Update README.md

README.md

@@ -31,36 +31,9 @@ task_ids:
 
 ### Dataset Description
 
-The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. The dataset is split into two groups:
+The Twitter Financial News dataset is an English-language dataset containing an annotated corpus of finance-related tweets. This dataset is used to classify finance-related tweets for their sentiment.
 
-1. Topic classification: 21,107 documents annotated with 20 labels:
-
-```python
-topics = {
-    "LABEL_0": "Analyst Update",
-    "LABEL_1": "Fed | Central Banks",
-    "LABEL_2": "Company | Product News",
-    "LABEL_3": "Treasuries | Corporate Debt",
-    "LABEL_4": "Dividend",
-    "LABEL_5": "Earnings",
-    "LABEL_6": "Energy | Oil",
-    "LABEL_7": "Financials",
-    "LABEL_8": "Currencies",
-    "LABEL_9": "General News | Opinion",
-    "LABEL_10": "Gold | Metals | Materials",
-    "LABEL_11": "IPO",
-    "LABEL_12": "Legal | Regulation",
-    "LABEL_13": "M&A | Investments",
-    "LABEL_14": "Macro",
-    "LABEL_15": "Markets",
-    "LABEL_16": "Politics",
-    "LABEL_17": "Personnel Change",
-    "LABEL_18": "Stock Commentary",
-    "LABEL_19": "Stock Movement",
-}
-```
-
-2. Sentiment analysis: 11,932 documents annotated with 3 labels:
+1. The dataset holds 11,932 documents annotated with 3 labels:
 
 ```python
 sentiments = {
@@ -72,7 +45,7 @@ sentiments = {
 
 The data was collected using the Twitter API. The current dataset supports the multi-class classification task.
 
-### Task 1: Sentiment Analysis
+### Task: Sentiment Analysis
 
 # Data Splits
 There are 2 splits: train and validation. Below are the statistics: