MetaEval Collection of tasks for meta-learning and extreme multitask learning. We gather a large collection of classification tasks (single sentence or sentence pair) and align their format for convenient multi-task learning. We derive a standard train/validation/split for all datasets (when no test set is available, we use half of the validation set), and map all label/text keys.

A MetaEval dataset has a train/validation/test splits, a label key, and text keys that are either sentence or [sentence1,entence2]

pip install metaeval or pip install git+https://github.com/sileod/metaeval.git

from metaeval import tasks_mapping, load_and_align
tasks_mapping.head(3)

returns

dataset = load_and_align('health_fact')

returns a huggingface dataset with a unified format

DatasetDict({
    train: Dataset({
        features: ['sentence1', 'sentence2', 'label'],
        num_rows: 9832
    })
    test: Dataset({
        features: ['sentence1', 'sentence2', 'label'],
        num_rows: 1235
    })
    validation: Dataset({
        features: ['sentence1', 'sentence2', 'label'],
        num_rows: 1225
    })
})

All datasets can then be used interchangeably with standard code that can handle sentence or sentence1,sentence2 text keys.

@inproceedings{sileo2021analysis,
      title={Analysis and Prediction of NLP Models Via Task Embeddings}, 
      author={Damien Sileo and Marie-Francine Moens},
      booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
      year={2022},
}