## DIETClassifier Objects

### class DIETClassifier(IntentClassifier, EntityExtractor)

A multi-task model for intent classification and entity extraction.

DIET is Dual Intent and Entity Transformer. The architecture is based on a transformer which is shared for both tasks. A sequence of entity labels is predicted through a Conditional Random Field (CRF) tagging layer on top of the transformer output sequence corresponding to the input sequence of tokens. The transformer output for the `__CLS__` token and intent labels are embedded into a single semantic vector space. We use the dot-product loss to maximize the similarity with the target label and minimize similarities with negative samples.

#### __init__

```python
__init__(component_config: Optional[Dict[Text, Any]]=None, index_label_id_mapping: Optional[Dict[int, Text]]=None, entity_tag_specs: Optional[List[EntityTagSpec]]=None, model: Optional[RasaModel]=None, finetune_mode:bool=False, sparse_feature_sizes: Optional[Dict[Text, Dict[Text, List[int]]]=None)->None
```

Declare instance variables with default values.

#### label_key

```python
@property
label_key()-> Optional[Text]
```

Return key if intent classification is activated.

#### label_sub_key

```python
@property
label_sub_key()-> Optional[Text]
```

Return sub key if intent classification is activated.

#### preprocess_train_data

```python
preprocess_train_data(training_data: TrainingData)-> RasaModelData
```

Prepares data for training. Performs sanity checks on training data, extracts encodings for labels.

#### train

```python
train(training_data: TrainingData, config: Optional[RasaNLUModelConfig]=None, **kwargs: Any)->None
```

Train the embedding intent classifier on a data set.

#### process

```python
process(message: Message, **kwargs: Any)->None
```

Augments the message with intents, entities, and diagnostic data.

#### persist

```python
persist(file_name: Text, model_dir: Text)-> Dict[Text, Any]
```

Persist this model into the passed directory. Return the metadata necessary to load the model again.

#### load

```python
@classmethod
load(cls, meta: Dict[Text, Any], model_dir: Text, model_metadata: Metadata =None, cached_component: Optional["DIETClassifier"] = None, should_finetune:bool=False, **kwargs: Any)->"DIETClassifier"
```

Loads the trained model from the provided directory.

## DIET Objects

### class DIET(TransformerRasaModel)

#### batch_loss

```python
batch_loss(batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]])-> tf.Tensor
```

Calculates the loss for the given batch.

**Arguments**:
- `batch_in` - The batch.

**Returns**:
The loss of the given batch.

#### prepare_for_predict

```python
prepare_for_predict()->None
```

Prepares the model for prediction.

#### batch_predict

```python
batch_predict(batch_in: Union[Tuple[tf.Tensor], Tuple[np.ndarray]])-> Dict[Text, tf.Tensor]
```

Predicts the output of the given batch.

**Arguments**:
- `batch_in` - The batch.

**Returns**:
The output to predict.