You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Copy file name to clipboardExpand all lines: docs/nlu/training-data-format.rst
-14Lines changed: 0 additions & 14 deletions
Display the source diff
Display the rich diff
Original file line number
Diff line number
Diff line change
@@ -223,17 +223,3 @@ Alternatively, you can add an "entity_synonyms" array to define several synonyms
223
223
.. note::
224
224
Please note that adding synonyms using the above format does not improve the model's classification of those entities.
225
225
**Entities must be properly classified before they can be replaced with the synonym value.**
226
-
227
-
228
-
Generating More Entity Examples
229
-
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
230
-
231
-
It is sometimes helpful to generate a bunch of entity examples, for
232
-
example if you have a database of restaurant names. There are a couple
233
-
of tools built by the community to help with that.
234
-
235
-
You can use `Chatito <https://rodrigopivi.github.io/Chatito/>`__ , a tool for generating training datasets in rasa's format using a simple DSL or `Tracy <https://yuukanoo.github.io/tracy>`__, a simple GUI to create training datasets for rasa.
236
-
237
-
However, creating synthetic examples usually leads to overfitting,
238
-
it is a better idea to use :ref:`lookup-tables` instead if you have a large number
description = "Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants"
0 commit comments