Update README.md

README.md

@@ -76,6 +76,38 @@ configs:
   - split: test
     path: generate_eval/test-*
 ---
-# Dataset Card for "ECCO"
+# ECCO
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Dataset from the paper "ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?"
+
+![teaser](https://github.com/user-attachments/assets/44659b06-3676-4deb-affb-2ec5f02787f6)
+
+The dataset consists of 2 subsets `edit` and `generate` each with 3 splits (`train`, `val` and `test`).
+
+Code repository: [https://github.com/CodeEff/ECCO](https://github.com/CodeEff/ECCO)
+
+### Loading the dataset / benchmark 
+```python
+dataset = load_dataset('CodeEff/ECCO', 'edit') # For history-based editing setting
+dataset = load_dataset('CodeEff/ECCO', 'generate') # For nl-instructed generation setting
+```
+These are used to generate code by each model across the 2 paradigms. We use the `test` split for the evaluation/results and the `train` and `val` splits for finetuning and few-shot prompting.
+
+### Evaluation dataset
+The dataset also consists of an additional 3rd subset `generate_eval` which consists of the spectrum of user solutions for each problem in the `test` split.     
+This is used for the percentile evaluation of the **NL-instructed generation** paradigm.
+
+### Data Sources 
+Dataset is sourced from [IBM CodeNet](https://github.com/IBM/Project_CodeNet) which consists of primarily competetive programming solutions. 
+This is further filtered for efficiency and correctness as described in our paper. 
+
+
+### Citation 
+```
+@article{waghjale2024ecco,
+  title={ECCO: Can We Improve Model-Generated Code Efficiency Without Sacrificing Functional Correctness?},
+  author={Waghjale, Siddhant and Veerendranath, Vishruth and Wang, Zora Zhiruo and Fried, Daniel},
+  journal={arXiv preprint arXiv:2407.14044},
+  year={2024}
+}
+```