Update README.md

README.md

@@ -2,6 +2,7 @@
 license: mit
 task_categories:
 - summarization
+- text2text-generation
 language:
 - en
 size_categories:
@@ -32,16 +33,15 @@ The text in both the "article" and "summary" columns was processed to ensure tha
 The length of each text sample was calculated in terms of tokens using the T5 tokenizer. The `calculate_token_length` function was used to encode each text sample using the tokenizer and return the number of resulting tokens. The resulting token lengths were added as new columns to the dataframes.
 ## Data Format
 
-The resulting processed datasets are saved in separate directories as parquet files. The directories are named according to the dataset and split name, and each directory contains three parquet files for the train, test, and validation splits.
+The resulting processed data files are stored in Apache parquet and can be loaded using the `pandas' library or the `datasets' library from the Hugging Face transformers package. The relevant column names and data types for summarization are
 
-The datasets can be loaded using the `pandas` library or using the `datasets` library from the Hugging Face transformers package. The column names and data types are as follows: 
 - `article`: the scientific article text (string) 
 - `summary`: the lay summary text (string) 
-- `article_length`: the length of the article in terms of tokens (int) 
-- `summary_length`: the length of the summary in terms of tokens (int)
+- `article_length`: the length of the article in tokens (int) 
+- `summary_length`: the length of the summary in tokens (int)
 ## Usage
 
-To use the processed datasets, load the desired parquet file(s) using `pandas` or `datasets`. Here is an example using `pandas`:
+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