metadata

license: mit
task_categories:
  - summarization
  - text2text-generation
language:
  - en
size_categories:
  - 10K<n<100K
source_datasets: tomasg25/scientific_lay_summarisation

scientific_lay_summarisation - elife - normalized

This is the "elife" split. For more words, refer to the PLOS split README

load with datasets:

from datasets import load_dataset

# If the dataset is gated/private, make sure you have run huggingface-cli login
dataset = load_dataset("pszemraj/scientific_lay_summarisation-elife-norm")
dataset

Output:

DatasetDict({
    train: Dataset({
        features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
        num_rows: 4346
    })
    test: Dataset({
        features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
        num_rows: 241
    })
    validation: Dataset({
        features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'],
        num_rows: 241
    })
})

Lengths

Train set:

Datasets:

pszemraj
/

scientific_lay_summarisation-elife-norm

scientific_lay_summarisation - elife - normalized

Contents

Lengths