Redirect readers to the model catalog for configuration examples
@@ -19,7 +19,7 @@ OpenNMT-tf focuses on modularity to support advanced modeling and training capab
* **on-the-fly tokenization**<br/>apply advanced tokenization dynamically during the training and detokenize the predictions during inference or evaluation.
* **automatic evaluation**<br/>support for saving evaluation predictions and running external evaluators (e.g. BLEU).
-and all of the above can be used simultaneously to train novel and complex architectures. See the [predefined models](config/models) to discover how they are defined and the [API documentation](http://opennmt.net/OpenNMT-tf/package/opennmt.html) to customize them.
+and all of the above can be used simultaneously to train novel and complex architectures. See the [predefined models](opennmt/models/catalog.py) to discover how they are defined and the [API documentation](http://opennmt.net/OpenNMT-tf/package/opennmt.html) to customize them.
OpenNMT-tf is also compatible with some of the best TensorFlow features:
