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Disinformation news classification

This repository holds the code to train a disinformation vs real news text classifier. The data is expected on a folder named data, which is not included in the repository.

Instructions

  1. To reproduce the environment using Anaconda run the following command in an Anaconda Prompt after cloning or downloading this repository and changing directories inside of it.
conda env create --file sentinel.yml
  1. Or if using Windows:
conda create --name sentinel --file pkgs.txt

NOTE: If GPU is available you can install conda install pytorch cudatoolkit=10.0 -c pytorch instead of cpuonly.

The code in this repository is for an environment with CPU only for ease of reproducibility.

Notebooks

  • First an exploratory data analysis of the texts is presented in EDA.

  • Secondly, a transformer-based Language Model is fine-tuned in a text classification task in Classification.

Deployment

  • The trained model is saved to the outputs folder with the name pytorch_model.bin after training under the checkpoints folder. The model is not commited to the repository due to its size so it is necessary to run the notebook. The last section of the notebook presents the way to predict the value of an arbitrary news title.

Clustering

In this notebook, spaCy takes care of the lemmatization and tokenization of the texts. For this, we need to download a model first.

python -m spacy download en_core_web_sm

Proposal

For the document of the proposal, see this inside GitHub or the PDF version of it.