Update README.md

README.md

@@ -42,11 +42,21 @@ illustrated by the introduction of open-source models such as Polyglot-Ko and pr
 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 
+CSAT-QA is a comprehensive collection of 936 multiple choice question answering (MCQA) questions, 
+manually collected the College Scholastic Ability Test (CSAT), a rigorous Korean University entrance exam. 
+The CSAT-QA is divided into two subsets: a complete version encompassing all 936 questions,
+and a smaller, specialized version used for targeted evaluations.
+
+The smaller subset further diversifies into six distinct categories: 
+Writing (WR), Grammar (GR), Reading Comprehension: Science (RCS), Reading Comprehension: Social Science (RCSS), 
+Reading Comprehension: Humanities (RCH), and Literature (LI). Moreover, the smaller subset includes the recorded accuracy of South Korean students, 
+providing a valuable real-world performance benchmark. 
+
+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 |
@@ -66,7 +76,7 @@ The CSAT-QA includes two subsets. The full version with 936 questions can be dow
 
 ```
 from datasets import load_dataset
-dataset = load_dataset("EleutherAI/CSAT-QA",split="full")
+dataset = load_dataset("EleutherAI/CSAT-QA", "full")
 ```
 
 A more condensed version, which includes human accuracy data, can be downloaded using the following code:
@@ -74,8 +84,32 @@ A more condensed version, which includes human accuracy data, can be downloaded
 from datasets import load_dataset
 import pandas as pd
 
-dataset = load_dataset("EleutherAI/CSAT-QA",split="full")
-dataset = pd.DataFrame(dataset["train"]).dropna(subset=["Category"])
+dataset = load_dataset("EleutherAI/CSAT-QA", "GR") # Choose from either WR, GR, LI, RCH, RCS, RCSS, 
+
+```
+
+## Evaluate using LM-Eval-Harness
+To evaluate your model simply by using the LM-Eval-Harness by EleutherAI follow the steps below.
+
+1. To install lm-eval from the github repository main branch, run:
+```
+git clone https://github.com/EleutherAI/lm-evaluation-harness
+cd lm-evaluation-harness
+pip install -e .
+```
+
+2. To install additional multilingual tokenization and text segmentation packages, you must install the package with the multilingual extra:
+```
+pip install -e ".[multilingual]"
+```
+
+3. Run the evaluation by:
+```
+python main.py \
+    --model hf-causal \
+    --model_args pretrained=EleutherAI/polyglot-ko-1.3b \
+    --tasks csatqa_wr,csatqa_gr,csatqa_rcs,csatqa_rcss,csatqa_rch,csatqa_li \
+    --device cuda:0
 ```
 
 ## License