Update README.md

README.md

@@ -12,20 +12,19 @@ pretty_name: AsyLex
 
 The dataset introduces 59,112 documents of refugee status determination in Canada from 1996 to 2022, providing researchers and practitioners with essential material for training and evaluating NLP models for legal research and case review.
 
-The dataset contains labeled data suited for two NLP tasks: (1) Entity extraction and (2) Legal Judgment Prediction.
+AsyLex contains labeled data suited for two NLP tasks: (1) Entity extraction and (2) Legal Judgment Prediction.
 
 ## Dataset Details
 
-The dataset includes gold-standard human-labeled annotations for 24 legally relevant entity types curated with the help of legal experts, and 1,682 gold-standard labeled documents for the outcome of the case.
+AsyLex includes gold-standard human-labeled annotations for 24 legally relevant entity types curated with the help of legal experts, and 1,682 gold-standard labeled documents for the outcome of the case.
 
 The dataset can be split in two sets: 
 - (1) a Case Covers set that consists of semi-structured data and displays meta-information (the first page of each case);
 - (2) a Main Text set that contains the body of each case, in full text.
 
-### Dataset Sources [optional]
+### Dataset Sources
 
-The documents have been collected from the online services of the Canadian
-Legal Information Institute (CanLII). 
+The documents have been collected from the online services of the Canadian Legal Information Institute (CanLII). 
 
 ## Uses
 
@@ -38,7 +37,7 @@ The dataset must be used for research purposes only. It must not be use for comm
 
 This dataset contains the following files: 
 
-| file | description |
+| Files | Description |
 | ------------- | ------------- |
 | cases_anonymized_txt_raw.tar.gz | contains the raw text from all documents, by case, with the corresponding case identifier |
 | all_sentences_anonymized.tar.xz | contains the raw text from all retrieved documents, split by sentences, with the corresponding case identifier |