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add textcat architectures documentation
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@ -148,11 +148,113 @@ architectures into your training config.
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## Text classification architectures {#textcat source="spacy/ml/models/textcat.py"}
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A text classification architecture needs to take a `Doc` as input, and produce a
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score for each potential label class. Textcat challenges can be binary (e.g.
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sentiment analysis) or involve multiple possible labels. Multi-label challenges
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can either have mutually exclusive labels (each example has exactly one label),
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or multiple labels may be applicable at the same time.
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As the properties of text classification problems can vary widely, we provide
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several different built-in architectures. It is recommended to experiment with
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different architectures and settings to determine what works best on your
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specific data and challenge.
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### spacy.TextCatEnsemble.v1 {#TextCatEnsemble}
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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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> #### 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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| Name | Type | Description |
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| -------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
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| `pretrained_vectors` | bool | Whether or not pretrained vectors will be used in addition to the feature vectors. |
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| `width` | int | Output dimension of the feature encoding step. |
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| `embed_size` | int | Input dimension of the feature encoding step. |
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| `conv_depth` | int | Depth of the Tok2Vec layer. |
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| `window_size` | int | The number of contextual vectors to [concatenate](https://thinc.ai/docs/api-layers#expand_window) from the left and from the right. |
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| `ngram_size` | int | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. |
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| `dropout` | float | The dropout rate. |
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| `nO` | int | Output dimension, determined by the number of different labels. |
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If the `nO` dimension is not set, the TextCategorizer component will set it when
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`begin_training` is called.
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### spacy.TextCatCNN.v1 {#TextCatCNN}
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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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> dropout = null
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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 | Type | Description |
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| ------------------- | ------------------------------------------ | --------------------------------------------------------------- |
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| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
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| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | The [`tok2vec`](#tok2vec) layer of the model. |
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| `nO` | int | Output dimension, determined by the number of different labels. |
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If the `nO` dimension is not set, the TextCategorizer component will set it when
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`begin_training` is called.
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### spacy.TextCatBOW.v1 {#TextCatBOW}
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### spacy.TextCatCNN.v1 {#TextCatCNN}
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An ngram "bag-of-words" model. This architecture should run much faster than the
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others, but may not be as accurate, especially if texts are short.
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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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| Name | Type | Description |
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| ------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------- |
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| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
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| `ngram_size` | int | Determines the maximum length of the n-grams in the BOW model. For instance, `ngram_size=3`would give unigram, trigram and bigram features. |
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| `no_output_layer` | float | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes=True`, else `Logistic`. |
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| `nO` | int | Output dimension, determined by the number of different labels. |
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If the `nO` dimension is not set, the TextCategorizer component will set it when
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`begin_training` is called.
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### spacy.TextCatLowData.v1 {#TextCatLowData}
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@ -210,17 +312,18 @@ If the `nO` dimension is not set, the Entity Linking component will set it when
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### spacy.EmptyKB.v1 {#EmptyKB}
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A function that creates a default, empty Knowledge Base from a [`Vocab`](/api/vocab) instance.
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A function that creates a default, empty Knowledge Base from a
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[`Vocab`](/api/vocab) instance.
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| Name | Type | Description |
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| ---------------------- | ---- | -------------------------------------------------------- |
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| ---------------------- | ---- | ------------------------------------------------------------------------- |
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| `entity_vector_length` | int | The length of the vectors encoding each entity in the KB - 64 by default. |
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### spacy.CandidateGenerator.v1 {#CandidateGenerator}
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A function that takes as input a [`KnowledgeBase`](/api/kb) and a [`Span`](/api/span) object denoting a
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named entity, and returns a list of plausible
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[`Candidate` objects](/api/kb/#candidate_init).
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A function that takes as input a [`KnowledgeBase`](/api/kb) and a
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[`Span`](/api/span) object denoting a named entity, and returns a list of
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plausible [`Candidate` objects](/api/kb/#candidate_init).
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The default `CandidateGenerator` simply uses the text of a mention to find its
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potential aliases in the Knowledgebase. Note that this function is
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