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Merge branch 'develop' into nightly.spacy.io

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Ines Montani 2020-08-10 00:46:51 +02:00
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website/docs/api/architectures.md
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@ -525,12 +525,11 @@ 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 | Type | Description |
| --------------------------- | ------------------------------------------ | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | The [`tok2vec`](#tok2vec) layer of the model. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when |
| `begin_training` is called. |
| Name | Type | Description |
| ------------------- | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `tok2vec` | [`Model`](https://thinc.ai/docs/api-model) | The [`tok2vec`](#tok2vec) layer of the model. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
### spacy.TextCatBOW.v1 {#TextCatBOW}
@ -548,13 +547,12 @@ architecture is usually less accurate than the ensemble, but runs faster.
An ngram "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 | Type | Description |
| --------------------------- | ----- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `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. |
| `no_output_layer` | float | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes=True`, else `Logistic`. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when |
| `begin_training` is called. |
| Name | Type | Description |
| ------------------- | ----- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| `exclusive_classes` | bool | Whether or not categories are mutually exclusive. |
| `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. |
| `no_output_layer` | float | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes=True`, else `Logistic`. |
| `nO` | int | Output dimension, determined by the number of different labels. If not set, the the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. |
<!-- TODO:
### spacy.TextCatLowData.v1 {#TextCatLowData}