Link to paper

README.md

@@ -1,30 +1,17 @@
 ---
-configs:
-- config_name: time_complexity_test_set.jsonl
-  data_files: "data/time_complexity_test_set.jsonl"
-  default: true
-- config_name: space_complexity_test_set.jsonl
-  data_files: "data/space_complexity_test_set.jsonl"
-- config_name: problem_and_human_solutions_list.jsonl
-  data_files: "data/problem_and_human_solutions_list.jsonl"
-- config_name: complexity_labels_full
-  data_files: "data/complexity_labels_full/*.jsonl"
-- config_name: complexity_labels_light.jsonl
-  data_files: "data/complexity_labels_light.jsonl"
-  
+language:
+- en
 license: cc-by-nc-4.0
+size_categories:
+- 100K<n<1M
 task_categories:
 - text-classification
 - question-answering
 - text-generation
 - reinforcement-learning
-language:
-- en
 tags:
 - code
 - synthetic
-size_categories:
-- 100K<n<1M
 ---
 
 <p align="center">
@@ -51,7 +38,7 @@ size_categories:
   <a href="https://huggingface.co/datasets/facebook/BigOBench">
     <img alt="HuggingFace" src="https://img.shields.io/badge/🤗%20HuggingFace-facebook/BigOBench-ffc107"/>
   </a>
-  <a href="https://arxiv.org/abs/2503.15242">
+  <a href="https://huggingface.co/papers/2503.15242">
     <img alt="ArXiv" src="https://img.shields.io/badge/arXiv-2503.15242-b5212f?logo=arxiv"/>
   </a>
 </div>
@@ -82,7 +69,7 @@ size_categories:
 
 <span style="font-variant: small-caps;"><b>BigO(Bench)</b></span> is a benchmark of ~300 code problems to be solved in Python, along with 3,105 coding problems and 1,190,250 solutions for training purposes, that evaluates whether LLMs can find the time-space complexity of code solutions or generate code solutions themselves that respect a time-space complexity requirement. This benchmark addresses the gap in current evaluations that often overlook the ability of models to comprehend and produce code constrained by computational complexity. <span style="font-variant: small-caps;"><b>BigO(Bench)</b></span> includes a complexity inference framework that can run any Python code snippet, measure multiple runtime and memory footprint values, and infer its algorithmic time-space complexity. It also includes of set of 3,105 coding problems and 1,190,250 solutions from Code Contests annotated with inferred (synthetic) time and space complexity labels from the complexity framework, as well as corresponding runtime and memory footprint values for a large set of input sizes.
 
-For more details, see our [Paper](todo), [GitHub repository](https://github.com/facebookresearch/bigobench) and [Website](todo).
+For more details, see our [Paper](https://huggingface.co/papers/2503.15242), [GitHub repository](https://github.com/facebookresearch/bigobench) and [Website](https://facebookresearch.github.io/BigOBench).
 
 
 ## 📋 Getting Started with the data