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@ -545,18 +545,18 @@ 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~~ |
| `embed_size` | Input dimension of the feature encoding step. ~~int~~ |
| `conv_depth` | Depth of the tok2vec layer. ~~int~~ |
| `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~~ |
| `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~~ |
| `dropout` | The dropout rate. ~~float~~ |
| 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~~ |
| `embed_size` | Input dimension of the feature encoding step. ~~int~~ |
| `conv_depth` | Depth of the tok2vec layer. ~~int~~ |
| `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~~ |
| `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~~ |
| `dropout` | The dropout rate. ~~float~~ |
| `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]~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
### spacy.TextCatCNN.v1 {#TextCatCNN}
@ -585,12 +585,12 @@ 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~~ |
| 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]~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
### spacy.TextCatBOW.v1 {#TextCatBOW}
@ -610,13 +610,13 @@ 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~~ |
| 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~~ |
| `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]~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
## Entity linking architectures {#entitylinker source="spacy/ml/models/entity_linker.py"}