spaCy/website/docs/api/legacy.md

title	teaser	source
Legacy functions and architectures	Archived implementations available through spacy-legacy	spacy/legacy

The spacy-legacy package includes outdated registered functions and architectures. It is installed automatically as a dependency of spaCy, and provides backwards compatibility for archived functions that may still be used in projects.

You can find the detailed documentation of each such legacy function on this page.

Architectures

These functions are available from @spacy.registry.architectures.

spacy.Tok2Vec.v1

The spacy.Tok2Vec.v1 architecture was expecting an encode model of type Model[Floats2D, Floats2D] such as spacy.MaxoutWindowEncoder.v1 or spacy.MishWindowEncoder.v1.

Example config

[model]
@architectures = "spacy.Tok2Vec.v1"

[model.embed]
@architectures = "spacy.CharacterEmbed.v1"
# ...

[model.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
# ...

Construct a tok2vec model out of two subnetworks: one for embedding and one for encoding. See the "Embed, Encode, Attend, Predict" blog post for background.

Name	Description
embed	Embed tokens into context-independent word vector representations. For example, CharacterEmbed or MultiHashEmbed. Model[List[Doc], List[Floats2d]]
encode	Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, MaxoutWindowEncoder.v1. Model[Floats2d, Floats2d]
CREATES	The model using the architecture. Model[List[Doc], List[Floats2d]]

spacy.MaxoutWindowEncoder.v1

The spacy.MaxoutWindowEncoder.v1 architecture was producing a model of type Model[Floats2D, Floats2D]. Since spacy.MaxoutWindowEncoder.v2, this has been changed to output type Model[List[Floats2d], List[Floats2d]].

Example config

[model]
@architectures = "spacy.MaxoutWindowEncoder.v1"
width = 128
window_size = 1
maxout_pieces = 3
depth = 4

Encode context using convolutions with maxout activation, layer normalization and residual connections.

Name	Description
width	The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between 64 and 300. int
window_size	The number of words to concatenate around each token to construct the convolution. Recommended value is 1. int
maxout_pieces	The number of maxout pieces to use. Recommended values are 2 or 3. int
depth	The number of convolutional layers. Recommended value is 4. int
CREATES	The model using the architecture. Model[Floats2d, Floats2d]

spacy.MishWindowEncoder.v1

The spacy.MishWindowEncoder.v1 architecture was producing a model of type Model[Floats2D, Floats2D]. Since spacy.MishWindowEncoder.v2, this has been changed to output type Model[List[Floats2d], List[Floats2d]].

Example config

[model]
@architectures = "spacy.MishWindowEncoder.v1"
width = 64
window_size = 1
depth = 4

Encode context using convolutions with Mish activation, layer normalization and residual connections.

Name	Description
width	The input and output width. These are required to be the same, to allow residual connections. This value will be determined by the width of the inputs. Recommended values are between 64 and 300. int
window_size	The number of words to concatenate around each token to construct the convolution. Recommended value is 1. int
depth	The number of convolutional layers. Recommended value is 4. int
CREATES	The model using the architecture. Model[Floats2d, Floats2d]

spacy.TextCatEnsemble.v1

The spacy.TextCatEnsemble.v1 architecture built an internal tok2vec and linear_model. Since spacy.TextCatEnsemble.v2, this has been refactored so that the TextCatEnsemble takes these two sublayers as input.

Example Config

[model]
@architectures = "spacy.TextCatEnsemble.v1"
exclusive_classes = false
pretrained_vectors = null
width = 64
embed_size = 2000
conv_depth = 2
window_size = 1
ngram_size = 1
dropout = null
nO = null

Stacked ensemble of a bag-of-words model and a neural network model. The neural network has an internal CNN Tok2Vec layer and uses attention.

Name	Description
exclusive_classes	Whether or not categories are mutually exclusive. bool
pretrained_vectors	Whether or not pretrained vectors will be used in addition to the feature vectors. bool
width	Output dimension of the feature encoding step. int
embed_size	Input dimension of the feature encoding step. int
conv_depth	Depth of the tok2vec layer. int
window_size	The number of contextual vectors to concatenate from the left and from the right. int
ngram_size	Determines the maximum length of the n-grams in the BOW model. For instance, ngram_size=3would give unigram, trigram and bigram features. int
dropout	The dropout rate. float
nO	Output dimension, determined by the number of different labels. If not set, the TextCategorizer component will set it when initialize is called. Optional[int]
CREATES	The model using the architecture. Model[List[Doc], Floats2d]

spacy.HashEmbedCNN.v1

Identical to spacy.HashEmbedCNN.v2 except using spacy.StaticVectors.v1 if vectors are included.

spacy.MultiHashEmbed.v1

Identical to spacy.MultiHashEmbed.v2 except with spacy.StaticVectors.v1 if vectors are included.

spacy.CharacterEmbed.v1

Identical to spacy.CharacterEmbed.v2 except using spacy.StaticVectors.v1 if vectors are included.

Layers

These functions are available from @spacy.registry.layers.

spacy.StaticVectors.v1

Identical to spacy.StaticVectors.v2 except for the handling of tokens without vectors.

spacy.StaticVectors.v1 maps tokens without vectors to the final row in the vectors table, which causes the model predictions to change if new vectors are added to an existing vectors table. See more details in issue #7662.

spacy.TextCatCNN.v1

Since spacy.TextCatCNN.v2, this architecture has become resizable, which means that you can add labels to a previously trained textcat. TextCatCNN v1 did not yet support that.

Example Config

[model]
@architectures = "spacy.TextCatCNN.v1"
exclusive_classes = false
nO = null

[model.tok2vec]
@architectures = "spacy.HashEmbedCNN.v1"
pretrained_vectors = null
width = 96
depth = 4
embed_size = 2000
window_size = 1
maxout_pieces = 3
subword_features = true

A neural network model where token vectors are calculated using a CNN. The vectors are mean pooled and used as features in a feed-forward network. This architecture is usually less accurate than the ensemble, but runs faster.

Name	Description
exclusive_classes	Whether or not categories are mutually exclusive. bool
tok2vec	The tok2vec layer of the model. Model
nO	Output dimension, determined by the number of different labels. If not set, the TextCategorizer component will set it when initialize is called. Optional[int]
CREATES	The model using the architecture. Model[List[Doc], Floats2d]

spacy.TextCatBOW.v1

Since spacy.TextCatBOW.v2, this architecture has become resizable, which means that you can add labels to a previously trained textcat. TextCatBOW v1 did not yet support that.

Example Config

[model]
@architectures = "spacy.TextCatBOW.v1"
exclusive_classes = false
ngram_size = 1
no_output_layer = false
nO = null

An n-gram "bag-of-words" model. This architecture should run much faster than the others, but may not be as accurate, especially if texts are short.

Name	Description
exclusive_classes	Whether or not categories are mutually exclusive. bool
ngram_size	Determines the maximum length of the n-grams in the BOW model. For instance, ngram_size=3 would give unigram, trigram and bigram features. int
no_output_layer	Whether or not to add an output layer to the model (Softmax activation if exclusive_classes is True, else Logistic). bool
nO	Output dimension, determined by the number of different labels. If not set, the TextCategorizer component will set it when initialize is called. Optional[int]
CREATES	The model using the architecture. Model[List[Doc], Floats2d]

Loggers

These functions are available from @spacy.registry.loggers.

spacy.WandbLogger.v1

The first version of the WandbLogger did not yet support the log_dataset_dir and model_log_interval arguments.

Example config

[training.logger]
@loggers = "spacy.WandbLogger.v1"
project_name = "monitor_spacy_training"
remove_config_values = ["paths.train", "paths.dev", "corpora.train.path", "corpora.dev.path"]

Name	Description
project_name	The name of the project in the Weights & Biases interface. The project will be created automatically if it doesn't exist yet. str
remove_config_values	A list of values to include from the config before it is uploaded to W&B (default: empty). List[str]