kiwi.systems.encoders.quetch

Module Contents

Classes

InputEmbeddingsConfig	Embeddings size for each input field, if they are not loaded.
QUETCHEncoder	Base class for all neural network modules.

kiwi.systems.encoders.quetch.logger

class kiwi.systems.encoders.quetch.InputEmbeddingsConfig

Bases: kiwi.utils.io.BaseConfig

Embeddings size for each input field, if they are not loaded.

source :TokenEmbeddings.Config

target :TokenEmbeddings.Config

source_pos :Optional[TokenEmbeddings.Config]

target_pos :Optional[TokenEmbeddings.Config]

class kiwi.systems.encoders.quetch.QUETCHEncoder(vocabs: Dict[str, Vocabulary], config: Config, pre_load_model: bool = True)

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.

window_size :int = 3

Size of sliding window.

embeddings :InputEmbeddingsConfig

classmethod input_data_encoders(cls, config: Config)

size(self, field=None)

forward(self, batch_inputs)

kiwi.systems.encoders.predictor kiwi.systems.encoders.xlm