chatbot How to solve error in domain yml while training rasa

Table 3 depicts the number of values found in the RDF file which relates to one of the six entity types. The second column in the table indicates how many empty slots in the utterances exist, which need to be filled in order to create the final dataset. If there were more empty slots than unique entity values, some values were used more then once which were selected randomly. If there were more unique entity values than empty slots, some utterances were used more than once. In that case, we randomly selected a matching number of utterances from the list of utterances that only have an empty slot of that specific type. Those were then used to fill in the remaining utterances to finish the replacement process.

• Using lots of checkpoints can quickly make your
  stories hard to understand.
• For more information on the additional parameters, see Model Storage.
• In addition, we derived the types of entity values that are required to perform the succeeding processing step, such as making a database inquiry (not realized in this work).
• This behavior will work fine when defined as a story, but even better when defined
  as a rule.

Just as we have intents to abstract out what the user is trying to say, we have responses to represent what the bot would say. As you can probably guess, this is more of an iterative process, where we evaluate our bot’s performance in the real world and use that to improve its performance. Rasa is an open-source framework to build text and voice-based chatbots. It’s working at Level 3 of conversational AI, where the bot can understand the context.

Predictive Modeling w/ Python

This page describes the different types of training data that go into a Rasa assistant and how this training data is structured. Crowd-sourced training data for the development and testing of Rasa NLU models. Regex features for entity extraction
are currently only supported by the CRFEntityExtractor and DIETClassifier components. Other entity extractors, like
MitieEntityExtractor or SpacyEntityExtractor, won’t use the generated
features and their presence will not improve entity recognition for
these extractors. While running rasa train , I get an error which automatially takes an empty domain.yml instead of the .yml file that I want to choose .

In the last process step the empty slots in the utterances from step 4 are replaced using one of the lists created in step 5. At last information about the two sets of labels are added to each utterance. This includes the intent label, the entity type, the entity value and the position at which the entity values can be found in the utterance. Entities are structured pieces of information inside a user message.

More from Ng Wai Foong and Towards Data Science

Slots save values to your assistant’s memory, and entities are automatically saved to slots that have the same name. So if we had an entity called status, with two possible values ( new or returning), we could save that entity to a slot that is also called status. If you have custom validation actions extending FormValidationAction which override required_slots method, you should
double-check the dynamic form behavior of your migrated assistant. nlu training data Slots set by the default action
action_extract_slots may need to be reset within the context of your
form by the custom validation actions for the form’s required slots. 3 we aim to determine which design concept is best for training a domain-specific NLU. Based on the design specification of the concepts it can be assumed that if a dataset is created that contains all available entity values the results are likely to be highest.

As shown in the above examples, the user and examples keys are followed by |
(pipe) symbol. In YAML | identifies multi-line strings with preserved indentation. This helps to keep special symbols like “, ‘ and others still available in the
training examples.

Unfeaturized Slots#

The behavior and the performance of the classifier depend largely on the NLU pipeline. So, you have to curate your NLU pipeline according to your training data. For example, if your dataset has fewer training examples then you might have to use pre-trained tokenizers like SpacyTokenizer, ConveRTTokenizer. The components like RegexFeaturizer can be used to extract certain regex patterns and lookup table values. Similarly DucklingHTTPExtractor can be used to extract entities other than the entities you have marked in your dataset, it can be used for entities like dates, amounts of money, distance, etc. Other training parameters like epochs, the number of transformer layers, turning on/off entity recognition, embedding dimension, etc. can be configured under the component DIETClassifier.

Directory train_test_split will contain all yaml files processed with prefixes train_ or test_ containing
train and test parts. Rasa train will store the trained model in the directory defined by –out, models/ by default. If you want to name your model differently,
you can specify the name using the –fixed-model-name flag. The version key refers to the format of training data that rasa supports. This sounds simple, but categorizing user messages into intents isn’t always so clear cut.

Don’t overuse intents

Let’s say you’re building an assistant that asks insurance customers if they want to look up policies for home, life, or auto insurance. The user might reply “for my truck,” “automobile,” or “4-door sedan.” It would be a good idea to map truck, automobile, and sedan to the normalized value auto. This allows us to consistently save the value to a slot so we can base some logic around the user’s selection.

Rasa Core and Rasa NLU are independent of each other and can be used separately. That’s a wrap for our 10 best practices for designing NLU training data, but there’s one last thought we want to leave you with. There’s no magic, instant solution for building a quality data set. Finally, once you’ve made improvements to your training data, there’s one last step you shouldn’t skip. Testing ensures that things that worked before still work and your model is making the predictions you want.

Incremental training#

Easily roll back changes and implement review and testing workflows, for predictable, stable updates to your chatbot or voice assistant. Generators are placeholders that exist merely to reduce duplication in utterance templates, e.g., to substitute verb or preposition synonyms in a given template. The main content in an intent file is a list of phrases that a user might utter in order to accomplish the action represented by the intent.

Here are some tips for designing your NLU training data and pipeline to get the most
out of your bot. While writing stories, you do not have to deal with the specific
contents of the messages that the users send. Instead, you can take
advantage of the output from the NLU pipeline, which uses
a combination of an intent and entities to refer to all possible
messages the users can send with the same meaning.

Entities Roles and Groups#

1 one utterance for each of the two intents is depicted where each includes one of the two defined entity types. In the sixth step, a list of entity values for each type is created that is then used to fill the empty slots in the utterances in order to create the final dataset. As explained in the previous section there are two approaches that can be applied for replacing the empty slots in the utterances. The first one is depicted in step 5.1 where a list of ‘real’ entity values is extracted from a related knowledge base.