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Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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Co-authored-by: Marcus Blättermann <marcus@essenmitsosse.de>
310 lines
17 KiB
Plaintext
310 lines
17 KiB
Plaintext
---
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title: Legacy functions and architectures
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teaser: Archived implementations available through spacy-legacy
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source: spacy/legacy
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---
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The [`spacy-legacy`](https://github.com/explosion/spacy-legacy) package includes
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outdated registered functions and architectures. It is installed automatically
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as a dependency of spaCy, and provides backwards compatibility for archived
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functions that may still be used in projects.
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You can find the detailed documentation of each such legacy function on this
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page.
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## Architectures {id="architectures"}
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These functions are available from `@spacy.registry.architectures`.
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### spacy.Tok2Vec.v1 {id="Tok2Vec_v1"}
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The `spacy.Tok2Vec.v1` architecture was expecting an `encode` model of type
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`Model[Floats2D, Floats2D]` such as `spacy.MaxoutWindowEncoder.v1` or
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`spacy.MishWindowEncoder.v1`.
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> #### Example config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.Tok2Vec.v1"
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>
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> [model.embed]
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> @architectures = "spacy.CharacterEmbed.v1"
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> # ...
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>
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> [model.encode]
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> @architectures = "spacy.MaxoutWindowEncoder.v1"
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> # ...
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> ```
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Construct a tok2vec model out of two subnetworks: one for embedding and one for
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encoding. See the
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["Embed, Encode, Attend, Predict"](https://explosion.ai/blog/deep-learning-formula-nlp)
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blog post for background.
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| Name | Description |
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| ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `embed` | Embed tokens into context-independent word vector representations. For example, [CharacterEmbed](/api/architectures#CharacterEmbed) or [MultiHashEmbed](/api/architectures#MultiHashEmbed). ~~Model[List[Doc], List[Floats2d]]~~ |
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| `encode` | Encode context into the embeddings, using an architecture such as a CNN, BiLSTM or transformer. For example, [MaxoutWindowEncoder.v1](/api/legacy#MaxoutWindowEncoder_v1). ~~Model[Floats2d, Floats2d]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], List[Floats2d]]~~ |
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### spacy.MaxoutWindowEncoder.v1 {id="MaxoutWindowEncoder_v1"}
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The `spacy.MaxoutWindowEncoder.v1` architecture was producing a model of type
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`Model[Floats2D, Floats2D]`. Since `spacy.MaxoutWindowEncoder.v2`, this has been
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changed to output type `Model[List[Floats2d], List[Floats2d]]`.
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> #### Example config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.MaxoutWindowEncoder.v1"
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> width = 128
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> window_size = 1
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> maxout_pieces = 3
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> depth = 4
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> ```
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Encode context using convolutions with maxout activation, layer normalization
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and residual connections.
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| Name | Description |
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| --------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `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~~ |
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| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
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| `maxout_pieces` | The number of maxout pieces to use. Recommended values are `2` or `3`. ~~int~~ |
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| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
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| **CREATES** | The model using the architecture. ~~Model[Floats2d, Floats2d]~~ |
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### spacy.MishWindowEncoder.v1 {id="MishWindowEncoder_v1"}
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The `spacy.MishWindowEncoder.v1` architecture was producing a model of type
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`Model[Floats2D, Floats2D]`. Since `spacy.MishWindowEncoder.v2`, this has been
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changed to output type `Model[List[Floats2d], List[Floats2d]]`.
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> #### Example config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.MishWindowEncoder.v1"
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> width = 64
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> window_size = 1
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> depth = 4
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> ```
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Encode context using convolutions with
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[`Mish`](https://thinc.ai/docs/api-layers#mish) activation, layer normalization
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and residual connections.
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| Name | Description |
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| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `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~~ |
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| `window_size` | The number of words to concatenate around each token to construct the convolution. Recommended value is `1`. ~~int~~ |
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| `depth` | The number of convolutional layers. Recommended value is `4`. ~~int~~ |
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| **CREATES** | The model using the architecture. ~~Model[Floats2d, Floats2d]~~ |
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### spacy.HashEmbedCNN.v1 {id="HashEmbedCNN_v1"}
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Identical to [`spacy.HashEmbedCNN.v2`](/api/architectures#HashEmbedCNN) except
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using [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are included.
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### spacy.MultiHashEmbed.v1 {id="MultiHashEmbed_v1"}
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Identical to [`spacy.MultiHashEmbed.v2`](/api/architectures#MultiHashEmbed)
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except with [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are
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included.
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### spacy.CharacterEmbed.v1 {id="CharacterEmbed_v1"}
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Identical to [`spacy.CharacterEmbed.v2`](/api/architectures#CharacterEmbed)
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except using [`spacy.StaticVectors.v1`](#StaticVectors_v1) if vectors are
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included.
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### spacy.TextCatEnsemble.v1 {id="TextCatEnsemble_v1"}
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The `spacy.TextCatEnsemble.v1` architecture built an internal `tok2vec` and
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`linear_model`. Since `spacy.TextCatEnsemble.v2`, this has been refactored so
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that the `TextCatEnsemble` takes these two sublayers as input.
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TextCatEnsemble.v1"
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> exclusive_classes = false
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> pretrained_vectors = null
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> width = 64
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> embed_size = 2000
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> conv_depth = 2
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> window_size = 1
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> ngram_size = 1
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> dropout = null
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> nO = null
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> ```
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Stacked ensemble of a bag-of-words model and a neural network model. The neural
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network has an internal CNN Tok2Vec layer and uses attention.
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| Name | Description |
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| -------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
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| `pretrained_vectors` | Whether or not pretrained vectors will be used in addition to the feature vectors. ~~bool~~ |
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| `width` | Output dimension of the feature encoding step. ~~int~~ |
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| `embed_size` | Input dimension of the feature encoding step. ~~int~~ |
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| `conv_depth` | Depth of the tok2vec layer. ~~int~~ |
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| `window_size` | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. ~~int~~ |
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| `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~~ |
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| `dropout` | The dropout rate. ~~float~~ |
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| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TextCatCNN.v1 {id="TextCatCNN_v1"}
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Since `spacy.TextCatCNN.v2`, this architecture has become resizable, which means
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that you can add labels to a previously trained textcat. `TextCatCNN` v1 did not
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yet support that.
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TextCatCNN.v1"
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> exclusive_classes = false
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> nO = null
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>
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> [model.tok2vec]
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> @architectures = "spacy.HashEmbedCNN.v1"
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> pretrained_vectors = null
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> width = 96
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> depth = 4
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> embed_size = 2000
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> window_size = 1
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> maxout_pieces = 3
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> subword_features = true
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> ```
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A neural network model where token vectors are calculated using a CNN. The
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vectors are mean pooled and used as features in a feed-forward network. This
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architecture is usually less accurate than the ensemble, but runs faster.
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| Name | Description |
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| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
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| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
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| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TextCatBOW.v1 {id="TextCatBOW_v1"}
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Since `spacy.TextCatBOW.v2`, this architecture has become resizable, which means
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that you can add labels to a previously trained textcat. `TextCatBOW` v1 did not
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yet support that.
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> #### Example Config
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>
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> ```ini
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> [model]
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> @architectures = "spacy.TextCatBOW.v1"
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> exclusive_classes = false
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> ngram_size = 1
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> no_output_layer = false
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> nO = null
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> ```
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An n-gram "bag-of-words" model. This architecture should run much faster than
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the others, but may not be as accurate, especially if texts are short.
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| Name | Description |
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| ------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
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| `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~~ |
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| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`). ~~bool~~ |
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| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `initialize` is called. ~~Optional[int]~~ |
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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TransitionBasedParser.v1 {id="TransitionBasedParser_v1"}
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Identical to
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[`spacy.TransitionBasedParser.v2`](/api/architectures#TransitionBasedParser)
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except the `use_upper` was set to `True` by default.
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## Layers {id="layers"}
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These functions are available from `@spacy.registry.layers`.
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### spacy.StaticVectors.v1 {id="StaticVectors_v1"}
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Identical to [`spacy.StaticVectors.v2`](/api/architectures#StaticVectors) except
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for the handling of tokens without vectors.
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<Infobox title="Bugs for tokens without vectors" variant="warning">
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`spacy.StaticVectors.v1` maps tokens without vectors to the final row in the
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vectors table, which causes the model predictions to change if new vectors are
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added to an existing vectors table. See more details in
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[issue #7662](https://github.com/explosion/spaCy/issues/7662#issuecomment-813925655).
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</Infobox>
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## Loggers {id="loggers"}
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These functions are available from `@spacy.registry.loggers`.
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### spacy.ConsoleLogger.v1 {id="ConsoleLogger_v1"}
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> #### Example config
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>
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> ```ini
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> [training.logger]
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> @loggers = "spacy.ConsoleLogger.v1"
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> progress_bar = true
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> ```
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Writes the results of a training step to the console in a tabular format.
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<Accordion title="Example console output" spaced>
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```bash
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$ python -m spacy train config.cfg
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```
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```
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ℹ Using CPU
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ℹ Loading config and nlp from: config.cfg
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ℹ Pipeline: ['tok2vec', 'tagger']
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ℹ Start training
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ℹ Training. Initial learn rate: 0.0
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E # LOSS TOK2VEC LOSS TAGGER TAG_ACC SCORE
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--- ------ ------------ ----------- ------- ------
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0 0 0.00 86.20 0.22 0.00
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0 200 3.08 18968.78 34.00 0.34
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0 400 31.81 22539.06 33.64 0.34
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0 600 92.13 22794.91 43.80 0.44
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0 800 183.62 21541.39 56.05 0.56
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0 1000 352.49 25461.82 65.15 0.65
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0 1200 422.87 23708.82 71.84 0.72
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0 1400 601.92 24994.79 76.57 0.77
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0 1600 662.57 22268.02 80.20 0.80
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0 1800 1101.50 28413.77 82.56 0.83
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0 2000 1253.43 28736.36 85.00 0.85
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0 2200 1411.02 28237.53 87.42 0.87
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0 2400 1605.35 28439.95 88.70 0.89
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```
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Note that the cumulative loss keeps increasing within one epoch, but should
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start decreasing across epochs.
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</Accordion>
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| Name | Description |
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| -------------- | --------------------------------------------------------- |
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| `progress_bar` | Whether the logger should print the progress bar ~~bool~~ |
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Logging utilities for spaCy are implemented in the
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[`spacy-loggers`](https://github.com/explosion/spacy-loggers) repo, and the
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functions are typically available from `@spacy.registry.loggers`.
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More documentation can be found in that repo's
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[readme](https://github.com/explosion/spacy-loggers/blob/main/README.md) file.
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