https://github.com/mihail911/fake-news
Building a fake news detector from initial ideation to model deployment
https://github.com/mihail911/fake-news
deeplearning machinelearning mlops natural-language-processing nlp pytorch scikit-learn
Last synced: 3 months ago
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Building a fake news detector from initial ideation to model deployment
- Host: GitHub
- URL: https://github.com/mihail911/fake-news
- Owner: mihail911
- License: agpl-3.0
- Created: 2020-09-18T05:59:08.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2026-02-15T14:28:50.000Z (4 months ago)
- Last Synced: 2026-02-15T21:10:56.976Z (4 months ago)
- Topics: deeplearning, machinelearning, mlops, natural-language-processing, nlp, pytorch, scikit-learn
- Language: Jupyter Notebook
- Homepage:
- Size: 2.59 MB
- Stars: 166
- Watchers: 4
- Forks: 64
- Open Issues: 48
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Fake News Detector Powered By Machine Learning
A complete example of building an end-to-end machine learning project from initial idea to deployment.
This repo accompanies the blog post series describing how to build a fake news detection application. The posts included here:
- [Initial Setup and Tooling](https://www.mihaileric.com/posts/setting-up-a-machine-learning-project/): Describes project ideation, setting up your repository, and initial project tooling.
- [Exploratory Data Analysis](https://www.mihaileric.com/posts/performing-exploratory-data-analysis/): Describes how to acquire a dataset and perform exploratory data analysis with tools like [Pandas](https://pandas.pydata.org/) in order to better understand the problem.
- [Building a V1 Model Training/Testing Pipeline](https://www.mihaileric.com/posts/machine-learning-project-model-v1/): Describes how to get a functional training/evaluation pipeline for the first ML model (a random-forest classifier), including how to properly test various parts of your pipeline.
- [Error Analysis and Model V2](https://www.mihaileric.com/posts/machine-learning-project-error-analysis-model-v2/): Describes how to interpret what your first model has learned through feature analysis (via techniques like [Shapley values](https://christophm.github.io/interpretable-ml-book/shapley.html)) and error analysis. Also works toward a second model powered by [Roberta](https://ai.facebook.com/blog/roberta-an-optimized-method-for-pretraining-self-supervised-nlp-systems/).
- [Model Deployment and Continuous Integration](https://www.mihaileric.com/posts/machine-learning-project-model-deployment/): Describes how to deploy your model using [FastAPI](https://fastapi.tiangolo.com/) and [Docker](https://www.docker.com/) and build an accompanying Chrome extension. Also illustrates key components of a continuous integration system for collaborating on the application with other team members in a scalable and reproducible fashion.
## Features
* **Random forest classifier** powered by [Scikit-learn](https://scikit-learn.org/stable/).
* **RoBERTa** model powered by [HuggingFace Transformers](https://huggingface.co/transformers/) and [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning).
* **Data versioning** and configurable train/test pipelines using [DVC](https://github.com/iterative/dvc).
* **Exploratory data analysis** using [Pandas](https://pandas.pydata.org/).
* **Experiment tracking** and **logging** via [MLFlow](https://mlflow.org/).
* **Continuous integration** with [Github actions](https://github.com/features/actions).
* **Functionality tests** powered by [PyTest](https://docs.pytest.org/en/stable/) and [Great Expectations](https://greatexpectations.io/).
* **Error** and **model feature analysis** via [SHAP](https://github.com/slundberg/shap).
* **Production-ready server** via [FastAPI](https://fastapi.tiangolo.com/) and [Gunicorn](https://gunicorn.org/).
* **Chrome extension** for interacting with a model in the [browser](https://chrome.google.com/webstore/category/extensions?hl=en).
## How to Use It
Go to the root directory of the repo and run:
```
pip install -r requirements.txt
```
Download the data from [this link](https://github.com/Tariq60/LIAR-PLUS/tree/master/dataset/tsv) into `data/raw`.
You're ready to go!
### Train
To train the [random forest baseline](https://www.mihaileric.com/posts/machine-learning-project-model-v1/), run the following from the root directory:
```
dvc repro train-random-forest
```
Your output should look something like the following:
```
INFO - 2021-01-21 21:26:49,779 - features.py - Creating featurizer from scratch...
INFO - 2021-01-21 21:26:49,781 - tree_based.py - Initializing model from scratch...
INFO - 2021-01-21 21:26:49,781 - train.py - Training model...
INFO - 2021-01-21 21:26:50,163 - features.py - Saving featurizer to disk...
INFO - 2021-01-21 21:26:50,169 - tree_based.py - Featurizing data from scratch...
INFO - 2021-01-21 21:26:59,360 - tree_based.py - Saving model to disk...
INFO - 2021-01-21 21:26:59,459 - train.py - Evaluating model...
INFO - 2021-01-21 21:26:59,584 - train.py - Val metrics: {'val f1': 0.7587628865979381, 'val accuracy': 0.7266355140186916, 'val auc': 0.8156070164865074, 'val true negative': 381, 'val false negative': 116, 'val false positive': 235, 'val true positive': 552}
```
### Deploy
Once you have successfully trained a model using the step above, you should have a model checkpoint saved in `model_checkpoints/random_forest`.
Now build your deployment Docker image:
```
docker build . -f deploy/Dockerfile.serve -t fake-news-deploy
```
Once your image is built, you can run the model locally via a REST API with:
```
docker run -p 8000:80 -e MODEL_DIR="/home/fake-news/random_forest" -e MODULE_NAME="fake_news.server.main" fake-news-deploy
```
From here you can interact with the API using [Postman](https://www.postman.com/) or through a simple cURL request:
```
curl -X POST http://127.0.0.1:8000/api/predict-fakeness -d '{"text": "some example string"}'
```