kiwi.modules.token_embeddings

Module Contents

Classes

TokenEmbeddings

Base class for all neural network modules.

class kiwi.modules.token_embeddings.TokenEmbeddings(num_embeddings: int, pad_idx: int, config: Config, vectors=None)

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.

class Config

Bases: kiwi.utils.io.BaseConfig

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

dim :int = 50

freeze :bool = False

dropout :float = 0.0

use_position_embeddings :bool = False

max_position_embeddings :int = 4000

sparse_embeddings :bool = False

scale_embeddings :bool = False

input_layer_norm :bool = False

property num_embeddings(self)

size(self)

forward(self, batch_input, *args)

kiwi.modules.sentence_level_output kiwi.modules.word_level_output