# 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/1.10.12/core/policies/#ted-policy) replaced the [Keras Policy](https://legacy-docs-v1.rasa.com/1.10.12/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/1.10.12/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/1.10.12/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/1.10.12/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/1.10.12/core/policies/#embedding-policy) was renamed to [TED Policy](https://legacy-docs-v1.rasa.com/1.10.12/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/1.10.12/nlu/components/#embedding-intent-classifier) is now deprecated and will be replaced by [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.12/nlu/components/#diet-classifier) in the future. `DIETClassfier` performs intent classification as well as entity recognition. If you want to get the same model behavior 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/1.10.12/nlu/components/#diet-classifier) for more information about the new component. Specifying `EmbeddingIntentClassifier` in the configuration maps to the above component definition, the behavior 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 behavior 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 [LexicalSyntacticFeaturizer](https://legacy-docs-v1.rasa.com/1.10.12/nlu/components/#lexicalsyntacticfeaturizer). Thus, in order to obtain the same results as before, you need to add this featurizer to your pipeline before the [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.12/nlu/components/#diet-classifier). Specifying `CRFEntityExtractor` in the configuration maps to the above component definition, the behavior 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/1.10.12/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 `"softmax"` (which has been default since 1.3).

## Rasa 1.2 to Rasa 1.3

### General

- Default parameters of `EmbeddingIntentClassifier` are changed... etc.

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