Update README.md

README.md

@@ -21,13 +21,11 @@ size_categories:
 
 ## Dataset Summary
 
-The FinSTS Golden Dataset is designed for financial semantic textual similarity tasks. It contains a development set of 2001 sentence pairs and a test set of 1999 sentence pairs, annotated to reflect the degree of semantic similarity between financial texts. This dataset is ideal for training and evaluating models in financial text analysis, clustering, and semantic search.
+The FinSTS Golden Dataset is designed for financial semantic textual similarity tasks. It contains a development set of 2001 sentence pairs and a test set of 1999 sentence pairs, annotated to reflect the degree of semantic similarity between financial texts. This dataset is ideal for evaluating models in financial text analysis.
 
 ## Supported Tasks and Leaderboards
 
-- **Text Similarity**: This dataset can be used to train models to measure the semantic similarity between pairs of financial sentences.
-- **Clustering**: The dataset can be utilized to cluster sentences based on their semantic content.
-- **Semantic Search**: It is useful for creating models that improve search results by understanding the semantic meaning of queries and documents.
+- **Text Similarity**: This dataset can be used to evaluate models measuring the semantic similarity between pairs of financial sentences.
 
 ## Languages
 
@@ -37,15 +35,18 @@ The dataset is in English.
 
 ### Data Fields
 
+- `split`: Indicates whether the data point belongs to the development or test set.
 - `sentence1`: The first sentence in the pair.
 - `sentence2`: The second sentence in the pair.
-- `similarity_score`: A score between 0 and 5 indicating the degree of semantic similarity between the two sentences.
-
+- `gpt_score`: An integer score between 0 and 5 indicating the degree of semantic similarity between the two sentences, annotated by GPT-4 Turbo API.
+- `score`: An integer score between 0 and 5 indicating the degree of semantic similarity between the two sentences, annotated by 4 human experts.
 ### Example
 
 ```json
 {
-  "sentence1": "The company's revenue grew significantly.",
-  "sentence2": "The firm experienced a substantial increase in sales.",
-  "similarity_score": 4.5
+  "split": "dev",
+  "sentence1": "Unlike many industrial companies, substantially all of our assets and virtually all of our liabilities are monetary in nature.",
+  "sentence2": "Unlike most industrial companies, virtually all of our assets and liabilities are monetary in nature.",
+  "gpt_score": 5,
+  "score": 4
 }