Updated model card

README.md

@@ -17,9 +17,9 @@ task_ids:
 - multi-class-classification
 - sentiment-classification
 paperswithcode_id: null
-pretty_name: Auditor_Review
+pretty_name: Auditor_Sentiment
 ---
-# Dataset Card for [Dataset Name]
+# Dataset Card for Auditor Sentiment
 
 ## Table of Contents
 - [Table of Contents](#table-of-contents)
@@ -45,13 +45,13 @@ pretty_name: Auditor_Review
   - [Licensing Information](#licensing-information)
 
 ## Dataset Description
-Auditor review data collected by News Department
+Auditor review sentiment collected by News Department
 
 - **Point of Contact:**
 Talked to COE for Auditing, currently sue@demo.org
 ### Dataset Summary
 
-Auditor sentiment dataset of sentences from financial news. The dataset consists of *** sentences from English language financial news categorized by sentiment. The dataset is divided by agreement rate of 5-8 annotators.
+Auditor sentiment dataset of sentences from financial news. The dataset consists of several thousand sentences from English language financial news categorized by sentiment. 
 
 ### Supported Tasks and Leaderboards
 
@@ -83,7 +83,7 @@ A train/test split was created randomly with a 75/25 split
 
 ### Curation Rationale
 
-To gather our auditor evaluations into one dataset. Previous attempts using off the shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.
+To gather our auditor evaluations into one dataset. Previous attempts using off-the-shelf sentiment had only 70% F1, this dataset was an attempt to improve upon that performance.
 
 ### Source Data
 
@@ -101,7 +101,7 @@ The source data was written by various auditors.
 
 This release of the auditor reviews covers a collection of 4840
 sentences. The selected collection of phrases was annotated by 16 people with
-adequate background knowledge on financial markets.  The subset here is where interannotation agreement was greater than 75%.
+adequate background knowledge on financial markets.  The subset here is where inter-annotation agreement was greater than 75%.
 
 #### Who are the annotators?
 
@@ -122,16 +122,6 @@ There is no personal or sensitive information in this dataset.
 All annotators were from the same institution and so interannotator agreement
 should be understood with this taken into account.
 
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
 ### Licensing Information
 
 License: Demo.Org Proprietary - DO NOT SHARE