# 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

### General

- The [TED Policy](https://legacy-docs-v1.rasa.com/1.9.5/core/policies/#ted-policy) replaced the [Keras Policy](https://legacy-docs-v1.rasa.com/1.9.5/core/policies/#keras-policy) as recommended machine learning policy. New projects generated with `rasa init` will automatically use this policy. If you want to change your existing model configuration to use the [TED Policy](https://legacy-docs-v1.rasa.com/1.9.5/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/1.9.5/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/1.9.5/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/1.9.5/core/policies/#embedding-policy) was renamed to [TED Policy](https://legacy-docs-v1.rasa.com/1.9.5/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/1.9.5/nlu/components/#embedding-intent-classifier) is now deprecated and will be replaced by [DIETClassifier](https://legacy-docs-v1.rasa.com/1.9.5/nlu/components/#diet-classifier) in the future.

```yaml
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/1.9.5/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 behavior as the current `CRFEntityExtractor`, you can use the following configuration:

```yaml
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`.

## 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

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.