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Initial git commit. Here we have downloaded the code from the DVC site
Initialized DVC, and added a virtual environment
Retrieved example data which is about 41Mb in size. Because of how DVC works, this data is not commited to the git repo, but instead exists in the DVC cache.
Unzipped the data file. According to the command given, DVC knows to automatically add the unzipped data file to the .gitignore and the .dvc/cache
Performed XML to TSV and performed the data train/test split. These are two consecutive steps of the data pipeline. This step goes to show that you can perform multiple steps of the pipeline before commiting without any problems
Peformed the following DVC steps - Featurization, Training and model evaluation.For the final step we create an eval.txt file which includes an AUC metric for measuring the performance of the model.
Outside of the original DVC tutorial we have created this file as a metric using the flag -M instead of the -o flag appearing in the original tutorial.
Created a new branch called bigram. Like it's name, we have tried to use bigrams (features extract from word pairs) additionally to the unigrams (single word features) used earlier.
This step is performed in order to try and improve our AUC metric. It has indeed improved, but by a very small amount, which is not so exciting. We are logging this relatively unsuccessful attempt nontheless.
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