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Fix formatting [ci skip]

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Ines Montani 2019-03-23 16:45:50 +01:00
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website/docs/api/textcategorizer.md
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@ -57,10 +57,10 @@ allows configuration of its model architecture, using the `architecture` keyword
argument.
| Name | Description |
| -------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `"ensemble"` | **Default:** Stacked ensemble of a bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. The "ngram_size" and "attr" arguments can be used to configure the feature extraction for the bag-of-words model.
| -------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `"ensemble"` | **Default:** Stacked ensemble of a bag-of-words model and a neural network model. The neural network uses a CNN with mean pooling and attention. The "ngram_size" and "attr" arguments can be used to configure the feature extraction for the bag-of-words model. |
| `"simple_cnn"` | 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. |
| `"bow"` | 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. The features extracted can be controlled using the keyword arguments ngram_size and attr. For instance, `ngram_size=3` and `attr="lower"` would give lower-cased unigram, trigram and bigram features. 2, 3 or 4 are usually good choices of ngram size. |
| `"bow"` | 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. The features extracted can be controlled using the keyword arguments `ngram_size` and `attr`. For instance, `ngram_size=3` and `attr="lower"` would give lower-cased unigram, trigram and bigram features. 2, 3 or 4 are usually good choices of ngram size. |
## TextCategorizer.\_\_call\_\_ {#call tag="method"}