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https://github.com/explosion/spaCy.git
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Move TextCatCNN docs to legacy, in prep for moving to spacy-legacy
This commit is contained in:
Daniël de Kok
parent
71aa6f4628
commit
9fb573e255
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@ -55,6 +55,7 @@ redirects = [
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{from = "/models/comparison", to = "/models", force = true},
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{from = "/models/comparison", to = "/models", force = true},
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{from = "/api/#section-cython", to = "/api/cython", force = true},
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{from = "/api/#section-cython", to = "/api/cython", force = true},
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{from = "/api/#cython", to = "/api/cython", force = true},
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{from = "/api/#cython", to = "/api/cython", force = true},
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{from = "/api/architectures#TextCatCNN", to = "/api/legacy#TextCatCNN_v2", force = true},
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{from = "/api/sentencesegmenter", to="/api/sentencizer"},
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{from = "/api/sentencesegmenter", to="/api/sentencizer"},
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{from = "/universe", to = "/universe/project/:id", query = {id = ":id"}, force = true},
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{from = "/universe", to = "/universe/project/:id", query = {id = ":id"}, force = true},
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{from = "/universe", to = "/universe/category/:category", query = {category = ":category"}, force = true},
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{from = "/universe", to = "/universe/category/:category", query = {category = ":category"}, force = true},
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@ -1018,49 +1018,6 @@ but used an internal `tok2vec` instead of taking it as argument:
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</Accordion>
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</Accordion>
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### spacy.TextCatCNN.v2 {id="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.v2"
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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.v2"
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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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This model is identical to [TexCatReduce.v1](#TextCatReduce) with
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`use_reduce_mean=true`, `use_reduce_first=false` and `use_reduce_max=false`.
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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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<Accordion title="spacy.TextCatCNN.v1 definition" spaced>
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[TextCatCNN.v1](/api/legacy#TextCatCNN_v1) had the exact same signature, but was
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not yet resizable. Since v2, new labels can be added to this component, even
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after training.
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</Accordion>
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### spacy.TextCatBOW.v3 {id="TextCatBOW"}
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### spacy.TextCatBOW.v3 {id="TextCatBOW"}
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> #### Example Config
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> #### Example Config
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@ -162,7 +162,10 @@ network has an internal CNN Tok2Vec layer and uses attention.
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Since `spacy.TextCatCNN.v2`, this architecture has become resizable, which means
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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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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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yet support that. `TextCatCNN` has been replaced by the more general
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[`TextCatReduce`](/api/architectures#TextCatReduce) layer. `TextCatCNN` is
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identical to `TextCatReduce` with `use_reduce_mean=true`,
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`use_reduce_first=false` and `use_reduce_max=false`.
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> #### Example Config
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> #### Example Config
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>
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>
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@ -194,6 +197,51 @@ architecture is usually less accurate than the ensemble, but runs faster.
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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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| `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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| **CREATES** | The model using the architecture. ~~Model[List[Doc], Floats2d]~~ |
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### spacy.TextCatCNN.v2 {id="TextCatCNN_v2"}
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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.v2"
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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.v2"
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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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|
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`TextCatCNN` has been replaced by the more general
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[`TextCatReduce`](/api/architectures#TextCatReduce) layer. `TextCatCNN` is
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identical to `TextCatReduce` with `use_reduce_mean=true`,
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`use_reduce_first=false` and `use_reduce_max=false`.
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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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<Accordion title="spacy.TextCatCNN.v1 definition" spaced>
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[TextCatCNN.v1](/api/legacy#TextCatCNN_v1) had the exact same signature, but was
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not yet resizable. Since v2, new labels can be added to this component, even
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after training.
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</Accordion>
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### spacy.TextCatBOW.v1 {id="TextCatBOW_v1"}
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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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Since `spacy.TextCatBOW.v2`, this architecture has become resizable, which means
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