task_categories:summarization task_categories:text2text-generation size_categories:10K<n<100K source_datasets:tomasg25/scientific_lay_summarisation language:en license:mit region:us
---
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](https://huggingface.co/datasets/pszemraj/scientific_lay_summarisation-plos-norm)

## Contents 

load with datasets:

```python
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:

```python
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:

![t5-tokens](https://i.imgur.com/8BQrbgs.png)
