# 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/1.8.3/core/policies/#ted-policy) replaced the [Keras Policy](https://legacy-docs-v1.rasa.com/1.8.3/core/policies/#keras-policy) as the 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/1.8.3/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
    ```

- 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**.

- The [Embedding Policy](https://legacy-docs-v1.rasa.com/1.8.3/core/policies/#embedding-policy) was renamed to [TED Policy](https://legacy-docs-v1.rasa.com/1.8.3/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 |

- [EmbeddingIntentClassifier](https://legacy-docs-v1.rasa.com/1.8.3/nlu/components/#embedding-intent-classifier) is now deprecated and will be replaced by [DIETClassifier](https://legacy-docs-v1.rasa.com/1.8.3/nlu/components/#diet-classifier) in the future. 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
    ```

- If your pipeline contains `CRFEntityExtractor` and `EmbeddingIntentClassifier`, you can substitute both components with [DIETClassifier](https://legacy-docs-v1.rasa.com/1.8.3/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). This is configurable via the `ranking_length` configuration parameter; to turn off normalization to match the previous behavior, set `ranking_length: 0`.

## Rasa 1.2 to Rasa 1.3

### General

- Default parameters of `EmbeddingIntentClassifier` are changed. See [Components](https://legacy-docs-v1.rasa.com/1.8.3/nlu/components/#components) for details.

*... (subsequent sections would follow the same pattern, referring to specific versions and changes within the documentation)*

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