Update README.md

README.md

@@ -6,11 +6,17 @@ language:
 - en
 size_categories:
 - 10K<n<100K
+source_datasets: tomasg25/scientific_lay_summarisation
 ---
 
 # scientific_lay_summarisation - PLOS - normalized
 
-This dataset contains scientific lay summaries which have been preprocessed [with this code](https://gist.github.com/pszemraj/bd344637af7c0c10ecf4ab62c4d0ce91). The preprocessing includes fixing punctuation and whitespace issues, and calculating the token length of each text sample using a tokenizer from the T5 model.
+This dataset is a modified version of [tomasg25/scientific_lay_summarization](https://huggingface.co/datasets/tomasg25/scientific_lay_summarisation) and contains scientific lay summaries that have been preprocessed [with this code](https://gist.github.com/pszemraj/bd344637af7c0c10ecf4ab62c4d0ce91). The preprocessing includes fixing punctuation and whitespace problems, and calculating the token length of each text sample using a tokenizer from the T5 model.
+
+Original dataset details:
+- **Repository:** https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
+- **Paper:** [Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature](https://arxiv.org/abs/2210.09932)
+
 ## Data Cleaning
 
 The text in both the "article" and "summary" columns was processed to ensure that punctuation and whitespace were consistent. The `fix_punct_whitespace` function was applied to each text sample to:
@@ -38,14 +44,12 @@ The datasets can be loaded using the `pandas` library or using the `datasets` li
 To use the processed datasets, load the desired parquet file(s) using `pandas` or `datasets`. Here is an example using `pandas`:
 
 ```python
-
+# download the dataset files by clicking on 'use in datasets' and cloning 
 import pandas as pd
 
-# Load a parquet file
+# Load train set
 df = pd.read_parquet("scientific_lay_summarisation-plos-norm/train.parquet")
-
-# Print the first few rows
-print(df.head())
+print(df.info())
 ```
 
 
@@ -53,11 +57,10 @@ print(df.head())
 And here is an example using `datasets`:
 
 ```python
-
 from datasets import load_dataset
 
 dataset = load_dataset("pszemraj/scientific_lay_summarisation-plos-norm")
-
+train_set = dataset['train']
 # Print the first few samples
 for i in range(5):
     print(dataset[i])