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svlandeg 2020-08-18 18:55:56 +02:00
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website/docs/api/architectures.md
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@ -546,7 +546,7 @@ network has an internal CNN Tok2Vec layer and uses attention.
<!-- TODO: model return type -->
| Name | Description |
| -------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| -------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `pretrained_vectors` | Whether or not pretrained vectors will be used in addition to the feature vectors. ~~bool~~ |
| `width` | Output dimension of the feature encoding step. ~~int~~ |
@ -586,7 +586,7 @@ architecture is usually less accurate than the ensemble, but runs faster.
<!-- TODO: model return type -->
| Name | Description |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `tok2vec` | The [`tok2vec`](#tok2vec) layer of the model. ~~Model~~ |
| `nO` | Output dimension, determined by the number of different labels. If not set, the [`TextCategorizer`](/api/textcategorizer) component will set it when `begin_training` is called. ~~Optional[int]~~ |
@ -611,7 +611,7 @@ others, but may not be as accurate, especially if texts are short.
<!-- TODO: model return type -->
| Name | Description |
| ------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| ------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `exclusive_classes` | Whether or not categories are mutually exclusive. ~~bool~~ |
| `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~~ |
| `no_output_layer` | Whether or not to add an output layer to the model (`Softmax` activation if `exclusive_classes` is `True`, else `Logistic`. ~~bool~~ |