text evaluation

evaluation of text embedding techniques

GETTING STARTED

• Create and switch to the virtual environment:

cd text_evaluation
make create_environment
conda activate text_evaluation
make requirements

• Explore the notebooks in the notebooks directory

Project Organization

• LICENSE
• Makefile
  • top-level makefile. Type make for a list of valid commands
• README.md
  • this file
• data
  • Data directory. often symlinked to a filesystem with lots of space
  • data/raw
    • Raw (immutable) hash-verified downloads
  • data/interim
    • Extracted and interim data representations
  • data/processed
    • The final, canonical data sets for modeling.
• docs
  • A default Sphinx project; see sphinx-doc.org for details
• models
  • Trained and serialized models, model predictions, or model summaries
  • models/trained
    • Trained models
  • models/output
    • predictions and transformations from the trained models
• notebooks
  • Jupyter notebooks. Naming convention is a number (for ordering), the creator's initials, and a short - delimited description, e.g. 1.0-jqp-initial-data-exploration.
• references
  • Data dictionaries, manuals, and all other explanatory materials.
• reports
  • Generated analysis as HTML, PDF, LaTeX, etc.
  • reports/figures
    • Generated graphics and figures to be used in reporting
  • reports/tables
    • Generated data tables to be used in reporting
  • reports/summary
    • Generated summary information to be used in reporting
• requirements.txt
  • (if using pip+virtualenv) The requirements file for reproducing the analysis environment, e.g. generated with pip freeze > requirements.txt
• environment.yml
  • (if using conda) The YAML file for reproducing the analysis environment
• setup.py
  • Turns contents of src into a pip-installable python module (pip install -e .) so it can be imported in python code
• src
  • Source code for use in this project.
  • src/__init__.py
    • Makes src a Python module
  • src/data
    • Scripts to fetch or generate data. In particular:
    • src/data/make_dataset.py
      • Run with python -m src.data.make_dataset fetch or python -m src.data.make_dataset process
  • src/analysis
    • Scripts to turn datasets into output products
  • src/models
    • Scripts to train models and then use trained models to make predictions. e.g. predict_model.py, train_model.py
• tox.ini
  • tox file with settings for running tox; see tox.testrun.org

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This project was built using cookiecutter-easydata, an experimental fork of [cookiecutter-data-science](https://github.com/drivendata/cookiecutter-data-science) aimed at making your data science workflow reproducible.