Update README.md

README.md

@@ -8,6 +8,8 @@ dataset_info:
     dtype: string
   - name: tables
     dtype: sequence
+    feature:
+      dtype: string
   - name: topic
     dtype: string
   - name: python_solution
@@ -15,19 +17,16 @@ dataset_info:
   - name: ground_truth
     dtype: string
 ---
-## Dataset Description
+## KnowledgeMath Benchmark Description
 
 **KnowledgeMath** is a knowledge-intensive dataset focused on mathematical reasoning within the domain of finance. It requires the model to comprehend specialized financial terminology and to interpret tabular data presented in the questions. 
 **KnowledgeMath** includes **1200 QA examples** across 7 key areas in finance. These examples were collected from financial experts and feature detailed solution annotations in Python format.
 
-## Dataset Information
-
 - Paper: https://arxiv.org/abs/2311.09797
 - Code: https://github.com/yale-nlp/KnowledgeMath
 - Leaderboard: will be released soon!
 
-### Data Downloading and Usage
-
+## KnowledgeMath Dataset Information
 All the data examples were divided into two subsets: *validation* and *test*.
 
 - **validation**: 200 examples used for model development, validation, or for those with limited computing resources.
@@ -39,11 +38,7 @@ You can download this dataset by the following command:
 from datasets import load_dataset
 
 dataset = load_dataset("yale-nlp/KnowledgeMath")
-```
 
-Here are some examples of how to access the downloaded dataset:
-
-```python
 # print the first example on the validation set
 print(dataset["validation"][0])
 
@@ -51,8 +46,6 @@ print(dataset["validation"][0])
 print(dataset["test"][0])
 ```
 
-### Data Format
-
 The dataset is provided in json format and contains the following attributes:
 
 ```json
@@ -62,11 +55,12 @@ The dataset is provided in json format and contains the following attributes:
     "tables": [list] List of Markdown-format tables associated with the question, 
     "python_solution": [string] Python-format and executable solution by financial experts. The code is written in a clear and executable format, with well-named variables and a detailed explanation,
     "ground_truth": [integer] Executed result of `python solution`, rounded to three decimal places,
-    "topic": [string] The related financial area of the question
+    "topic": [string] The related financial area of the question,
+    "knowledge_terms": [list] List of knowledge terms in our constructed knowledge bank that is necessary to answer the given question. We will release this feature upon paper publication
 }
 ```
 
-### Automated Evaluation
+## Automated Evaluation
 
 To automatically evaluate a model on **KnowledgeMath**, please refer to our GitHub repository [here](https://github.com/yale-nlp/KnowledgeMath).