Preprocessings to prepare datasets for a task
Project description
tasksource: 500+ dataset harmonization preprocessings with structured annotations for frictionless extreme multi-task learning and evaluation
Huggingface Datasets is a great library, but it lacks standardization, and datasets require preprocessing work to be used interchangeably.
tasksource
automates this and facilitates multi-task learning scaling and reproducibility.
Each dataset is standardized to either MultipleChoice
, Classification
, or TokenClassification
dataset with identical fields. We do not support generation tasks as they are addressed by promptsource. All implemented preprocessings are in tasks.py or tasks.md. A preprocessing is a function that accepts a dataset and returns the standardized dataset. Preprocessing code is concise and human-readable.
Installation and usage:
pip install tasksource
Get the task index and iterate over harmonized tasks:
from tasksource import list_tasks, load_task
df = list_tasks()
for id in df[df.task_type=="MultipleChoice"].id:
dataset = load_task(id)
# all yielded datasets can be used interchangeably
See supported 500+ tasks in tasks.md (+200 MultipleChoice tasks, +200 Classification tasks). Feel free to request a new task.
Pretrained model:
I pretrained models on tasksource and obtained state-of-the-art results: https://hf.co/sileod/deberta-v3-base-tasksource-nli
Contact and citation
I can help you integrate tasksource in your experiments. damien.sileo@inria.fr
More details on this article:
@article{sileo2023tasksource,
title={tasksource: Structured Dataset Preprocessing Annotations for Frictionless Extreme Multi-Task Learning and Evaluation},
author={Sileo, Damien},
url= {https://arxiv.org/abs/2301.05948},
journal={arXiv preprint arXiv:2301.05948},
year={2023}
}
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for tasksource-0.0.12-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d29c8bdda9906aecc27b104ff2d6f4cd088e1cdd057ec62c7a11188f85d5d32e |
|
MD5 | 5cb8cfbd97e5cfa25944c3b3cb74778e |
|
BLAKE2b-256 | 059ada3e2aa4c0270aefc1c7e51a3f7284e5486cea1ead33ecb888f6807be72d |