# 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](https://legacy-docs-v1.rasa.com/1.9.4/core/policies/#ted-policy) replaced the [Keras Policy](https://legacy-docs-v1.rasa.com/1.9.4/core/policies/#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](https://legacy-docs-v1.rasa.com/1.9.4/core/policies/#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
    
    
The given snippet specifies default values for the parameters `max_history` and `epochs`. `max_history` is particularly important and strongly depends on your stories. Please see the docs of the [TED Policy](https://legacy-docs-v1.rasa.com/1.9.4/core/policies/#ted-policy) if you want to customize them.

- 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](https://legacy-docs-v1.rasa.com/1.9.4/nlu/choosing-a-pipeline/#choosing-a-pipeline) to decide on what components you should use in your configuration file.

- The [Embedding Policy](https://legacy-docs-v1.rasa.com/1.9.4/core/policies/#embedding-policy) was renamed to [TED Policy](https://legacy-docs-v1.rasa.com/1.9.4/core/policies/#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](https://legacy-docs-v1.rasa.com/1.9.4/nlu/components/#embedding-intent-classifier) is now deprecated and will be replaced by [DIETClassifier](https://legacy-docs-v1.rasa.com/1.9.4/nlu/components/#diet-classifier) in the future.  
`DIETClassfier` 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](https://legacy-docs-v1.rasa.com/1.9.4/nlu/components/#diet-classifier) 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 :ref:`LexicalSyntacticFeaturizer`. 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](https://legacy-docs-v1.rasa.com/1.9.4/nlu/components/#diet-classifier). 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 `