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Integration:  dvc git github
Arslan ff3aa02f98
Modify embed_tfidf stage to use save_npz
3 years ago
3835ed1044
Add base data and track it with DVC. Add associated python requirements
3 years ago
5972fe96ad
Tfidf vectorizer with 25k features
3 years ago
ff3aa02f98
Modify embed_tfidf stage to use save_npz
3 years ago
a8aba98742
Add necessary folders - data,metrics,artifacts
3 years ago
ff3aa02f98
Modify embed_tfidf stage to use save_npz
3 years ago
ff3aa02f98
Modify embed_tfidf stage to use save_npz
3 years ago
3835ed1044
Add base data and track it with DVC. Add associated python requirements
3 years ago
92fdede2b3
Add basic app config (shared)
3 years ago
6ee8331777
Initial commit
3 years ago
6ee8331777
Initial commit
3 years ago
f352145576
Add api base/skeleton
3 years ago
ff3aa02f98
Modify embed_tfidf stage to use save_npz
3 years ago
ff3aa02f98
Modify embed_tfidf stage to use save_npz
3 years ago
54b636dc7e
Track experiments using DVC exp feature
3 years ago
4fd725a465
Req Add: uvicorn, gunicorn, python-multipart
3 years ago
4fd725a465
Req Add: uvicorn, gunicorn, python-multipart
3 years ago
4fd725a465
Req Add: uvicorn, gunicorn, python-multipart
3 years ago
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README.md

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Turkish Text Categorization

This is meant to serve as an example of how to structure and work on your ML projects, and deploy the resultant models.

The dataset I have chosen can be found on Kaggle.

  • The python packages can be installed via the requirements.txt file (in a venv), or using Poetry (preferred way).
  • To get the models and data files, you'll also need DVC. Just run dvc pull in this repo to get the data/artifacts.
Tip!

Press p or to see the previous file or, n or to see the next file

About

https://www.kaggle.com/savasy/ttc4900

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