data-modori 0.1.5

LMOps Tool for Korean

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Data-Modori: LMOps Tool for Korean

 

TEAMREBOOTT website ^HOT      TeamAR platform ^{TRY IT OUT}

 

📘Documentation | 🛠️Installation | 🤔Reporting Issues

Data-Modori is a creative and advanced platform that guides you into the realm of data, offering diverse possibilities by collecting information from various sources. We gather all the puzzle pieces of data, assemble them into one, and invite you into the world of the information you desire.

• Data Integration: We collect data from various sources, integrating them into one central hub for your convenience.
• Flexible Analysis: Utilize advanced analysis tools to delve into your data, gaining new insights and perspectives.
• Customized Results: Organize and present data according to your requirements, delivering tailored results.
• User-Friendly Interface: An intuitive and easy-to-use interface allows users to harness the power of data without requiring advanced knowledge.

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Table of Contents

• Data-Modori: LMOps Tool for Korean
• Table of Contents
  • Installation
  • Data Processing
  • Data Analysis
  • Data Visualization
  • Build Up Config Files
  • Documentation
  • License
  • Contributing
  • Acknowledgement
  • References

Installation

• Get source code from Github

git clone https://github.com/teamreboott/data-modori
cd data-modori

• Run the following commands to install the latest basic data_modori version in editable mode:

pip install -v -e .

• Some OPs rely on some other too large or low-platform-compatibility third-party libraries. You can install optional dependencies as needed:

pip install -v -e .  # install a minimal dependencies, which support the basic functions
pip install -v -e .[tools] # install a subset of tools dependencies

• The dependency options are listed below:

Tag	Description
. or .[mini]	Install minimal dependencies for basic Data-Modori.
.[all]	Install all optional dependencies (including minimal dependencies and all of the following).
.[sci]	Install all dependencies for all OPs.
.[dist]	Install dependencies for distributed data processing. (Experimental)
.[dev]	Install dependencies for developing the package as contributors.
.[tools]	Install dependencies for dedicated tools, such as quality classifiers.

Data Processing

• Run process_data.py tool with your config as the argument to process your dataset.

python tools/process_data.py --config configs/process.yaml

• Note: For some operators that involve third-party models or resources which are not stored locally on your computer, it might be slow for the first running because these ops need to download corresponding resources into a directory first. The default download cache directory is ~/.cache/data_modori. Change the cache location by setting the shell environment variable, DATA_MODORI_CACHE_HOME to another directory, and you can also change DATA_MODORI_MODELS_CACHE or DATA_MODORI_ASSETS_CACHE in the same way:

# cache home
export DATA_MODORI_CACHE_HOME="/path/to/another/directory"
# cache models
export DATA_MODORI_MODELS_CACHE="/path/to/another/directory/models"
# cache assets
export DATA_MODORI_ASSETS_CACHE="/path/to/another/directory/assets"

Data Analysis

• Run analyze_data.py tool with your config as the argument to analyse your dataset.

python tools/analyze_data.py --config configs/analyser.yaml

• Note: Analyser only compute stats of Filter ops. So extra Mapper or Deduplicator ops will be ignored in the analysis process.

Data Visualization

• Run app.py tool to visualize your dataset in your browser.
• Note: only available for installation from source.

streamlit run app.py

Build Up Config Files

• Config files specify some global arguments, and an operator list for the data process. You need to set:
  • Global arguments: input/output dataset path, number of workers, etc.
  • Operator list: list operators with their arguments used to process the dataset.
• You can build up your own config files by:
  • ➖：Modify from our example config file config_all.yaml which includes all ops and default arguments. You just need to remove ops that you won't use and refine some arguments of ops.
  • ➕：Build up your own config files from scratch. You can refer our example config file config_all.yaml, op documents, and advanced Build-Up Guide for developers.
  • Besides the yaml files, you also have the flexibility to specify just one (of several) parameters on the command line, which will override the values in yaml files.

python xxx.py --config configs/process.yaml --language_id_score_filter.lang=ko 

# Process config example for dataset

# global parameters
project_name: 'demo-process'
dataset_path: './data/test.json'  # path to your dataset directory or file
export_path: './output/test.jsonl'

np: 4  # number of subprocess to process your dataset
text_keys: 'content'

# process schedule
# a list of several process operators with their arguments
process:
  - language_id_score_filter:
      lang: 'en'

Documentation

• Overview
• Operators
• Configs
• Developer Guide

License

Data-Modori is released under Apache License 2.0.

Contributing

We are in a rapidly developing field and greatly welcome contributions of new features, bug fixes and better documentations. Please refer to How-to Guide for Developers.

Acknowledgement

Data-Modori is used across various LLM products and research initiatives, including industrial LLMs from Teamreboott AI TEAM(AR), such as AUT for trade and AUW for work.

We look forward to more of your experience, suggestions and discussions for collaboration!

Data-Modori thanks and refers to several community projects, such as data-juicer, Huggingface-Datasets, Bloom, Pile, Megatron-LM, DeepSpeed, Arrow, Ray, Beam, LM-Harness, HELM, ....

References

If you find our work useful for your research or development, please kindly cite the following paper.

@misc{chen2023datajuicer,
title={Data-Juicer: A One-Stop Data Processing System for Large Language Models},
author={Daoyuan Chen and Yilun Huang and Zhijian Ma and Hesen Chen and Xuchen Pan and Ce Ge and Dawei Gao and Yuexiang Xie and Zhaoyang Liu and Jinyang Gao and Yaliang Li and Bolin Ding and Jingren Zhou},
year={2023},
eprint={2309.02033},
archivePrefix={arXiv},
primaryClass={cs.LG}
}