ai.djl.modality.nlp.embedding

Class TrainableWordEmbedding

java.lang.Object

ai.djl.nn.AbstractBlock

ai.djl.nn.core.Embedding<java.lang.String>

ai.djl.modality.nlp.embedding.TrainableWordEmbedding

All Implemented Interfaces:

WordEmbedding, Block, AbstractEmbedding<java.lang.String>, AbstractIndexedEmbedding<java.lang.String>

public class TrainableWordEmbedding
extends Embedding<java.lang.String>
implements WordEmbedding

TrainableWordEmbedding is an implementation of WordEmbedding and Embedding based on a SimpleVocabulary. This WordEmbedding is ideal when there are no pre-trained embeddings available.

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	TrainableWordEmbedding.Builder A builder for a TrainableWordEmbedding.

Nested classes/interfaces inherited from class ai.djl.nn.core.Embedding

Embedding.BaseBuilder<T,B extends Embedding.BaseBuilder<T,B>>, Embedding.DefaultEmbedding, Embedding.DefaultItem

Field Summary

Fields inherited from class ai.djl.nn.core.Embedding

dataType, embedding, embeddingSize, fallthroughEmbedding, numItems, sparseGrad

Fields inherited from class ai.djl.nn.AbstractBlock

children, inputNames, inputShapes, parameters, parameterShapeCallbacks, version

Constructor Summary

Constructors

Constructor and Description
TrainableWordEmbedding(NDArray embedding, java.util.List<java.lang.String> items) Constructs a pretrained embedding.
TrainableWordEmbedding(NDArray embedding, java.util.List<java.lang.String> items, boolean sparseGrad) Constructs a pretrained embedding.
TrainableWordEmbedding(TrainableWordEmbedding.Builder builder) Constructs a new instance of TrainableWordEmbedding from the TrainableWordEmbedding.Builder.
TrainableWordEmbedding(Vocabulary vocabulary, int embeddingSize) Constructs a new instance of TrainableWordEmbedding from a SimpleVocabulary and a given embedding size.

Method Summary

Modifier and Type	Method and Description
static TrainableWordEmbedding.Builder	builder() Creates a builder to build an Embedding.
java.lang.String	decode(byte[] byteArray) Decodes the given byte array into an object of input parameter type.
long	embed(java.lang.String item) Embeds an item.
NDArray	embedWord(NDArray index) Embeds the word after preprocessed using WordEmbedding.preprocessWordToEmbed(String).
byte[]	encode(java.lang.String input) Encodes an object of input type into a byte array.
boolean	hasItem(java.lang.String item) Returns whether an item is in the embedding.
long	preprocessWordToEmbed(java.lang.String word) Pre-processes the word to embed into an array to pass into the model.
java.util.Optional<java.lang.String>	unembed(long index) Returns the item corresponding to the given index.
java.lang.String	unembedWord(NDArray word) Returns the closest matching word for the given index.
boolean	vocabularyContains(java.lang.String word) Returns whether an embedding exists for a word.

Methods inherited from class ai.djl.nn.core.Embedding

embed, forward, getOutputShapes, loadParameters, saveParameters

Methods inherited from class ai.djl.nn.AbstractBlock

addChildBlock, addParameter, addParameter, addParameter, beforeInitialize, cast, clear, describeInput, getChildren, getDirectParameters, getParameters, getParameterShape, initialize, initializeChildBlocks, isInitialized, loadMetadata, readInputShapes, saveInputShapes, saveMetadata, setInitializer, setInitializer, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Methods inherited from interface ai.djl.modality.nlp.embedding.WordEmbedding

embedWord, embedWord

Methods inherited from interface ai.djl.nn.Block

forward, forward, validateLayout

Constructor Detail

TrainableWordEmbedding

public TrainableWordEmbedding(TrainableWordEmbedding.Builder builder)

Constructs a new instance of TrainableWordEmbedding from the TrainableWordEmbedding.Builder.

Parameters:

builder - the TrainableWordEmbedding.Builder

TrainableWordEmbedding

public TrainableWordEmbedding(Vocabulary vocabulary,
                              int embeddingSize)

Constructs a new instance of TrainableWordEmbedding from a SimpleVocabulary and a given embedding size.

Parameters:

vocabulary - a Vocabulary to get tokens from

embeddingSize - the required embedding size

TrainableWordEmbedding

public TrainableWordEmbedding(NDArray embedding,
                              java.util.List<java.lang.String> items)

Constructs a pretrained embedding.

Parameters:

embedding - the embedding array

items - the items in the embedding (in matching order to the embedding array)

TrainableWordEmbedding

public TrainableWordEmbedding(NDArray embedding,
                              java.util.List<java.lang.String> items,
                              boolean sparseGrad)

Constructs a pretrained embedding.

Parameters:

embedding - the embedding array

items - the items in the embedding (in matching order to the embedding array)

sparseGrad - whether to compute row sparse gradient in the backward calculation

Method Detail

vocabularyContains

public boolean vocabularyContains(java.lang.String word)

Returns whether an embedding exists for a word.

Specified by:

vocabularyContains in interface WordEmbedding

Parameters:

word - the word to check

Returns:

true if an embedding exists

preprocessWordToEmbed

public long preprocessWordToEmbed(java.lang.String word)

Pre-processes the word to embed into an array to pass into the model.

Make sure to call WordEmbedding.embedWord(NDManager, long) after this.

Specified by:

preprocessWordToEmbed in interface WordEmbedding

Parameters:

word - the word to embed

Returns:

the word that is ready to embed

embedWord

public NDArray embedWord(NDArray index)
                  throws EmbeddingException

Description copied from interface: WordEmbedding

Embeds the word after preprocessed using WordEmbedding.preprocessWordToEmbed(String).

Specified by:

embedWord in interface WordEmbedding

Parameters:

index - the index of the word to embed

Returns:

the embedded word

Throws:

EmbeddingException - if there is an error while trying to embed

unembedWord

public java.lang.String unembedWord(NDArray word)

Returns the closest matching word for the given index.

Specified by:

unembedWord in interface WordEmbedding

Parameters:

word - the word embedding to find the matching string word for.

Returns:

a word similar to the passed in embedding

encode

public byte[] encode(java.lang.String input)

Encodes an object of input type into a byte array. This is used in saving and loading the Embedding objects.

Specified by:

encode in interface AbstractIndexedEmbedding<java.lang.String>

Parameters:

input - the input object to be encoded

Returns:

the encoded byte array.

decode

public java.lang.String decode(byte[] byteArray)

Decodes the given byte array into an object of input parameter type.

Specified by:

decode in interface AbstractIndexedEmbedding<java.lang.String>

Parameters:

byteArray - the byte array to be decoded

Returns:

the decode object of input parameter type

embed

public long embed(java.lang.String item)

Description copied from interface: AbstractIndexedEmbedding

Embeds an item.

Specified by:

embed in interface AbstractIndexedEmbedding<java.lang.String>

Parameters:

item - the item to embed

Returns:

the index of the item in the embedding

unembed

public java.util.Optional<java.lang.String> unembed(long index)

Description copied from interface: AbstractIndexedEmbedding

Returns the item corresponding to the given index.

Specified by:

unembed in interface AbstractIndexedEmbedding<java.lang.String>

Parameters:

index - the index

Returns:

the item corresponding to the given index

builder

public static TrainableWordEmbedding.Builder builder()

Creates a builder to build an Embedding.

Returns:

a new builder

hasItem

public boolean hasItem(java.lang.String item)

Description copied from interface: AbstractEmbedding

Returns whether an item is in the embedding.

Specified by:

hasItem in interface AbstractEmbedding<java.lang.String>

Parameters:

item - the item to test

Returns:

true if the item is in the embedding