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Ede Python library

The Ede Python library automates the generations of instruction fine-tuning datasets in low-resource languages. PyPI package coming soon. Using GPT-4, it takes 0.5-1.5s per generation (using batch processing) so ~1 day to create 100,000 generations.

Setup

The full API for this library is coming soon. For now, you can clone the repository, install requirements and run the run.py script.

pip install -r requirements.txt
python run.py

Don't forget to add your OpenAI API key to the .env file.

OPENAI_API_KEY = "your_openai_api_key"

The following parameters are editable. It is possible to run with default settings using only target_language and size.

import Ede

model={"provider": "", "model": ""} # accepts openai and anthropic as providers, although anthropic is less performant as it currently lacks a reliable JSON mode.
target_language = "" # target language e.g. Yoruba
data_dir="data" # data directory (defaults to data). Should contain input, output, schemas, and seeds folders (more info on folder structure below)
size=100 # dataset size (defaults to 100)

pipeline = Ede(
    target_language=target_language, 
    model=model, 
    data_dir=data_dir, 
    size=size, 
)

pipeline.run()

Folder structure

.
├── ...
├── data                        # Directory containing data for the project
│   ├── input                   # Input folder contains input files with column names variable_1, variable_2, ..., variable_n
|   │   ├── input_1.csv      
|   │   ├── ...              
|   │   └── input_n.csv      
│   ├── output                  # Output folder is where generated output is saved as output.csv
│   ├── prompts                 # Contains template system and user prompts in .txt format
│   ├── schemas                 # Contains schemas for input and output files (see below)
|   │   ├── input_schema.csv    
|   │   └── output_schema.csv   
│   ├── seeds                   # Contains seed tasks in .jsonl format (see below)
|   └────── seed_tasks.jsonl      
└── ...

Input schema

file_name task_category task_description variables total
yosm.csv classification sentiment analysis of movie reviews {""variable_1"": ""movie review"", ""variable_2"": ""sentiment""} 1501

Output schema

task_category task_description percent total
classification sentiment analysis of movie reviews categorize or label the provided content into predefined classes or groups 0.03

Versioning

We are keen for your feedback; please open an issue with questions, bugs, or suggestions.

Requirements

Python 3.7 or higher.