Add documentation

examples/training/train_new_entity_type.py

@@ -1,3 +1,32 @@
+#!/usr/bin/env python
+"""
+Example of training and additional entity type
+
+This script shows how to add a new entity type to an existing pre-trained NER
+model. To keep the example short and simple, only four sentences are provided
+as examples. In practice, you'll need many more — a few hundred would be a
+good start. You will also likely need to mix in examples of other entity
+types, which might be obtained by running the entity recognizer over unlabelled
+sentences, and adding their annotations to the training set.
+
+The actual training is performed by looping over the examples, and calling
+`nlp.entity.update()`. The `update()` method steps through the words of the
+input. At each word, it makes a prediction. It then consults the annotations
+provided on the GoldParse instance, to see whether it was right. If it was
+wrong, it adjusts its weights so that the correct action will score higher
+next time.
+
+After training your model, you can save it to a directory. We recommend
+wrapping models as Python packages, for ease of deployment.
+
+For more details, see the documentation:
+* Training the Named Entity Recognizer: https://spacy.io/docs/usage/train-ner
+* Saving and loading models: https://spacy.io/docs/usage/saving-loading
+
+Developed for: spaCy 1.7.6
+Last tested for: spaCy 1.7.6
+"""
+# coding: utf8
 from __future__ import unicode_literals, print_function
 
 import random