personal code for hyundai ship tag-mapping project
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Richard Wong 1b9c4323c3 Feat: added classification with number tokens
- added analysis for overall statistics
2025-01-09 23:13:24 +09:00
analysis Feat: added classification with number tokens 2025-01-09 23:13:24 +09:00
data_import Chore: changed ipynb to py files in the data_preprocess folder 2024-10-29 22:55:22 +09:00
data_preprocess Feat: added post_processing based on rules 2024-12-18 13:43:56 +09:00
evaluation Doc: updated README.md to reflect execution order 2024-10-31 16:51:47 +09:00
interpretation Feat: added post_processing based on rules 2024-12-18 13:43:56 +09:00
overall Feat: added classification with number tokens 2025-01-09 23:13:24 +09:00
post_process Feat: added post_processing based on rules 2024-12-18 13:43:56 +09:00
production Feat: added post_processing based on rules 2024-12-18 13:43:56 +09:00
relevant_class Feat: added classification with number tokens 2025-01-09 23:13:24 +09:00
train Feat: added classification with number tokens 2025-01-09 23:13:24 +09:00
translation Chore: re-organized train folders to have standardized naming schemes 2024-11-20 15:07:47 +09:00
.gitignore Feat: added post_processing based on rules 2024-12-18 13:43:56 +09:00
README.md Doc: updated README.md to reflect execution order 2024-10-31 16:51:47 +09:00

README.md

hipom_data_mapping

Before we begin

This repository utilizes .py files rather than .ipynb for greater clarity.

If you use vscode, just use the ipython functionality from .py files.

In order to generate .ipynb file from .py file, you can do the following:

jupytext --to notebook your_script.py

Order of Execution

  • data_import: Import data from database
  • data_preprocess: Apply pre-process method
  • train: Train mapping model and apply model on test data
  • post_process: Apply post-processing method