NLU Components

NLU-based assistants

This section refers to building NLU-based assistants. If you are working with Conversational AI with Language Models (CALM), this content may not apply to you.

Tokenizers

Tokenizers split text into tokens. If you want to split intents into multiple labels, e.g. for predicting multiple intents or for modeling hierarchical intent structure, use the following flags with any tokenizer:

intent_tokenization_flag indicates whether to tokenize intent labels or not. Set it to True, so that intent labels are tokenized.
intent_split_symbol sets the delimiter string to split the intent labels, default is underscore (_).

WhitespaceTokenizer

Short

Tokenizer using whitespaces as a separator
Outputs

tokens for user messages, responses (if present), and intents (if specified)
Requires

Nothing
Description

Creates a token for every whitespace separated character sequence.

Any character not in: a-zA-Z0-9_#@& will be substituted with whitespace before splitting on whitespace if the character fulfills any of the following conditions:
- the character follows a whitespace: " !word" → "word"
- the character precedes a whitespace: "word! " → "word"
- the character is at the beginning of the string: "!word" → "word"
- the character is at the end of the string: "word!" → "word"
Note that:
- "wo!rd" → "wo!rd"
In addition, any character not in: a-zA-Z0-9_#@&.~:\/?[]()!$*+,;=- will be substituted with whitespace before splitting on whitespace if the character is not between numbers:
- "twenty\{one" → "twenty", "one" ("{"` is not between numbers)
- "20\{1" → "20\{1" ("{"` is between numbers)
Note that:
- "name@example.com" → "name@example.com"
- "10,000.1" → "10,000.1"
- "1 - 2" → "1","2"

Configuration

config.yml
pipeline:
- name: "WhitespaceTokenizer"
  # Flag to check whether to split intents
  "intent_tokenization_flag": False
  # Symbol on which intent should be split
  "intent_split_symbol": "_"
  # Regular expression to detect tokens
  "token_pattern": None

Featurizers

Text featurizers are divided into two different categories: sparse featurizers and dense featurizers. Sparse featurizers are featurizers that return feature vectors with a lot of missing values, e.g. zeros. As those feature vectors would normally take up a lot of memory, we store them as sparse features. Sparse features only store the values that are non zero and their positions in the vector. Thus, we save a lot of memory and are able to train on larger datasets.

All featurizers can return two different kind of features: sequence features and sentence features. The sequence features are a matrix of size (number-of-tokens x feature-dimension). The matrix contains a feature vector for every token in the sequence. This allows us to train sequence models. The sentence features are represented by a matrix of size (1 x feature-dimension). It contains the feature vector for the complete utterance. The sentence features can be used in any bag-of-words model. The corresponding classifier can therefore decide what kind of features to use. Note: The feature-dimension for sequence and sentence features does not have to be the same.

LanguageModelFeaturizer

Short

Creates a vector representation of user message and response (if specified) using a pre-trained language model.
Outputs

dense_features for user messages and responses
Type

Dense featurizer
Description

Creates features for entity extraction, intent classification, and response selection. Uses a pre-trained language model to compute vector representations of input text.

note
Please make sure that you use a language model which is pre-trained on the same language corpus as that of your training data.

Configuration

Include a Tokenizer component before this component.

You should specify what language model to load via the parameter model_name. See the below table for the currently supported language models. The weights to be loaded can be specified by the additional parameter model_weights. If left empty, it uses the default model weights listed in the table.

+----------------+--------------+-------------------------+
| Language Model | Parameter    | Default value for       |
|                | "model_name" | "model_weights"         |
+----------------+--------------+-------------------------+
| BERT           | bert         | rasa/LaBSE              |
+----------------+--------------+-------------------------+
| GPT            | gpt          | openai-gpt              |
+----------------+--------------+-------------------------+
| GPT-2          | gpt2         | gpt2                    |
+----------------+--------------+-------------------------+
| XLNet          | xlnet        | xlnet-base-cased        |
+----------------+--------------+-------------------------+
| DistilBERT     | distilbert   | distilbert-base-uncased |
+----------------+--------------+-------------------------+
| RoBERTa        | roberta      | roberta-base            |
+----------------+--------------+-------------------------+
| camemBERT      | camembert    | camembert-base          |
+----------------+--------------+-------------------------+

Apart from the default pretrained model weights, further models can be used from HuggingFace models provided the following conditions are met (the mentioned files can be found in the "Files and versions" section of the model website):

The model architecture is one of the supported language models (check that the model_type in config.json is listed in the table's column model_name)
The model has pretrained Tensorflow weights (check that the file tf_model.h5 exists, at this time Safetensors are not supported.)
The model uses the default tokenizer (config.json should not contain a custom tokenizer_class setting)

note

While the LaBSE weights are loaded by default for the bert architecture offering a multi-lingual model trained on 112 languages (see our tutorial and the original paper), we now recommend using MiniLM model for better performance.

The LaBSE weights can still serve as a baseline for initial testing and development. After establishing this baseline, we strongly encourage exploring optimization with the MiniLM to improve your assistant effectiveness, before trying to optimize this component with other weights/architectures.

The following configuration loads the language model BERT with rasa/LaBSE weights, which can be found here:

config.yml
pipeline:
  - name: LanguageModelFeaturizer
    # Name of the language model to use
    model_name: "bert"
    # Pre-Trained weights to be loaded
    model_weights: "rasa/LaBSE"

    # An optional path to a directory from which
    # to load pre-trained model weights.
    # If the requested model is not found in the
    # directory, it will be downloaded and
    # cached in this directory for future use.
    # The default value of `cache_dir` can be
    # set using the environment variable
    # `TRANSFORMERS_CACHE`, as per the
    # Transformers library.
    cache_dir: null

For enhanced performance, we recommend the sentence-transformers/all-MiniLM-L6-v2 weights, which can be found here:

config.yml
pipeline:
- name: LanguageModelFeaturizer
  model_name: "bert"
  model_weights: "sentence-transformers/all-MiniLM-L6-v2"
  cache_dir: null

RegexFeaturizer

Short

Creates a vector representation of user message using regular expressions.
Outputs

sparse_features for user messages and tokens.pattern
Requires

tokens
Type

Sparse featurizer
Description

Creates features for entity extraction and intent classification. During training the RegexFeaturizer creates a list of regular expressions defined in the training data format. For each regex, a feature will be set marking whether this expression was found in the user message or not. All features will later be fed into an intent classifier / entity extractor to simplify classification (assuming the classifier has learned during the training phase, that this set feature indicates a certain intent / entity). Regex features for entity extraction are currently only supported by the CRFEntityExtractor.
Configuration

Make the featurizer case insensitive by adding the case_sensitive: False option, the default being case_sensitive: True.

To correctly process languages such as Chinese that don't use whitespace for word separation, the user needs to add the use_word_boundaries: False option, the default being use_word_boundaries: True.
config.yml
```
pipeline:
- name: "RegexFeaturizer"
  # Text will be processed with case sensitive as default
  "case_sensitive": True
  # use match word boundaries for lookup table
  "use_word_boundaries": True
```

CountVectorsFeaturizer

Short

Creates bag-of-words representation of user messages, intents, and responses.
Outputs

sparse_features for user messages, intents, and responses
Requires

tokens
Type

Sparse featurizer
Description

Creates features for intent classification and response selection. Creates bag-of-words representation of user message, intent, and response using sklearn's CountVectorizer. All tokens which consist only of digits (e.g. 123 and 99 but not a123d) will be assigned to the same feature.
Configuration

See sklearn's CountVectorizer docs for detailed description of the configuration parameters.

This featurizer can be configured to use word or character n-grams, using the analyzer configuration parameter. By default analyzer is set to word so word token counts are used as features. If you want to use character n-grams, set analyzer to char or char_wb. The lower and upper boundaries of the n-grams can be configured via the parameters min_ngram and max_ngram. By default both of them are set to 1. By default the featurizer takes the lemma of a word instead of the word directly if it is available. You can disable this behavior by setting use_lemma to False.

note
Option char_wb creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space. This option can be used to create Subword Semantic Hashing.

note
For character n-grams do not forget to increase min_ngram and max_ngram parameters. Otherwise the vocabulary will contain only single letters.

Handling Out-Of-Vocabulary (OOV) words:

note
Enabled only if analyzer is word.

Since the training is performed on limited vocabulary data, it cannot be guaranteed that during prediction an algorithm will not encounter an unknown word (a word that were not seen during training). In order to teach an algorithm how to treat unknown words, some words in training data can be substituted by generic word OOV_token. In this case during prediction all unknown words will be treated as this generic word OOV_token.

For example, one might create separate intent outofscope in the training data containing messages of different number of OOV_token s and maybe some additional general words. Then an algorithm will likely classify a message with unknown words as this intent outofscope.

You can either set the OOV_token or a list of words OOV_words:
- OOV_token set a keyword for unseen words; if training data contains OOV_token as words in some messages, during prediction the words that were not seen during training will be substituted with provided OOV_token; if OOV_token=None (default behavior) words that were not seen during training will be ignored during prediction time;
- OOV_words set a list of words to be treated as OOV_token during training; if a list of words that should be treated as Out-Of-Vocabulary is known, it can be set to OOV_words instead of manually changing it in training data or using custom preprocessor.
note
This featurizer creates a bag-of-words representation by counting words, so the number of OOV_token in the sentence might be important.

note
Providing OOV_words is optional, training data can contain OOV_token input manually or by custom additional preprocessor. Unseen words will be substituted with OOV_token only if this token is present in the training data or OOV_words list is provided.

If you want to share the vocabulary between user messages and intents, you need to set the option use_shared_vocab to True. In that case a common vocabulary set between tokens in intents and user messages is build.
config.yml
```
pipeline:
- name: "CountVectorsFeaturizer"
  # Analyzer to use, either 'word', 'char', or 'char_wb'
  "analyzer": "word"
  # Set the lower and upper boundaries for the n-grams
  "min_ngram": 1
  "max_ngram": 1
  # Set the out-of-vocabulary token
  "OOV_token": "_oov_"
  # Whether to use a shared vocab
  "use_shared_vocab": False
```
Configuring for incremental training

To ensure that sparse_features are of fixed size during incremental training, the component should be configured to account for additional vocabulary tokens that may be added as part of new training examples in the future. To do so, configure the additional_vocabulary_size parameter while training the base model from scratch:
config.yml
```
pipeline:
- name: CountVectorsFeaturizer
  additional_vocabulary_size:
    text: 1000
    response: 1000
    action_text: 1000
```
As in the above example, you can define additional vocabulary size for each of text (user messages), response (bot responses used by ResponseSelector) and action_text (bot responses not used by ResponseSelector). If you are building a shared vocabulary (use_shared_vocab=True), you only need to define a value for the text attribute. If any of the attribute is not configured by the user, the component takes half of the current vocabulary size as the default value for the attribute's additional_vocabulary_size. This number is kept at a minimum of 1000 in order to avoid running out of additional vocabulary slots too frequently during incremental training. Once the component runs out of additional vocabulary slots, the new vocabulary tokens are dropped and not considered during featurization. At this point, it is advisable to retrain a new model from scratch.

The above configuration parameters are the ones you should configure to fit your model to your data. However, additional parameters exist that can be adapted.

More configurable parameters

+---------------------------+-------------------------+--------------------------------------------------------------+
| Parameter                 | Default Value           | Description                                                  |
+===========================+=========================+==============================================================+
| use_shared_vocab          | False                   | If set to 'True' a common vocabulary is used for labels      |
|                           |                         | and user message.                                            |
+---------------------------+-------------------------+--------------------------------------------------------------+
| analyzer                  | word                    | Whether the features should be made of word n-gram or        |
|                           |                         | character n-grams. Option 'char_wb' creates character        |
|                           |                         | n-grams only from text inside word boundaries;               |
|                           |                         | n-grams at the edges of words are padded with space.         |
|                           |                         | Valid values: 'word', 'char', 'char_wb'.                     |
+---------------------------+-------------------------+--------------------------------------------------------------+
| strip_accents             | None                    | Remove accents during the pre-processing step.               |
|                           |                         | Valid values: 'ascii', 'unicode', 'None'.                    |
+---------------------------+-------------------------+--------------------------------------------------------------+
| stop_words                | None                    | A list of stop words to use.                                 |
|                           |                         | Valid values: 'english' (uses an internal list of            |
|                           |                         | English stop words), a list of custom stop words, or         |
|                           |                         | 'None'.                                                      |
+---------------------------+-------------------------+--------------------------------------------------------------+
| min_df                    | 1                       | When building the vocabulary ignore terms that have a        |
|                           |                         | document frequency strictly lower than the given threshold.  |
+---------------------------+-------------------------+--------------------------------------------------------------+
| max_df                    | 1                       | When building the vocabulary ignore terms that have a        |
|                           |                         | document frequency strictly higher than the given threshold  |
|                           |                         | (corpus-specific stop words).                                |
+---------------------------+-------------------------+--------------------------------------------------------------+
| min_ngram                 | 1                       | The lower boundary of the range of n-values for different    |
|                           |                         | word n-grams or char n-grams to be extracted.                |
+---------------------------+-------------------------+--------------------------------------------------------------+
| max_ngram                 | 1                       | The upper boundary of the range of n-values for different    |
|                           |                         | word n-grams or char n-grams to be extracted.                |
+---------------------------+-------------------------+--------------------------------------------------------------+
| max_features              | None                    | If not 'None', build a vocabulary that only consider the top |
|                           |                         | max_features ordered by term frequency across the corpus.    |
+---------------------------+-------------------------+--------------------------------------------------------------+
| lowercase                 | True                    | Convert all characters to lowercase before tokenizing.       |
+---------------------------+-------------------------+--------------------------------------------------------------+
| OOV_token                 | None                    | Keyword for unseen words.                                    |
+---------------------------+-------------------------+--------------------------------------------------------------+
| OOV_words                 | []                      | List of words to be treated as 'OOV_token' during training.  |
+---------------------------+-------------------------+--------------------------------------------------------------+
| alias                     | CountVectorFeaturizer   | Alias name of featurizer.                                    |
+---------------------------+-------------------------+--------------------------------------------------------------+
| use_lemma                 | True                    | Use the lemma of words for featurization.                    |
+---------------------------+-------------------------+--------------------------------------------------------------+
| additional_vocabulary_size| text: 1000              | Size of additional vocabulary to account for incremental     |
|                           | response: 1000          | training while training a model from scratch                 |
|                           | action_text: 1000       |                                                              |
+---------------------------+-------------------------+--------------------------------------------------------------+

LexicalSyntacticFeaturizer

Short

Creates lexical and syntactic features for a user message to support entity extraction.
Outputs

sparse_features for user messages
Requires

tokens
Type

Sparse featurizer
Description

Creates features for entity extraction. Moves with a sliding window over every token in the user message and creates features according to the configuration (see below). As a default configuration is present, you don't need to specify a configuration.

Configuration

You can configure what kind of lexical and syntactic features the featurizer should extract. The following features are available:

==============  ==========================================================================================
Feature Name    Description
==============  ==========================================================================================
BOS             Checks if the token is at the beginning of the sentence.
EOS             Checks if the token is at the end of the sentence.
low             Checks if the token is lower case.
upper           Checks if the token is upper case.
title           Checks if the token starts with an uppercase character and all remaining characters are
                lowercased.
digit           Checks if the token contains just digits.
prefix5         Take the first five characters of the token.
prefix2         Take the first two characters of the token.
suffix5         Take the last five characters of the token.
suffix3         Take the last three characters of the token.
suffix2         Take the last two characters of the token.
suffix1         Take the last character of the token.
==============  ==========================================================================================

As the featurizer is moving over the tokens in a user message with a sliding window, you can define features for previous tokens, the current token, and the next tokens in the sliding window. You define the features as a [before, token, after] array. If you want to define features for the token before, the current token, and the token after, your features configuration would look like this:

config.yml
pipeline:
- name: LexicalSyntacticFeaturizer
  "features": [
    ["low", "title", "upper"],
    ["BOS", "EOS", "low", "upper", "title", "digit"],
    ["low", "title", "upper"],
  ]

This configuration is also the default configuration.

Intent Classifiers

Intent classifiers assign one of the intents defined in the domain file to incoming user messages.

LogisticRegressionClassifier

Short

Logistic regression intent classifier, using the scikit-learn implementation.
Outputs

intent and intent_ranking
Requires

Either sparse_features or dense_features need to be present.
Output-Example

{
  "intent": { "name": "greet", "confidence": 0.78 },
  "intent_ranking": [
    {
      "confidence": 0.78,
      "name": "greet"
    },
    {
      "confidence": 0.14,
      "name": "goodbye"
    },
    {
      "confidence": 0.08,
      "name": "restaurant_search"
    }
  ]
}

Description

This classifier uses scikit-learn's logistic regression implementation to perform intent classification. It's able to use only sparse features, but will also pick up any dense features that are present. In general, DIET should yield higher accuracy results, but this classifier should train faster and may be used as a lightweight benchmark. Our implementation uses the base settings from scikit-learn, with the exception of the class_weight parameter where we assume the "balanced" setting.
Configuration

An example configuration with all the defaults can be found below.

pipeline:
  - name: LogisticRegressionClassifier
    max_iter: 100
    solver: lbfgs
    tol: 0.0001
    random_state: 42
    ranking_length: 10

There configuration parameters are briefly explained below.

max_iter: Maximum number of iterations taken for the solvers to converge.
solver: Solver to be used. For very small datasets you might consider liblinear.
tol: Tolerance for stopping criteria of the optimizer.
random_state: Used to shuffle the data before training.
ranking_length: Number of top intents to report. Set to 0 to report all intents

More details on the parameters can be found on the scikit-learn documentation page.

SklearnIntentClassifier

Short

Sklearn intent classifier
Outputs

intent and intent_ranking
Requires

dense_features for user messages

Output-Example

{
  "intent": { "name": "greet", "confidence": 0.78 },
  "intent_ranking": [
    {
      "confidence": 0.78,
      "name": "greet"
    },
    {
      "confidence": 0.14,
      "name": "goodbye"
    },
    {
      "confidence": 0.08,
      "name": "restaurant_search"
    }
  ]
}

Description

The sklearn intent classifier trains a linear SVM which gets optimized using a grid search. It also provides rankings of the labels that did not “win”. The SklearnIntentClassifier needs to be preceded by a dense featurizer in the pipeline. This dense featurizer creates the features used for the classification. For more information about the algorithm itself, take a look at the GridSearchCV documentation.

Configuration

During the training of the SVM a hyperparameter search is run to find the best parameter set. In the configuration you can specify the parameters that will get tried.

config.yml
pipeline:
- name: "SklearnIntentClassifier"
  # Specifies the list of regularization values to
  # cross-validate over for C-SVM.
  # This is used with the ``kernel`` hyperparameter in GridSearchCV.
  C: [1, 2, 5, 10, 20, 100]
  # Specifies the kernel to use with C-SVM.
  # This is used with the ``C`` hyperparameter in GridSearchCV.
  kernels: ["linear"]
  # Gamma parameter of the C-SVM.
  "gamma": [0.1]
  # We try to find a good number of cross folds to use during
  # intent training, this specifies the max number of folds.
  "max_cross_validation_folds": 5
  # Scoring function used for evaluating the hyper parameters.
  # This can be a name or a function.
  "scoring_function": "f1_weighted"

KeywordIntentClassifier

Short

Simple keyword matching intent classifier, intended for small, short-term projects.
Output s

intent
Requires

Nothing

Output-Example

{
  "intent": { "name": "greet", "confidence": 1.0 }
}

Description

This classifier works by searching a message for keywords. The matching is case sensitive by default and searches only for exact matches of the keyword-string in the user message. The keywords for an intent are the examples of that intent in the NLU training data. This means the entire example is the keyword, not the individual words in the example.

Configuration

config.yml
pipeline:
- name: "KeywordIntentClassifier"
  case_sensitive: True

Entity Extractors

Entity extractors extract entities, such as person names or locations, from the user message.

note

If you use multiple entity extractors, we advise that each extractor targets an exclusive set of entity types. For example, use Duckling to extract dates and times, and CRFEntityExtractor to extract person names. Otherwise, if multiple extractors target the same entity types, it is very likely that entities will be extracted multiple times.

For example, if you use two or more general purpose extractors like CRFEntityExtractor, the entity types in your training data will be found and extracted by all of them. If the slots you are filling with your entity types are of type text, then the last extractor in your pipeline will win. If the slot is of type list, then all results will be added to the list, including duplicates.

Another, less obvious case of duplicate/overlapping extraction can happen even if extractors focus on different entity types. Imagine a food delivery bot and a user message like I would like to order the Monday special. Hypothetically, if your time extractor's performance isn't very good, it might extract Monday here as a time for the order, and your other extractor might extract Monday special as the meal.

CRFEntityExtractor

Short

Conditional random field (CRF) entity extraction
Outputs

entities
Requires

tokens and dense_features (optional)

Output-Example

{
  "entities": [
    {
      "value": "New York City",
      "start": 20,
      "end": 33,
      "entity": "city",
      "confidence": 0.874,
      "extractor": "CRFEntityExtractor"
    }
  ]
}

Description

This component implements a conditional random fields (CRF) to do named entity recognition. CRFs can be thought of as an undirected Markov chain where the time steps are words and the states are entity classes. Features of the words (capitalization, POS tagging, etc.) give probabilities to certain entity classes, as are transitions between neighbouring entity tags: the most likely set of tags is then calculated and returned.

If you want to pass custom features, such as pre-trained word embeddings, to CRFEntityExtractor, you can add any dense featurizer to the pipeline before the CRFEntityExtractor and subsequently configure CRFEntityExtractor to make use of the dense features by adding "text_dense_feature" to its feature configuration. CRFEntityExtractor automatically finds the additional dense features and checks if the dense features are an iterable of len(tokens), where each entry is a vector. A warning will be shown in case the check fails. However, CRFEntityExtractor will continue to train just without the additional custom features. In case dense features are present, CRFEntityExtractor will pass the dense features to sklearn_crfsuite and use them for training.

Configuration

CRFEntityExtractor has a list of default features to use. However, you can overwrite the default configuration. The following features are available:

===================  ==========================================================================================
Feature Name         Description
===================  ==========================================================================================
low                  word identity - use the lower-cased token as a feature.
upper                Checks if the token is upper case.
title                Checks if the token starts with an uppercase character and all remaining characters are
                     lowercased.
digit                Checks if the token contains just digits.
prefix5              Take the first five characters of the token.
prefix2              Take the first two characters of the token.
suffix5              Take the last five characters of the token.
suffix3              Take the last three characters of the token.
suffix2              Take the last two characters of the token.
suffix1              Take the last character of the token.
pattern              Take the patterns defined by ``RegexFeaturizer``.
bias                 Add an additional "bias" feature to the list of features.
text_dense_features  Adds additional features from a dense featurizer.
===================  ==========================================================================================

Additional you can set a flag to determine whether to use the BILOU tagging schema or not.

BILOU_flag determines whether to use BILOU tagging or not. Default True.

config.yml
pipeline:
- name: "CRFEntityExtractor"
  # BILOU_flag determines whether to use BILOU tagging or not.
  "BILOU_flag": True
  # features to extract in the sliding window
  "features": [
    ["low", "title", "upper"],
    [
      "bias",
      "low",
      "prefix5",
      "prefix2",
      "suffix5",
      "suffix3",
      "suffix2",
      "upper",
      "title",
      "digit",
      "pattern",
      "text_dense_features"
    ],
    ["low", "title", "upper"],
  ]
  # The maximum number of iterations for optimization algorithms.
  "max_iterations": 50
  # weight of the L1 regularization
  "L1_c": 0.1
  # weight of the L2 regularization
  "L2_c": 0.1
  # Name of dense featurizers to use.
  # If list is empty all available dense features are used.
  "featurizers": []
  # Indicated whether a list of extracted entities should be split into individual entities for a given entity type
  "split_entities_by_comma":
      address: False
      email: True

note

If pattern features are used, you need to have RegexFeaturizer in your pipeline.

note

If text_dense_features features are used, you need to have a dense featurizer (e.g. LanguageModelFeaturizer) in your pipeline.

DucklingEntityExtractor

Short

Duckling lets you extract common entities like dates, amounts of money, distances, and others in a number of languages.
Outputs

entities
Requires

Nothing

Output-Example

{
  "entities": [
    {
      "end": 53,
      "entity": "time",
      "start": 48,
      "value": "2017-04-10T00:00:00.000+02:00",
      "confidence": 1.0,
      "extractor": "DucklingEntityExtractor"
    }
  ]
}

Description

To use this component you need to run a duckling server. The easiest option is to spin up a docker container using docker run -p 8000:8000 rasa/duckling.

Alternatively, you can install duckling directly on your machine and start the server.

Duckling allows to recognize dates, numbers, distances and other structured entities and normalizes them. Please be aware that duckling tries to extract as many entity types as possible without providing a ranking. For example, if you specify both number and time as dimensions for the duckling component, the component will extract two entities: 10 as a number and in 10 minutes as a time from the text I will be there in 10 minutes. In such a situation, your application would have to decide which entity type is be the correct one. The extractor will always return 1.0 as a confidence, as it is a rule based system.

The list of supported languages can be found in the Duckling GitHub repository.

Configuration

Configure which dimensions, i.e. entity types, the duckling component should extract. A full list of available dimensions can be found in the duckling project readme. Leaving the dimensions option unspecified will extract all available dimensions.

config.yml
pipeline:
- name: "DucklingEntityExtractor"
  # url of the running duckling server
  url: "http://localhost:8000"
  # dimensions to extract
  dimensions: ["time", "number", "amount-of-money", "distance"]
  # allows you to configure the locale, by default the language is
  # used
  locale: "de_DE"
  # if not set the default timezone of Duckling is going to be used
  # needed to calculate dates from relative expressions like "tomorrow"
  timezone: "Europe/Berlin"
  # Timeout for receiving response from http url of the running duckling server
  # if not set the default timeout of duckling http url is set to 3 seconds.
  timeout : 3

RegexEntityExtractor

Short

Extracts entities using the lookup tables and/or regexes defined in the training data
Outputs

entities
Requires

Nothing
Description

This component extract entities using the lookup tables and regexes defined in the training data. The component checks if the user message contains an entry of one of the lookup tables or matches one of the regexes. If a match is found, the value is extracted as entity.

This component only uses those regex features that have a name equal to one of the entities defined in the training data. Make sure to annotate at least one example per entity.

note
When you use this extractor in combination with CRFEntityExtractor, it can lead to multiple extraction of entities. Especially if many training sentences have entity annotations for the entity types for which you also have defined regexes. See the big info box at the start of the entity extractor section for more info on multiple extraction.
In the case where you seem to need both this RegexEntityExtractor and another of the aforementioned statistical extractors, we advise you to consider one of the following two options.
Option 1 is advisable when you have exclusive entity types for each type of extractor. To make the sure the extractors don't interfere with one another annotate only one example sentence for each regex/lookup entity type, but not more.
Option 2 is useful when you want to use regexes matches as additional signal for your statistical extractor, but you don't have separate entity types. In this case you will want to 1) add the RegexFeaturizer before the extractors in your pipeline 2) annotate all your entity examples in the training data and 3) remove the RegexEntityExtractor from your pipeline. This way, your statistical extractors will receive additional signal about the presence of regex matches and will be able to statistically determine when to rely on these matches and when not to.

Configuration

Make the entity extractor case sensitive by adding the case_sensitive: True option, the default being case_sensitive: False.

To correctly process languages such as Chinese that don't use whitespace for word separation, the user needs to add the use_word_boundaries: False option, the default being use_word_boundaries: True.

config.yml
    pipeline:
    - name: RegexEntityExtractor
      # text will be processed with case insensitive as default
      case_sensitive: False
      # use lookup tables to extract entities
      use_lookup_tables: True
      # use regexes to extract entities
      use_regexes: True
      # use match word boundaries for lookup table
      use_word_boundaries: True

EntitySynonymMapper

Short

Maps synonymous entity values to the same value.
Outputs

Modifies existing entities that previous entity extraction components found.
Requires

An extractor from Entity Extractors

Description

If the training data contains defined synonyms, this component will make sure that detected entity values will be mapped to the same value. For example, if your training data contains the following examples:

[
  {
    "text": "I moved to New York City",
    "intent": "inform_relocation",
    "entities": [
      {
        "value": "nyc",
        "start": 11,
        "end": 24,
        "entity": "city"
      }
    ]
  },
  {
    "text": "I got a new flat in NYC.",
    "intent": "inform_relocation",
    "entities": [
      {
        "value": "nyc",
        "start": 20,
        "end": 23,
        "entity": "city"
      }
    ]
  }
]

This component will allow you to map the entities New York City and NYC to nyc. The entity extraction will return nyc even though the message contains NYC. When this component changes an existing entity, it appends itself to the processor list of this entity.

Configuration
config.yml
```
pipeline:
- name: "EntitySynonymMapper"
```
note
When using the EntitySynonymMapper as part of an NLU pipeline, it will need to be placed below any entity extractors in the configuration file.

Incremental training

New in 2.2

This feature is experimental. We introduce experimental features to get feedback from our community, so we encourage you to try it out! However, the functionality might be changed or removed in the future. If you have feedback (positive or negative) please share it with us on the Rasa Forum.

In order to improve the performance of an assistant, it's helpful to practice CDD and add new training examples based on how your users have talked to your assistant. You can use rasa train --finetune to initialize the pipeline with an already trained model and further finetune it on the new training dataset that includes the additional training examples. This will help reduce the training time of the new model.

By default, the command picks up the latest model in the models/ directory. If you have a specific model which you want to improve, you may specify the path to this by running rasa train --finetune <path to model to finetune>. Finetuning a model usually requires fewer epochs to train machine learning components like DIETClassifier, ResponseSelector and TEDPolicy compared to training from scratch. Either use a model configuration for finetuning which defines fewer epochs than before or use the flag --epoch-fraction. --epoch-fraction will use a fraction of the epochs specified for each machine learning component in the model configuration file. For example, if DIETClassifier is configured to use 100 epochs, specifying --epoch-fraction 0.5 will only use 50 epochs for finetuning.

You can also finetune an NLU-only or dialogue management-only model by using rasa train nlu --finetune and rasa train core --finetune respectively.

To be able to fine tune a model, the following conditions must be met:

The configuration supplied should be exactly the same as the configuration used to train the model which is being finetuned. The only parameter that you can change is epochs for the individual machine learning components and policies.
The set of labels(intents, actions, entities and slots) for which the base model is trained should be exactly the same as the ones present in the training data used for finetuning. This means that you cannot add new intent, action, entity or slot labels to your training data during incremental training. You can still add new training examples for each of the existing labels. If you have added/removed labels in the training data, the pipeline needs to be trained from scratch.
The model to be finetuned is trained with MINIMUM_COMPATIBLE_VERSION of the currently installed rasa version.

NLU-based assistants

Tokenizers​

WhitespaceTokenizer​

Featurizers​

LanguageModelFeaturizer​

RegexFeaturizer​

CountVectorsFeaturizer​

LexicalSyntacticFeaturizer​

Intent Classifiers​

LogisticRegressionClassifier​

SklearnIntentClassifier​

KeywordIntentClassifier​

Entity Extractors​

CRFEntityExtractor​

DucklingEntityExtractor​

RegexEntityExtractor​

EntitySynonymMapper​

Incremental training​

Tokenizers

WhitespaceTokenizer

Featurizers

LanguageModelFeaturizer

RegexFeaturizer

CountVectorsFeaturizer

LexicalSyntacticFeaturizer

Intent Classifiers

LogisticRegressionClassifier

SklearnIntentClassifier

KeywordIntentClassifier

Entity Extractors

CRFEntityExtractor

DucklingEntityExtractor

RegexEntityExtractor

EntitySynonymMapper

Incremental training