pyabsa.tasks.AspectSentimentTripletExtraction.models.model

Module Contents

Classes

LayerNorm	Construct a layernorm module (See citation for details).
RefiningStrategy	Base class for all neural network modules.
GraphConvLayer	A GCN module operated on dependency graphs.
Biaffine	Base class for all neural network modules.
EMCGCN	Base class for all neural network modules.

class pyabsa.tasks.AspectSentimentTripletExtraction.models.model.LayerNorm(features, eps=1e-06)

Bases: torch.nn.Module

Construct a layernorm module (See citation for details).

forward(x)

class pyabsa.tasks.AspectSentimentTripletExtraction.models.model.RefiningStrategy(hidden_dim, edge_dim, dim_e, dropout_ratio=0.5)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(edge, node1, node2)

class pyabsa.tasks.AspectSentimentTripletExtraction.models.model.GraphConvLayer(device, gcn_dim, edge_dim, dep_embed_dim, pooling='avg')

Bases: torch.nn.Module

A GCN module operated on dependency graphs.

forward(weight_prob_softmax, weight_adj, gcn_inputs, self_loop)

class pyabsa.tasks.AspectSentimentTripletExtraction.models.model.Biaffine(config, in1_features, in2_features, out_features, bias=(True, True))

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

forward(input1, input2)

class pyabsa.tasks.AspectSentimentTripletExtraction.models.model.EMCGCN(config)

Bases: torch.nn.Module

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

inputs = ['tokens', 'masks', 'word_pair_position', 'word_pair_deprel', 'word_pair_pos', 'word_pair_synpost']

forward(inputs)