# Training Data Format ## Data Formats You can provide training data as Markdown or as JSON, as a single file or as a directory containing multiple files. Note that Markdown is usually easier to work with. ### Markdown Format Markdown is the easiest Rasa NLU format for humans to read and write. Examples are listed using the unordered list syntax, e.g. minus `-`, asterisk `*`, or plus `+`. Examples are grouped by intent, and entities are annotated as Markdown links, e.g. `[entity](entity name)`. ``` ## intent:check_balance - what is my balance - how much do I have on my [savings](source_account) - how much do I have on my [savings account](source_account:savings) - Could I pay in [yen](currency)? ## intent:greet - hey - hello ## synonym:savings - pink pig ## regex:zipcode - [0-9]{5} ## lookup:additional_currencies path/to/currencies.txt ``` The training data for Rasa NLU is structured into different parts: - common examples - synonyms - regex features and - lookup tables While common examples is the only part that is mandatory, including the others will help the NLU model learn the domain with fewer examples and also help it be more confident of its predictions. Synonyms will map extracted entities to the same name, for example mapping “my savings account” to simply “savings”. However, this only happens _after_ the entities have been extracted, so you need to provide examples with the synonyms present so that Rasa can learn to pick them up. Lookup tables may be specified as plain text files containing newline-separated words or phrases. Upon loading the training data, these files are used to generate case-insensitive regex patterns that are added to the regex features. Note The common theme here is that common examples, regex features and lookup tables merely act as cues to the final NLU model by providing additional features to the machine learning algorithm during training. Therefore, it must not be assumed that having a single example would be enough for the model to robustly identify intents and/or entities across all variants of that example. Note `/` symbol is reserved as a delimiter to separate retrieval intents from response text identifiers. Make sure not to use it in the name of your intents. ### JSON Format The JSON format consists of a top-level object called `rasa_nlu_data`, with the keys `common_examples`, `entity_synonyms` and `regex_features`. The most important one is `common_examples`. ``` { "rasa_nlu_data": { "common_examples": [], "regex_features" : [], "lookup_tables" : [], "entity_synonyms": [] } } ``` The `common_examples` are used to train your model. You should put all of your training examples in the `common_examples` array. Regex features are a tool to help the classifier detect entities or intents and improve the performance. ## Improving Intent Classification and Entity Recognition ### Common Examples Common examples have three components: `text`, `intent` and `entities`. The first two are strings while the last one is an array. > - The _text_ is the user message \[required\] > - The _intent_ is the intent that should be associated with the text \[optional\] > - The _entities_ are specific parts of the text which need to be identified \[optional\] Entities are specified with a `start` and an `end` value, which together make a python style range to apply to the string, e.g. in the example below, with `text="show me chinese restaurants"`, then `text[8:15] == 'chinese'`. Entities can span multiple words, and in fact the `value` field does not have to correspond exactly to the substring in your example. That way you can map synonyms, or misspellings, to the same `value`. ``` ## intent:restaurant_search - show me [chinese](cuisine) restaurants ``` ### Regular Expression Features Regular expressions can be used to support the intent classification and entity extraction. For example, if your entity has a deterministic structure (like a zipcode or an email address), you can use a regular expression to ease detection of that entity. For the zipcode example it might look like this: ``` ## regex:zipcode - [0-9]{5} ## regex:greet - hey[^\s]* ``` The name doesn’t define the entity nor the intent, it is just a human readable description for you to remember what this regex is used for and is the title of the corresponding pattern feature. As you can see in the above example, you can also use the regex features to improve the intent classification performance. ### Lookup Tables Lookup tables provide a convenient way to supply a list of entity examples. The supplied lookup table files must be in a newline-delimited format. For example, `data/test/lookup_tables/plates.txt` may contain: ``` tacos beef mapo tofu burrito lettuce wrap ``` And can be loaded and used as shown here: ``` ## lookup:plates data/test/lookup_tables/plates.txt ## intent:food_request - I'd like beef [tacos](plates) and a [burrito](plates) - How about some [mapo tofu](plates) ``` When lookup tables are supplied in training data, the contents are combined into a large, case-insensitive regex pattern that looks for exact matches in the training examples. These regexes match over multiple tokens, so `lettuce wrap` would match `get me a lettuce wrap ASAP`. These regexes are processed identically to the regular regex patterns directly specified in the training data. Note For lookup tables to be effective, there must be a few examples of matches in your training data. Otherwise the model will not learn to use the lookup table match features. Warning You have to be careful when you add data to the lookup table. For example if there are false positives or other noise in the table, this can hurt performance. So make sure your lookup tables contain clean data. ### Normalizing Data ### Entity Synonyms If you define entities as having the same value they will be treated as synonyms. Here is an example of that: ``` ## intent:search - in the center of [NYC](city:New York City) - in the centre of [New York City](city) ``` As you can see, the entity `city` has the value `New York City` in both examples, even though the text in the first example states `NYC`. By defining the value attribute to be different from the value found in the text between start and end index of the entity, you can define a synonym. Whenever the same text will be found, the value will use the synonym instead of the actual text in the message. To use the synonyms defined in your training data, you need to make sure the pipeline contains the `EntitySynonymMapper` component. Alternatively, you can add an “entity_synonyms” array to define several synonyms to one entity value. Here is an example of that: ``` ## synonym:New York City - NYC - nyc - the big apple ``` Note Please note that adding synonyms using the above format does not improve the model’s classification of those entities. **Entities must be properly classified before they can be replaced with the synonym value.** ### Generating More Entity Examples It is sometimes helpful to generate a bunch of entity examples, for example if you have a database of restaurant names. There are a couple of tools built by the community to help with that. You can use [Chatito](https://rodrigopivi.github.io/Chatito/) , a tool for generating training datasets in rasa’s format using a simple DSL or [Tracy](https://yuukanoo.github.io/tracy), a simple GUI to create training datasets for rasa. However, creating synthetic examples usually leads to overfitting, it is a better idea to use [Lookup Tables](https://legacy-docs-v1.rasa.com/1.8.3/nlu/training-data-format/#lookup-tables) instead if you have a large number of entity values.