Update README.md

README.md

@@ -54,7 +54,7 @@ task_ids:
 
 This dataset can be used to build extractive-QA and Language Models.
 
-Splts have been balanced by kind of question, and unlike other datasets like SQUAD, it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times.
+Splits have been balanced by kind of question, and unlike other datasets like SQUAD, it only contains, per record, one question and one answer for each context, although the contexts can repeat multiple times.
 
 ### Supported Tasks and Leaderboards
 Extractive-QA, Language Model.
@@ -85,7 +85,7 @@ Catalan (`ca`).
     },
 ```
 ### Data Fields
-Follows [Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250) for squad v1 datasets.
+Follows [Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250) for SQUAD v1 datasets.
 - `id` (str): Unique ID assigned to the question.
 - `title` (str): Title of the Wikipedia article.
 - `context` (str): Wikipedia section text.
@@ -99,7 +99,7 @@ Follows [Rajpurkar, Pranav et al., 2016](http://arxiv.org/abs/1606.05250) for sq
 - test.json: 2135 question/answer pairs
 ## Dataset Creation
 ### Methodology
-Aggregation anb balancing from ViquiQUAD and VilaQUAD datasets
+Aggregation and balancing from ViquiQUAD and VilaQUAD datasets.
 ### Curation Rationale
 For compatibility with similar datasets in other languages, we followed as close as possible existing curation guidelines. 
 ### Source Data
@@ -110,11 +110,11 @@ For compatibility with similar datasets in other languages, we followed as close
 [More Information Needed]
 ### Annotations
 #### Annotation process
-We comissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 ([Rajpurkar, Pranav et al. “SQuAD: 100, 000+ Questions for Machine Comprehension of Text.” EMNLP (2016)](http://arxiv.org/abs/1606.05250)).
+We commissioned the creation of 1 to 5 questions for each context, following an adaptation of the guidelines from SQUAD 1.0 ([Rajpurkar, Pranav et al. “SQuAD: 100, 000+ Questions for Machine Comprehension of Text.” EMNLP (2016)](http://arxiv.org/abs/1606.05250)).
 #### Who are the annotators?
-Annotation was commissioned to an specialized company that hired a team of native language speakers.
+Annotation was commissioned to a specialized company that hired a team of native language speakers.
 ### Personal and Sensitive Information
-No personal or sensitive information included.
+No personal or sensitive information is included.
 ## Considerations for Using the Data
 ### Social Impact of Dataset
 [More Information Needed]