git clone https://github.com/anaselmhamdi/sponsor-inference.git
cd sponsor-inference
pip3 install -r requirements.txt
wget https://anas-models.s3.amazonaws.com/tut7-model.pt --directory-prefix="./app"
You'll need around 2.5 Gb of free space and about 2 Gb of RAM to run optimally
Using a 70-15-15 training-validation-test split.
The script will download and cache the BERT cased modes (11M+ parameters)
python3 app/train.py -f training_file.json
The training file should contain a list of objects such as:
[{"This video is sponsored by Squarespace","label":"sponsor"}, {"Welcome to this NLP tutorial","label":"content"}]
I trained this model with a dataset I built based off of SponsorBlock's labels.
I used youtube-dl to get the english auto captions when they were available on the diffrent videos.
The dataset is publicly available here on Kaggle.
It took 4 hours 48 minutes to train on a 16 Gb GPU on a Kaggle kernel available here
It yielded a test accuracy of 93.85%.
python3 app/inference.py -s "This video is sponsored by Squarespace"
Should print:
{ "class": "sponsor", "probability": 0.9990846614236943 }
python3 app/inference.py -u "https://www.youtube.com/watch?v=MlOPPuNv4Ec"
Will download the video captions, and label it by 10s chunks.
The results will be written in the labeled_results.json file.
You can run this model for inference locally by running:
cd app
uvicorn main:app
You can see the routes at http://127.0.0.1:8000/docs