# ekorpkit 【iːkɔːkɪt】 : **eKo**nomic **R**esearch **P**ython Tool**kit**
[](https://badge.fury.io/py/ekorpkit) [](https://entelecheia.github.io/ekorpkit-book/) [](https://doi.org/10.5281/zenodo.6497226) [](https://github.com/entelecheia/ekorpkit/actions/workflows/release.yaml) [](https://github.com/entelecheia/ekorpkit/actions/workflows/codeql-analysis.yml) [](https://github.com/entelecheia/ekorpkit/actions/workflows/test.yaml) [](https://circleci.com/gh/entelecheia/ekorpkit/tree/main) [](https://codecov.io/gh/entelecheia/ekorpkit) [](https://github.com/entelecheia/ekorpkit/actions/workflows/markdown-autodocs.yaml)
eKorpkit provides a flexible interface for NLP and ML research pipelines such as extraction, transformation, tokenization, training, and visualization. Its powerful config composition is backed by [Hydra](https://hydra.cc/).
## Key features
### Easy Configuration
- You can compose your configuration dynamically, enabling you to easily get the perfect configuration for each research.
- You can override everything from the command line, which makes experimentation fast, and removes the need to maintain multiple similar configuration files.
- With a help of the **eKonf** class, it is also easy to compose configurations in a jupyter notebook environment.
### No Boilerplate
- eKorpkit lets you focus on the problem at hand instead of spending time on boilerplate code like command line flags, loading configuration files, logging etc.
### Workflows
- A workflow is a configurable automated process that will run one or more jobs.
- You can divide your research into several unit jobs (tasks), then combine those jobs into one workflow.
- You can have multiple workflows, each of which can perform a different set of tasks.
### Sharable and Reproducible
- With eKorpkit, you can easily share your datasets and models.
- Sharing configs along with datasets and models makes every research reproducible.
- You can share each unit jobs or an entire workflow.
### Pluggable Architecture
- eKorpkit has a pluggable architecture, enabling it to combine with your own implementation.
## [Tutorials](https://entelecheia.github.io/ekorpkit-book)
Tutorials for [ekorpkit](https://github.com/entelecheia/ekorpkit) package can be found at https://entelecheia.github.io/ekorpkit-book/
## [Installation](https://entelecheia.github.io/ekorpkit-book/docs/basics/install.html)
Install the latest version of ekorpkit:
```bash
pip install ekorpkit
```
To install all extra dependencies,
```bash
pip install ekorpkit[all]
```
## [The eKorpkit Corpus](https://github.com/entelecheia/ekorpkit/blob/main/docs/corpus/README.md)
The eKorpkit Corpus is a large, diverse, bilingual (ko/en) language modelling dataset.
## Citation
```tex
@software{lee_2022_6497226,
author = {Young Joon Lee},
title = {eKorpkit: eKonomic Research Python Toolkit},
month = apr,
year = 2022,
publisher = {Zenodo},
doi = {10.5281/zenodo.6497226},
url = {https://doi.org/10.5281/zenodo.6497226}
}
```
```tex
@software{lee_2022_ekorpkit,
author = {Young Joon Lee},
title = {eKorpkit: eKonomic Research Python Toolkit},
month = apr,
year = 2022,
publisher = {GitHub},
url = {https://github.com/entelecheia/ekorpkit}
}
```
## License
- eKorpkit is licensed under the [MIT License](https://opensource.org/licenses/MIT). This license covers the eKorpkit package and all of its components.
- Each corpus adheres to its own license policy. Please check the license of the corpus before using it!
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"description": "# ekorpkit \u3010i\u02d0k\u0254\u02d0k\u026at\u3011 : **eKo**nomic **R**esearch **P**ython Tool**kit**\n\n[](https://badge.fury.io/py/ekorpkit) [](https://entelecheia.github.io/ekorpkit-book/) [](https://doi.org/10.5281/zenodo.6497226) [](https://github.com/entelecheia/ekorpkit/actions/workflows/release.yaml) [](https://github.com/entelecheia/ekorpkit/actions/workflows/codeql-analysis.yml) [](https://github.com/entelecheia/ekorpkit/actions/workflows/test.yaml) [](https://circleci.com/gh/entelecheia/ekorpkit/tree/main) [](https://codecov.io/gh/entelecheia/ekorpkit) [](https://github.com/entelecheia/ekorpkit/actions/workflows/markdown-autodocs.yaml)\n\neKorpkit provides a flexible interface for NLP and ML research pipelines such as extraction, transformation, tokenization, training, and visualization. Its powerful config composition is backed by [Hydra](https://hydra.cc/).\n\n## Key features\n\n### Easy Configuration\n\n- You can compose your configuration dynamically, enabling you to easily get the perfect configuration for each research. \n- You can override everything from the command line, which makes experimentation fast, and removes the need to maintain multiple similar configuration files. \n- With a help of the **eKonf** class, it is also easy to compose configurations in a jupyter notebook environment.\n\n### No Boilerplate\n\n- eKorpkit lets you focus on the problem at hand instead of spending time on boilerplate code like command line flags, loading configuration files, logging etc.\n\n### Workflows\n\n- A workflow is a configurable automated process that will run one or more jobs.\n- You can divide your research into several unit jobs (tasks), then combine those jobs into one workflow.\n- You can have multiple workflows, each of which can perform a different set of tasks.\n\n### Sharable and Reproducible\n\n- With eKorpkit, you can easily share your datasets and models.\n- Sharing configs along with datasets and models makes every research reproducible.\n- You can share each unit jobs or an entire workflow.\n\n### Pluggable Architecture\n\n- eKorpkit has a pluggable architecture, enabling it to combine with your own implementation.\n\n## [Tutorials](https://entelecheia.github.io/ekorpkit-book)\n\nTutorials for [ekorpkit](https://github.com/entelecheia/ekorpkit) package can be found at https://entelecheia.github.io/ekorpkit-book/\n\n## [Installation](https://entelecheia.github.io/ekorpkit-book/docs/basics/install.html)\n\nInstall the latest version of ekorpkit:\n\n```bash\npip install ekorpkit\n```\n\nTo install all extra dependencies,\n\n```bash\npip install ekorpkit[all]\n```\n\n## [The eKorpkit Corpus](https://github.com/entelecheia/ekorpkit/blob/main/docs/corpus/README.md)\n\nThe eKorpkit Corpus is a large, diverse, bilingual (ko/en) language modelling dataset.\n\n\n\n## Citation\n\n```tex\n@software{lee_2022_6497226,\n author = {Young Joon Lee},\n title = {eKorpkit: eKonomic Research Python Toolkit},\n month = apr,\n year = 2022,\n publisher = {Zenodo},\n doi = {10.5281/zenodo.6497226},\n url = {https://doi.org/10.5281/zenodo.6497226}\n}\n```\n\n```tex\n@software{lee_2022_ekorpkit,\n author = {Young Joon Lee},\n title = {eKorpkit: eKonomic Research Python Toolkit},\n month = apr,\n year = 2022,\n publisher = {GitHub},\n url = {https://github.com/entelecheia/ekorpkit}\n}\n```\n\n## License\n\n- eKorpkit is licensed under the [MIT License](https://opensource.org/licenses/MIT). This license covers the eKorpkit package and all of its components.\n- Each corpus adheres to its own license policy. Please check the license of the corpus before using it!\n\n\n",
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