Migration Guide

This page contains information about changes between major versions and how you can migrate from one version to another.

Rasa 1.7 to Rasa 1.8

Warning

This is a release breaking backwards compatibility. It is not possible to load previously trained models. Please make sure to retrain a model before trying to use it with this improved version.

General

The TED Policy replaced the Keras Policy as recommended machine learning policy. New projects generated with rasa init will automatically use this policy. In case you want to change your existing model configuration to use the TED Policy add this to the policies section in your config.yml and remove potentially existing KerasPolicy entries:

policies:
# - ... other policies
- name: TEDPolicy
  max_history: 5
  epochs: 100

All pre-defined pipeline templates are deprecated. Any templates you use will be mapped to the new configuration, but the underlying architecture is the same. Take a look at Choosing a Pipeline to decide on what components you should use in your configuration file.
The Embedding Policy was renamed to TED Policy. The functionality of the policy stayed the same. Please update your configuration files to use TEDPolicy instead of EmbeddingPolicy.
Most of the model options for EmbeddingPolicy, EmbeddingIntentClassifier, and ResponseSelector got renamed. Please update your configuration files using the following mapping:

Old model option	New model option
hidden_layers_sizes_a	dictionary “hidden_layers_sizes” with key “text”
hidden_layers_sizes_b	dictionary “hidden_layers_sizes” with key “label”
hidden_layers_sizes_pre_dial	dictionary “hidden_layers_sizes” with key “dialogue”
hidden_layers_sizes_bot	dictionary “hidden_layers_sizes” with key “label”
num_transformer_layers	number_of_transformer_layers
num_heads	number_of_attention_heads
max_seq_length	maximum_sequence_length
dense_dim	dense_dimension
embed_dim	embedding_dimension
num_neg	number_of_negative_examples
mu_pos	maximum_positive_similarity
mu_neg	maximum_negative_similarity
use_max_sim_neg	use_maximum_negative_similarity
C2	regularization_constant
C_emb	negative_margin_scale
droprate_a	droprate_dialogue
droprate_b	droprate_label
evaluate_every_num_epochs	evaluate_every_number_of_epochs
evaluate_on_num_examples	evaluate_on_number_of_examples

Old configuration options will be mapped to the new names, and a warning will be thrown. However, these will be deprecated in a future release.

EmbeddingIntentClassifier is now deprecated and will be replaced by DIETClassifier in the future. DIETClassifier performs intent classification as well as entity recognition. If you want to get the same model behaviour as the current EmbeddingIntentClassifier, you can use the following configuration of DIETClassifier:

pipeline:
# - ... other components
- name: DIETClassifier
  hidden_layers_sizes:
    text: [256, 128]
  number_of_transformer_layers: 0
  weight_sparsity: 0
  intent_classification: True
  entity_recognition: False
  use_masked_language_model: False
  BILOU_flag: False
  # ... any other parameters

See DIETClassifier for more information about the new component. Specifying EmbeddingIntentClassifier in the configuration maps to the above component definition, the behaviour is unchanged from previous versions.

CRFEntityExtractor is now deprecated and will be replaced by DIETClassifier in the future. If you want to get the same model behaviour as the current CRFEntityExtractor, you can use the following configuration:

pipeline:
# - ... other components
- name: LexicalSyntacticFeaturizer
  features: [
    ["low", "title", "upper"],
    [
      "BOS",
      "EOS",
      "low",
      "prefix5",
      "prefix2",
      "suffix5",
      "suffix3",
      "suffix2",
      "upper",
      "title",
      "digit",
    ],
    ["low", "title", "upper"],
]
- name: DIETClassifier
  intent_classification: False
  entity_recognition: True
  use_masked_language_model: False
  number_of_transformer_layers: 0
  # ... any other parameters

CRFEntityExtractor featurizes user messages on its own, it does not depend on any featurizer. We extracted the featurization from the component into the new featurizer. Thus, in order to obtain the same results as before, you need to add this featurizer to your pipeline before the :ref:diet-classifier. Specifying CRFEntityExtractor` in the configuration maps to the above component definition, the behaviour is unchanged from previous versions.

If your pipeline contains CRFEntityExtractor and EmbeddingIntentClassifier you can substitute both components with DIETClassifier. You can use the following pipeline for that:

pipeline:
# - ... other components
- name: LexicalSyntacticFeaturizer
  features: [
    ["low", "title", "upper"],
    [
      "BOS",
      "EOS",
      "low",
      "prefix5",
      "prefix2",
      "suffix5",
      "suffix3",
      "suffix2",
      "upper",
      "title",
      "digit",
    ],
    ["low", "title", "upper"],
]
- name: DIETClassifier
  number_of_transformer_layers: 0
  # ... any other parameters

Rasa 1.6 to Rasa 1.7

General

By default, the EmbeddingIntentClassifier, EmbeddingPolicy, and ResponseSelector will now normalize the top 10 confidence results if the loss_type is "softmax" (which has been default since 1.3).

Rasa 1.2 to Rasa 1.3

Warning

General

Default parameters of EmbeddingIntentClassifier are changed. Architecture implementation is changed as well, so old trained models cannot be loaded.
/ is reserved as a delimiter token to distinguish between retrieval intent and the corresponding response text identifier. Make sure you don’t include / symbol in the name of your intents.

Rasa NLU 0.14.x and Rasa Core 0.13.x to Rasa 1.0

Warning

General

The scripts in rasa.core and rasa.nlu can no longer be executed. To train, test, run, … an NLU or Core model, you should now use the command line interface rasa. The functionality is, for the most part, the same as before.