Creating Training API for Text Classifier Predictive Model
Text classification is the process of assigning tags or categories to text according to its content. We will be building a text classification model and create an api for serving. Let us start.
Training Data : We are using small data set related to T20 cricket leagues to understand the code flow line by line
Code Component Details:
We will go through each of these code components
trainingData.json : It contains the training data set in json format
G:\textClassifier\machine_learning_training_service\api\run.py
We are using flask to run the services. In our case the training service will be running at http://localhost:9060/
G:\textClassifier\machine_learning_training_service\api\app\modelTrainer.py
The service to initiate the training would be http://localhost:9060//api/v1.0/train/textClassifier/model
The above API will call the function textClassifierTrainer() which is inside modelTrainer.py
G:\textClassifier\machine_learning_training_service\api\app\utility\textClassificationModeler.py
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The above code 1) reads the training file 2) converts the data to bag of words using 1 hot encoding 3) prepares training data set 4) builds a 3 layers neural network 4) saves the model files at G:\textClassifier\trainedModel
Execution Steps
Open the conda prompt and go to G:\textClassifier\machine_learning_training_service\api\
execute python run.py
The application is up and running at http://localhost:9060/
Open Advance Rest Client chrome extension and execute http://localhost:9060/api/v1.0/train/textClassifier/model as shown below:
Log shows training got completed
Model files gets generated at G:\textClassifier\trainedModel
In the next Post, I will be posting the steps to use the above model for prediction task using API http://localhost:9080/api/v1.0/leagues
Thanks for your time, likes and claps :)
