# Using the Trained Model
When your model is prepared, you can implement the model. First we need to initialize the model;
const modelUrl = 'file://C:/Projects/my-chatbot/models/model.json';
const metadataUrl = 'models/model_metadata.json';
const mymodel = await bkLabs.nlu.loadModel(modelUrl, metadataUrl, callbackFunction);
# Parameters
modelUrl: string
This is the URL where your trained model is located. You need to provided an Absolute URL here.
metadataUrl: string
This is the URL where model_metadata.json
file is located. You need to provide a Relative URL here.
callbackFunction: Provide a callback function to be executed when the model is loaded. This is where you can start your classification work.
# Returns
Array[0]
Initialized Model
Array[1]
Response Text data.
# Encode Text Input
Once the model is initialized, you need to encode the text input before parsing it to the model to classify.
const sentence = 'Hello, nice day!';
const encodedSentence = await bkLabs.nlu.encodeText(sentence);
# Parameters
sentence: string
Text input to classify.
# Returns
Tensor
Output Tensor.
# Classify Sentence
Once the text input is encoded, pass the encoded tensor to classify input text.
const predictData = await mymodel[0].predict(encodedSentence);
# Parameters
encodedSentence Tensor
encoded text input
# Returns
Tensor
Prediction result as a Tensor
# Get a Reply
Now you can get a reply that matches the intent of your input.
const myReply = bkLabs.nlu.predictReply(predictData, mymodel[1]);
console.log(myReply);
# Parameters
predictData Tensor
Output Tensor from prediction
mymodel[1] Array<object>
Response data extracted earlier when we initialized the model.
# Returns
string
Returns a reply as string.
# Example
The final example code will look something like this:
const modelUrl = 'file://C:/Projects/my-chatbot/models/model.json';
const metadataUrl = 'models/model_metadata.json';
const sentence = 'Hello, nice day!';
const mymodel = await bkLabs.nlu.loadModel(modelUrl, metadataUrl, callbackFunction);
async function callbackFunction() {
const encodedSentence = await bkLabs.nlu.encodeText(sentence);
const predictData = await mymodel[0].predict(encodedSentence);
const myReply = bkLabs.nlu.predictReply(predictData, mymodel[1]);
console.log(myReply);
}
Please feel free to report a bug (opens new window) if you find while using berkelium (opens new window).