Migration Guide

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

    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](https://legacy-docs-v1.rasa.com/1.10.6/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/1.10.6/core/policies/#embedding-policy) was renamed to [TED Policy](https://legacy-docs-v1.rasa.com/1.10.6/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/1.10.6/nlu/components/#embedding-intent-classifier) is now deprecated and will be replaced by [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.6/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/1.10.6/nlu/components/#diet-classifier) for more information about the new component. Stipulating `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:

```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 [LexicalSyntacticFeaturizer](https://legacy-docs-v1.rasa.com/1.10.6/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/1.10.6/nlu/components/#diet-classifier).

- If your pipeline contains `CRFEntityExtractor` and `EmbeddingIntentClassifier`, you can substitute both components with [DIETClassifier](https://legacy-docs-v1.rasa.com/1.10.6/nlu/components/#diet-classifier). You can use the following pipeline for that:

```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
      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 the default since 1.3).

## Rasa 1.2 to Rasa 1.3

Warning

### General

- Default parameters of `EmbeddingIntentClassifier` are changed. See [Components](https://legacy-docs-v1.rasa.com/1.10.6/nlu/components/#components) for details.