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--- |
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license: mit |
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task_categories: |
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- summarization |
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- text2text-generation |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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source_datasets: tomasg25/scientific_lay_summarisation |
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--- |
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# scientific_lay_summarisation - elife - normalized |
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This is the "_elife_" split. For more words, refer to the [PLOS split README](https://huggingface.co/datasets/pszemraj/scientific_lay_summarisation-plos-norm) |
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## Contents |
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load with datasets: |
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```python |
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from datasets import load_dataset |
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# If the dataset is gated/private, make sure you have run huggingface-cli login |
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dataset = load_dataset("pszemraj/scientific_lay_summarisation-elife-norm") |
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dataset |
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``` |
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Output: |
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```python |
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DatasetDict({ |
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train: Dataset({ |
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features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'], |
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num_rows: 4346 |
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}) |
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test: Dataset({ |
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features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'], |
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num_rows: 241 |
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}) |
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validation: Dataset({ |
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features: ['article', 'summary', 'section_headings', 'keywords', 'year', 'title', 'article_length', 'summary_length'], |
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num_rows: 241 |
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}) |
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}) |
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``` |
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## Lengths |
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Train set: |
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