Update README.md

README.md

@@ -45,12 +45,6 @@ A professionally annotated dataset released by [Chattermill AI](https://chatterm
 
 Two annotators possess extensive experience in developing human-labeled ABSA datasets for commercial companies, while the third annotator holds a PhD in computational linguistics.
 
-There has been a lack of high-quality ABSA datasets covering broad domains and addressing real-world applications. Academic progress has been confined to benchmarking on domain-specific, toy datasets such as restaurants and laptops, which are limited in size (e.g., [SemEval Task ABSA](https://aclanthology.org/S16-1002.pdf) or [SentiHood](https://aclanthology.org/C16-1146/)).
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-This dataset is part of the [FABSA paper](https://www.sciencedirect.com/science/article/pii/S0925231223009906), and we release it hoping to advance academic progress as tools for ingesting and analyzing customer feedback at scale improve significantly, yet evaluation datasets continue to lag. FABSA is a new, large-scale, multi-domain ABSA dataset of feedback reviews, consisting of approximately 10,500 reviews spanning 10 domains (Fashion, Consulting, Travel Booking, Ride-hailing, Banking, Trading, Streaming, Price Comparison, Information Technology, and Groceries).
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 ## Task
 
 This dataset encompasses **Aspect Category Sentiment Analysis** and is suitable for both **Aspect Category Detection** (ACD) and **Aspect Category Sentiment Classification** (ACSC). ACD in sentiment analysis identifies aspect categories mentioned in a sentence. These categories are conceptual; they may not explicitly appear in the review and are chosen from a predefined list of Aspect Categories. ACSC classifies the sentiment polarities of these conceptual aspect categories.
@@ -85,6 +79,14 @@ Labels: [Company brand: General satisfaction, positive), (Staff support: Attitud
 ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/608995d619137b3a6ba76113/Lr60Oc6NGalkOEj-_Gtzk.jpeg)
 
 
+## Release
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+There has been a lack of high-quality ABSA datasets covering broad domains and addressing real-world applications. Academic progress has been confined to benchmarking on domain-specific, toy datasets such as restaurants and laptops, which are limited in size (e.g., [SemEval Task ABSA](https://aclanthology.org/S16-1002.pdf) or [SentiHood](https://aclanthology.org/C16-1146/)).
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+This dataset is part of the [FABSA paper](https://www.sciencedirect.com/science/article/pii/S0925231223009906), and we release it hoping to advance academic progress as tools for ingesting and analyzing customer feedback at scale improve significantly, yet evaluation datasets continue to lag. FABSA is a new, large-scale, multi-domain ABSA dataset of feedback reviews, consisting of approximately 10,500 reviews spanning 10 domains (Fashion, Consulting, Travel Booking, Ride-hailing, Banking, Trading, Streaming, Price Comparison, Information Technology, and Groceries).
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 ## Citation
 ```
 @article{KONTONATSIOS2023126867,