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News Articles Classification

This folder contains distilbert fine-tuned for classifying news articles. The model has been fine-tuned using the News Category Dataset dataset from Kaggle.

The code of the report was developed in Google Colab.

You can download the the weights of the pre-trained model (filename: model-004.h5) and the label encoder (filename:label_encoder.pkl) from Google Drive.

Dockerized Model

In the dockerized_model folder you will find the dockerized version of the news articles classifier model that serves an HTTP API with Flask.

Instructions

  1. Clone the repo locally or just download the current folder.

  2. Download the the weighs of the pre-trained model (filename: model-004.h5) and the label encoder (filename:label_encoder.pkl) from Google Drive and place them into the same folder (dockerized_model).

  3. Get into the folder, open a terminal and run the command

docker build -t dockerized_model .

This may take a few minutes.

  1. When the building is completed run the command
docker run -p 5000:5000 dockerized_model

Now the container is up and running and our model is ready to receive requests

  1. Open an new terminal and send a curl request. An example is the following
curl -X POST http://0.0.0.0:5000/predict -H 'Content-Type: application/json' -d '{"text": "Messi did not score today"}'

Note that the model receives requests in a json format with the structure: {"text": "Messi did not score today"}, where text is the joined headline and content of an article and outputs messages in a json format with the structure: {"label": "SPORTS", "confidence": 0.99, "version": "0.0.1"} where the predicted_label is a string containing the predicted by the model label, the confidence_of_prediction is a float containing the confidence of the prediction of the model and "version" is a string containing the version of the model (currently 0.0.1)