You are viewing documentation for our open source project which is maintained by the community. If you want to get started building assistants with Rasa please check out our latest [documentation here](/content/docs/index.html).

#### featurize_training_examples [#](https://legacy-docs-oss.rasa.com/docs/rasa/2.x/reference/rasa/utils/tensorflow/model_data_utils/#featurize_training_examples "Direct link to heading")

```python
featurize_training_examples(training_examples: List[Message], attributes: List[Text], entity_tag_specs: Optional[List["EntityTagSpec"]]=None, featurizers: Optional[List[Text]]=None, bilou_tagging:bool=False)-> Tuple[List[Dict[Text, List["Features"]]], Dict[Text, Dict[Text, List[int]]]]
```

Converts training data into a list of attribute to features.

Possible attributes are, for example, INTENT, RESPONSE, TEXT, ACTION_TEXT,
ACTION_NAME or ENTITIES.
Also returns sparse feature sizes for each attribute. It could look like this:
{TEXT: {FEATURE_TYPE_SEQUENCE: [16, 32], FEATURE_TYPE_SENTENCE: [16, 32]}}.

**Arguments**:

- `training_examples` - the list of training examples
- `attributes` - the attributes to consider
- `entity_tag_specs` - the entity specs
- `featurizers` - the featurizers to consider
- `bilou_tagging` - indicates whether BILOU tagging should be used or not

**Returns**:

A list of attribute to features.
A dictionary of attribute to feature sizes.

#### get_tag_ids [#](https://legacy-docs-oss.rasa.com/docs/rasa/2.x/reference/rasa/utils/tensorflow/model_data_utils/#get_tag_ids "Direct link to heading")

```python
get_tag_ids(example: Message, tag_spec:"EntityTagSpec", bilou_tagging:bool)->"Features"
```

Creates a feature array containing the entity tag ids of the given example.

**Arguments**:

- `example` - the message
- `tag_spec` - entity tag spec
- `bilou_tagging` - indicates whether BILOU tagging should be used or not

**Returns**:

A list of features.

#### convert_to_data_format [#](https://legacy-docs-oss.rasa.com/docs/rasa/2.x/reference/rasa/utils/tensorflow/model_data_utils/#convert_to_data_format "Direct link to heading")

```python
convert_to_data_format(features: Union[
    List[List[Dict[Text, List["Features"]]], List[Dict[Text, List["Features"]]]], 
    fake_features: Optional[Dict[Text, List["Features"]]]=None, 
    consider_dialogue_dimension:bool=True, featurizers: Optional[List[Text]]=None)-> Tuple[Data, Optional[Dict[Text, List["Features"]]]]
```

Converts the input into "Data" format.

"features" can, for example, be a dictionary of attributes (INTENT,
TEXT, ACTION_NAME, ACTION_TEXT, ENTITIES, SLOTS, FORM) to a list of features for
all dialogue turns in all training trackers.
For NLU training it would just be a dictionary of attributes (either INTENT or
RESPONSE, TEXT, and potentially ENTITIES) to a list of features for all training
examples.

The "Data" format corresponds to Dict[Text, Dict[Text, List[FeatureArray]]]. It's
a dictionary of attributes (e.g. TEXT) to a dictionary of secondary attributes
(e.g. SEQUENCE or SENTENCE) to the list of actual features.

**Arguments**:

- `features` - a dictionary of attributes to a list of features for all examples in the training data
- `fake_features` - Contains default feature values for attributes
- `consider_dialogue_dimension` - If set to false the dialogue dimension will be removed from the resulting sequence features.
- `featurizers` - the featurizers to consider

**Returns**:

Input in "Data" format and fake features.