 3.7 compatibility.

Module contents

Torchrec Models

Torchrec provides the architecture for two popular recsys models; DeepFM and DLRM (Deep Learning Recommendation Model).

Along with the overall model, the individual architectures of each layer are also provided (e.g. SparseArch, DenseArch, InteractionArch, and OverArch).

Examples can be found within each model.

The following notation is used throughout the documentation for the models:

  • F: number of sparse features

  • D: embedding_dimension of sparse features

  • B: batch size

  • num_features: number of dense features

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