
# 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)

task_categories:summarization task_categories:text2text-generation size_categories:10K<n<100K source_datasets:tomasg25/scientific_lay_summarisation language:en license:mit region:us
{"language": ["en"], "license": "mit", "size_categories": ["10K<n<100K"], "source_datasets": "tomasg25/scientific_lay_summarisation", "task_categories": ["summarization", "text2text-generation"]}
TAGS
#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #region-us 


# scientific_lay_summarisation - elife - normalized

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

## Contents 

load with datasets:


Output:




## Lengths 

Train set:

!t5-tokens

# scientific_lay_summarisation - elife - normalized

This is the "_elife_" split. For more words, refer to the PLOS split README ## Contents 

load with datasets:


Output: ## Lengths 

Train set:

!t5-tokens
TAGS
#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #region-us 
 # scientific_lay_summarisation - elife - normalized

This is the "_elife_" split. For more words, refer to the PLOS split README ## Contents 

load with datasets:


Output: ## Lengths 

Train set:

!t5-tokens
passage: TAGS
#task_categories-summarization #task_categories-text2text-generation #size_categories-10K<n<100K #source_datasets-tomasg25/scientific_lay_summarisation #language-English #license-mit #region-us 
# scientific_lay_summarisation - elife - normalized

This is the "_elife_" split. For more words, refer to the PLOS split README## Contents 

load with datasets:


Output:## Lengths 

Train set:

!t5-tokens