Chatito helps you generate datasets for training and validating chatbot models using a simple DSL.
If you are building chatbots using commercial models, open source frameworks or writing your own natural language processing model, you need training and testing examples. Chatito is here to help you.
This project contains the:
For the full language specification and documentation, please refer to the DSL spec document.
Overfitting is a problem that can be prevented if we use Chatito correctly. The idea behind this tool, is to have an intersection between data augmentation and a description of possible sentences combinations. It is not intended to generate deterministic datasets that may overfit a single sentence model, in those cases, you can have some control over the generation paths only pull samples as required.
AI Blueprints: How to build and deploy AI business projects implements practical full chatbot examples using chatito at chapter 7.
3 steps to convert chatbot training data between different NLP Providers details a simple way to convert the data format to non implemented adapters. You can use a generated dataset with providers like DialogFlow, Wit.ai and Watson.
Aida-nlp is a tiny experimental NLP deep learning library for text classification and NER. Built with Tensorflow.js, Keras and Chatito. Implemented in JS and Python.
The language is independent from the generated output format and because each model can receive different parameters and settings, this are the currently implemented data formats, if your provider is not listed, at the Tools and resources section there is more information on how to support more formats.
NOTE: Samples are not shuffled between intents for easier review and because some adapters stream samples directly to the file and it's recommended to split intents in different files for easier review and maintenance.
Rasa is an open source machine learning framework for automated text and voice-based conversations. Understand messages, hold conversations, and connect to messaging channels and APIs. Chatito can help you build a dataset for the Rasa NLU component.
One particular behavior of the Rasa adapter is that when a slot definition sentence only contains one alias, and that alias defines the 'synonym' argument with 'true', the generated Rasa dataset will map the alias as a synonym. e.g.:
%[some intent]('training': '1') @[some slot] @[some slot] ~[some slot synonyms] ~[some slot synonyms]('synonym': 'true') synonym 1 synonym 2
In this example, the generated Rasa dataset will contain the
synonym 1 and
synonym 2 mapping to
some slot synonyms.
Flair A very simple framework for state-of-the-art NLP. Developed by Zalando Research. It provides state of the art (GPT, BERT, RoBERTa, XLNet, ELMo, etc...) pre trained embeddings for many languages that work out of the box. This adapter supports the
text classification dataset in FastText format and the
named entity recognition dataset in two column BIO annotated words, as documented at flair corpus documentation. This two data formats are very common and with many other providers or models.
The NER dataset requires a word tokenization processing that is currently done using a simple tokenizer.
NOTE: Flair adapter is only available for the NodeJS NPM CLI package, not for the IDE.
To train a LUIS model, you will need to post the utterance in batches to the relevant API for training or testing.
Reference issue: #61
Snips NLU is another great open source framework for NLU. One particular behavior of the Snips adapter is that you can define entity types for the slots. e.g.:
%[date search]('training':'1') for @[date] @[date]('entity': 'snips/datetime') ~[today] ~[tomorrow]
In the previous example, all
@[date] values will be tagged with the
snips/datetime entity tag.
Use the default format if you plan to train a custom model or if you are writing a custom adapter. This is the most flexible format because you can annotate
Intents with custom entity arguments, and they all will be present at the generated output, so for example, you could also include dialog/response generation logic with the DSL. E.g.:
%[some intent]('context': 'some annotation') @[some slot] ~[please?] @[some slot]('required': 'true', 'type': 'some type') ~[some alias here]
Custom entities like 'context', 'required' and 'type' will be available at the output so you can handle this custom arguments as you want.
Chatito supports Node.js
Install it with yarn or npm:
npm i chatito --save
Then create a definition file (e.g.:
trainClimateBot.chatito) with your code.
Run the npm generator:
npx chatito trainClimateBot.chatito
The generated dataset should be available next to your definition file.
Here is the full npm generator options:
npx chatito <pathToFileOrDirectory> --format=<format> --formatOptions=<formatOptions> --outputPath=<outputPath> --trainingFileName=<trainingFileName> --testingFileName=<testingFileName> --defaultDistribution=<defaultDistribution> --autoAliases=<autoAliases>
<pathToFileOrDirectory> path to a
.chatito file or a directory that contains chatito files. If it is a directory, will search recursively for all
*.chatito files inside and use them to generate the dataset. e.g.:
<formatOptions> Optional. Path to a .json file that each adapter optionally can use
<outputPath> Optional. The directory where to save the generated datasets. Uses the current directory as default.
<trainingFileName> Optional. The name of the generated training dataset file. Do not forget to add a .json extension at the end. Uses
<format>_dataset_training.json as default file name.
<testingFileName> Optional. The name of the generated testing dataset file. Do not forget to add a .json extension at the end. Uses
<format>_dataset_testing.json as default file name.
<defaultDistribution> Optional. The default frequency distribution if not defined at the entity level. Defaults to
regular and can be set to
<autoAliases> Optional. The generaor behavior when finding an undefined alias. Valid opions are
restrict. Defauls to 'allow'.