def inner(config: Dict[Text, Any],
load: bool = False,
finetune: bool = False) -> DIETClassifier:
if load:
constructor = DIETClassifier.load
else:
constructor = DIETClassifier.create
default_execution_context.is_finetuning = finetune
return constructor(
config=rasa.utils.common.override_defaults(
DIETClassifier.get_default_config(), config),
model_storage=default_model_storage,
execution_context=default_execution_context,
resource=default_diet_resource,
)
def inner(config: Dict[Text, Any],
load: bool = False,
finetune: bool = False) -> DIETClassifier:
if load:
constructor = DIETClassifier.load
else:
constructor = DIETClassifier.create
default_execution_context.is_finetuning = finetune
return constructor(
config={
**DIETClassifier.get_default_config(),
**config
},
model_storage=default_model_storage,
execution_context=default_execution_context,
resource=default_diet_resource,
)
def get_default_config() -> Dict[Text, Any]:
"""The component's default config (see parent class for full docstring)."""
return {
**DIETClassifier.get_default_config(),
# ## Architecture of the used neural network
# Hidden layer sizes for layers before the embedding layers for user message
# and labels.
# The number of hidden layers is equal to the length of the corresponding
# list.
HIDDEN_LAYERS_SIZES: {
TEXT: [256, 128],
LABEL: [256, 128]
},
# Whether to share the hidden layer weights between input words
# and responses
SHARE_HIDDEN_LAYERS: False,
# Number of units in transformer
TRANSFORMER_SIZE: None,
# Number of transformer layers
NUM_TRANSFORMER_LAYERS: 0,
# Number of attention heads in transformer
NUM_HEADS: 4,
# If 'True' use key relative embeddings in attention
KEY_RELATIVE_ATTENTION: False,
# If 'True' use key relative embeddings in attention
VALUE_RELATIVE_ATTENTION: False,
# Max position for relative embeddings. Only in effect if key-
# or value relative attention are turned on
MAX_RELATIVE_POSITION: 5,
# Use a unidirectional or bidirectional encoder.
UNIDIRECTIONAL_ENCODER: False,
# ## Training parameters
# Initial and final batch sizes:
# Batch size will be linearly increased for each epoch.
BATCH_SIZES: [64, 256],
# Strategy used when creating batches.
# Can be either 'sequence' or 'balanced'.
BATCH_STRATEGY: BALANCED,
# Number of epochs to train
EPOCHS: 300,
# Set random seed to any 'int' to get reproducible results
RANDOM_SEED: None,
# Initial learning rate for the optimizer
LEARNING_RATE: 0.001,
# ## Parameters for embeddings
# Dimension size of embedding vectors
EMBEDDING_DIMENSION: 20,
# Default dense dimension to use if no dense features are present.
DENSE_DIMENSION: {
TEXT: 512,
LABEL: 512
},
# Default dimension to use for concatenating sequence and sentence features.
CONCAT_DIMENSION: {
TEXT: 512,
LABEL: 512
},
# The number of incorrect labels. The algorithm will minimize
# their similarity to the user input during training.
NUM_NEG: 20,
# Type of similarity measure to use, either 'auto' or 'cosine' or 'inner'.
SIMILARITY_TYPE: AUTO,
# The type of the loss function, either 'cross_entropy' or 'margin'.
LOSS_TYPE: CROSS_ENTROPY,
# Number of top actions for which confidences should be predicted.
# Set to 0 if confidences for all intents should be reported.
RANKING_LENGTH: 10,
# Determines whether the confidences of the chosen top actions should be
# renormalized so that they sum up to 1. By default, we do not renormalize
# and return the confidences for the top actions as is.
# Note that renormalization only makes sense if confidences are generated
# via `softmax`.
RENORMALIZE_CONFIDENCES: False,
# Indicates how similar the algorithm should try to make embedding vectors
# for correct labels.
# Should be 0.0 < ... < 1.0 for 'cosine' similarity type.
MAX_POS_SIM: 0.8,
# Maximum negative similarity for incorrect labels.
# Should be -1.0 < ... < 1.0 for 'cosine' similarity type.
MAX_NEG_SIM: -0.4,
# If 'True' the algorithm only minimizes maximum similarity over
# incorrect intent labels, used only if 'loss_type' is set to 'margin'.
USE_MAX_NEG_SIM: True,
# Scale loss inverse proportionally to confidence of correct prediction
SCALE_LOSS: True,
# ## Regularization parameters
# The scale of regularization
REGULARIZATION_CONSTANT: 0.002,
# Fraction of trainable weights in internal layers.
CONNECTION_DENSITY: 1.0,
# The scale of how important is to minimize the maximum similarity
# between embeddings of different labels.
NEGATIVE_MARGIN_SCALE: 0.8,
# Dropout rate for encoder
DROP_RATE: 0.2,
# Dropout rate for attention
DROP_RATE_ATTENTION: 0,
# If 'True' apply dropout to sparse input tensors
SPARSE_INPUT_DROPOUT: False,
# If 'True' apply dropout to dense input tensors
DENSE_INPUT_DROPOUT: False,
# ## Evaluation parameters
# How often calculate validation accuracy.
# Small values may hurt performance, e.g. model accuracy.
EVAL_NUM_EPOCHS: 20,
# How many examples to use for hold out validation set
# Large values may hurt performance, e.g. model accuracy.
EVAL_NUM_EXAMPLES: 0,
# ## Selector config
# If 'True' random tokens of the input message will be masked and the model
# should predict those tokens.
MASKED_LM: False,
# Name of the intent for which this response selector is to be trained
RETRIEVAL_INTENT: None,
# Boolean flag to check if actual text of the response
# should be used as ground truth label for training the model.
USE_TEXT_AS_LABEL: False,
# If you want to use tensorboard to visualize training
# and validation metrics,
# set this option to a valid output directory.
TENSORBOARD_LOG_DIR: None,
# Define when training metrics for tensorboard should be logged.
# Either after every epoch or for every training step.
# Valid values: 'epoch' and 'batch'
TENSORBOARD_LOG_LEVEL: "epoch",
# Specify what features to use as sequence and sentence features
# By default all features in the pipeline are used.
FEATURIZERS: [],
# Perform model checkpointing
CHECKPOINT_MODEL: False,
# if 'True' applies sigmoid on all similarity terms and adds it
# to the loss function to ensure that similarity values are
# approximately bounded. Used inside cross-entropy loss only.
CONSTRAIN_SIMILARITIES: False,
# Model confidence to be returned during inference. Currently, the only
# possible value is `softmax`.
MODEL_CONFIDENCE: SOFTMAX,
}