kiwi.modules.word_level_output

Module Contents

Classes

WordLevelOutput	Base class for all neural network modules.
GapTagsOutput	Base class for all neural network modules.

class kiwi.modules.word_level_output.WordLevelOutput(input_size, output_size, pad_idx, class_weights=None, remove_first=False, remove_last=False)

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(Model, self).__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.

forward(self, features_tensor, batch_inputs=None)

class kiwi.modules.word_level_output.GapTagsOutput(input_size, output_size, pad_idx, class_weights=None, remove_first=False, remove_last=False)

Bases: kiwi.modules.word_level_output.WordLevelOutput

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(Model, self).__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.

forward(self, features_tensor, batch_inputs=None)

kiwi.modules.token_embeddings kiwi.systems