Add TextCategorizer API docs stub

website/docs/api/_data.json

@@ -21,6 +21,7 @@
             "Tagger": "tagger",
             "DependencyParser": "dependencyparser",
             "EntityRecognizer": "entityrecognizer",
+            "TextCategorizer": "textcategorizer",
             "Matcher": "matcher",
             "Lexeme": "lexeme",
             "Vocab": "vocab",
@@ -130,6 +131,12 @@
         "source": "spacy/pipeline.pyx"
     },
 
+    "textcategorizer": {
+        "title": "TextCategorizer",
+        "tag": "class",
+        "source": "spacy/pipeline.pyx"
+    },
+
     "dependencyparser": {
         "title": "DependencyParser",
         "tag": "class",

website/docs/api/textcategorizer.jade

@@ -0,0 +1,21 @@
+//- 💫 DOCS > API > TEXTCATEGORIZER
+
+include ../../_includes/_mixins
+
+p
+    |  Add text categorization models to spaCy pipelines. The model supports
+    |  classification with multiple, non-mutually exclusive labels.
+
+p
+    |  You can change the model architecture rather easily, but by default, the
+    |  #[code TextCategorizer] class uses a convolutional neural network to
+    |  assign position-sensitive vectors to each word in the document. This step
+    |  is similar to the #[+api("tensorizer") #[code Tensorizer]] component, but the
+    |  #[code TextCategorizer] uses its own CNN model, to avoid sharing weights
+    |  with the other pipeline components. The document tensor is then
+    |  summarized by concatenating max and mean pooling, and a multilayer
+    |  perceptron is used to predict an output vector of length #[code nr_class],
+    |  before a logistic activation is applied elementwise. The value of each
+    |  output neuron is the probability that some class is present.
+
++under-construction