Update README.md

README.md

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 # Dataset Card for "CSAT-QA"
 
+## Dataset Summary
+The field of Korean Language Processing is experiencing a surge in interest, 
+illustrated by the introduction of open-source models such as Polyglot-Ko and proprietary models like HyperClova. 
+Yet, as the development of larger and superior language models accelerates, evaluation methods aren't keeping pace. 
+Recognizing this gap, we at HAE-RAE are dedicated to creating tailored benchmarks for the rigorous evaluation of these models.
+
+CSAT-QA incorporates 936 multiple choice question answering (MCQA) questions, manually curated from 
+the Korean University entrance exam, the College Scholastic Ability Test (CSAT). For a detailed explanation of how the CSAT-QA was created 
+please check out the [accompanying blog post](https://github.com/guijinSON/hae-rae/blob/main/blog/CSAT-QA.md), 
+and for evaluation check out [LM-Eval-Harness](https://github.com/EleutherAI/lm-evaluation-harness) on github.
+
+## Evaluation Results
+
+| Category | Polyglot-Ko-12.8B | GPT-3.5-16k | GPT-4     | Human_Performance |
+|----------|----------------|-------------|-----------|-------------------|
+| WR       | 0.09           | 9.09        | 45.45     | **54.0**          |
+| GR       | 0.00           | 20.00       | 32.00     | **45.41**         |
+| LI       | 21.62          | 35.14       | **59.46** | 54.38             |
+| RCH      | 17.14          | 37.14       | **62.86** | 48.7              |
+| RCS      | 10.81          | 27.03       | **64.86** | 39.93             |
+| RCSS     | 11.9           | 30.95       | **71.43** | 44.54             |
+| Average  | 10.26          | 26.56       | **56.01** | 47.8              |
+
+![Untitled](https://github.com/guijinSON/hae-rae/blob/main/blog/assets/csat_spyder.png)
+
+## How to Use
+
+The CSAT-QA includes two subsets. The full version with 936 questions can be downloaded using the following code:
+
+```
+from datasets import load_dataset
+dataset = load_dataset("EleutherAI/CSAT-QA")
+```
+
+A more condensed version, which includes human accuracy data, can be downloaded using the following code:
+```
+from datasets import load_dataset
+import pandas as pd
+
+dataset = load_dataset("EleutherAI/CSAT-QA")
+dataset = pd.DataFrame(dataset["train"]).dropna(subset=["Category"])
+```
+
+## License
+
+The copyright of this material belongs to the Korea Institute for Curriculum and Evaluation(한국교육과정평가원) and may be used for research purposes only.
+
+
 [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)