17 KiB
| 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outdated registered functions and architectures.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. |
encode |
Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, MaxoutWindowEncoder.v1. |
| CREATES | The model using the architecture. |
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. |
window_size |
The number of words to concatenate around each token to construct the convolution. Recommended value is 1. |
maxout_pieces |
The number of maxout pieces to use. Recommended values are 2 or 3. |
depth |
The number of convolutional layers. Recommended value is 4. |
| CREATES | The model using the architecture. |
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. |
window_size |
The number of words to concatenate around each token to construct the convolution. Recommended value is 1. |
depth |
The number of convolutional layers. Recommended value is 4. |
| CREATES | The model using the architecture. |
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.
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. |
pretrained_vectors |
Whether or not pretrained vectors will be used in addition to the feature vectors. |
width |
Output dimension of the feature encoding step. |
embed_size |
Input dimension of the feature encoding step. |
conv_depth |
Depth of the tok2vec layer. |
window_size |
The number of contextual vectors to concatenate from the left and from the right. |
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. |
dropout |
The dropout rate. |
nO |
Output dimension, determined by the number of different labels. If not set, the TextCategorizer component will set it when initialize is called. |
| CREATES | The model using the architecture. |
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. |
tok2vec |
The tok2vec layer of the model. |
nO |
Output dimension, determined by the number of different labels. If not set, the TextCategorizer component will set it when initialize is called. |
| CREATES | The model using the architecture. |
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. |
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. |
no_output_layer |
Whether or not to add an output layer to the model (Softmax activation if exclusive_classes is True, else Logistic). |
nO |
Output dimension, determined by the number of different labels. If not set, the TextCategorizer component will set it when initialize is called. |
| CREATES | The model using the architecture. |
spacy.TransitionBasedParser.v1
Identical to
spacy.TransitionBasedParser.v2
except the use_upper was set to True by default.
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.
Loggers
These functions are available from @spacy.registry.loggers.
spacy.ConsoleLogger.v1
Example config
[training.logger] @loggers = "spacy.ConsoleLogger.v1" progress_bar = true
Writes the results of a training step to the console in a tabular format.
$ python -m spacy train config.cfg
ℹ Using CPU
ℹ Loading config and nlp from: config.cfg
ℹ Pipeline: ['tok2vec', 'tagger']
ℹ Start training
ℹ Training. Initial learn rate: 0.0
E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE
--- ------ ------------ ----------- ------- ------
0 0 0.00 86.20 0.22 0.00
0 200 3.08 18968.78 34.00 0.34
0 400 31.81 22539.06 33.64 0.34
0 600 92.13 22794.91 43.80 0.44
0 800 183.62 21541.39 56.05 0.56
0 1000 352.49 25461.82 65.15 0.65
0 1200 422.87 23708.82 71.84 0.72
0 1400 601.92 24994.79 76.57 0.77
0 1600 662.57 22268.02 80.20 0.80
0 1800 1101.50 28413.77 82.56 0.83
0 2000 1253.43 28736.36 85.00 0.85
0 2200 1411.02 28237.53 87.42 0.87
0 2400 1605.35 28439.95 88.70 0.89
Note that the cumulative loss keeps increasing within one epoch, but should start decreasing across epochs.
| Name | Description |
|---|---|
progress_bar |
Whether the logger should print the progress bar |
Logging utilities for spaCy are implemented in the
spacy-loggers repo, and the
functions are typically available from @spacy.registry.loggers.
More documentation can be found in that repo's readme file.