Update README.md

README.md

@@ -22,4 +22,42 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: mit
+task_categories:
+- question-answering
+language:
+- en
+tags:
+- code
+pretty_name: SciCode
+size_categories:
+- 1K<n<10K
 ---
+# Dataset Card for Dataset Name
+
+Official Description (from the authors): 
+Since language models (LMs) now outperform average humans on many challenging tasks, 
+it has become increasingly difficult to develop challenging, high-quality, and realistic evaluations. 
+We address this issue by examining LMs' capabilities to generate code for solving real scientific research problems. 
+Incorporating input from scientists and AI researchers in 16 diverse natural science sub-fields, 
+including mathematics, physics, chemistry, biology, and materials science, we created a scientist-curated coding benchmark, 
+SciCode. The problems in SciCode naturally factorize into multiple subproblems, each involving knowledge recall, reasoning, 
+and code synthesis. In total, SciCode contains 338 subproblems decomposed from 80 challenging main problems. 
+It offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions 
+and test cases for evaluation. Claude3.5-Sonnet, the best-performing model among those tested, 
+can solve only 4.6% of the problems in the most realistic setting. We believe that SciCode demonstrates both contemporary LMs' 
+progress towards becoming helpful scientific assistants and sheds light on the development and evaluation of scientific AI in the future.
+
+## Dataset Details
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** [https://github.com/scicode-bench/SciCode?tab=readme-ov-file]
+- **Paper [optional]:** [https://arxiv.org/abs/2407.13168]
+
+## Dataset Card Authors
+
+The original authors of SciCode benchmark and Akshath Mangudi for 
+providing the ground truth artifact.