kiwi.systems.outputs.translation_language_model

Module Contents

Classes

MaskedWordOutput	Base class for all neural network modules.
TLMOutputs	Base class for all neural network modules.

class kiwi.systems.outputs.translation_language_model.MaskedWordOutput(input_size, pad_idx, start_idx, stop_idx)

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)

class kiwi.systems.outputs.translation_language_model.TLMOutputs(inputs_dims: Dict[str, int], vocabs: Dict[str, Vocabulary], config: Config, pretraining: bool = False)

Bases: kiwi.systems._meta_module.MetaModule

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.

class Config

Bases: kiwi.utils.io.BaseConfig

Base class for all pydantic configs. Used to configure base behaviour of configs.

fine_tune :bool = False

Continue training an encoder on the post-edited text. Recommended if you have access to PE. Requires setting system.data.train.input.pe, system.data.valid.input.pe

forward(self, features, batch_inputs)

loss(self, model_out, batch_outputs)

metrics_step(self, batch, model_out, loss_dict)

metrics_end(self, steps, prefix='')

property metrics(self)

labels(self, field)

kiwi.systems.outputs.quality_estimation kiwi.systems._meta_module