Update README.md

README.md

@@ -91,7 +91,7 @@ We create a new Java dataset by scraping public repositories on GitHub. Our file
 # Collection
 
 We start the collection process by scraping **10500** public repositories using the [GitHub API](https://docs.github.com/en/rest/search/search?apiVersion=2022-11-28). We specifically look for repositories released under a strong copyleft license such as **GPL-2.0**, **GPL-3.0**, or **AGPL-3.0**. We use copyleft licenses to ensure our dataset is not contaminated with training data from Stack v2. This issue occurred with other publicly available file-level code datasets, including Stack v1, which claimed to contain only permissively licensed code, however, they were [contaminated with copyleft-licensed code](https://dl.acm.org/doi/10.1145/3650105.3652298). Stack v2 also [claims to exclude copyleft-licensed code](https://arxiv.org/abs/2402.19173) due to community stance uncertainty and its low volume. Nevertheless, we still deduplicated our dataset against Stack v2 to ensure there was no overlap and that our data was safe for training. 
-We extract repositories **created** between **2001** and **April** **2024** in **decreasing order** of their **star counts**. To avoid **GitHub rate limits**, we use **timeouts** and **pagination** to fetch the repositories.
+We extract repositories **created** up until **April** **2024** in **decreasing order** of their **star counts**. To avoid **GitHub rate limits**, we use **timeouts** and **pagination** to fetch the repositories.
 The search is based on the **repository license type**, **star count**, and **creation date**. 
 
 The features we extract for each repository are illustrated in the example below.
@@ -221,7 +221,7 @@ reduces false positives, leading to fewer unnecessary comparisons and lower comp
 Instead of removing near-duplicates, we introduce a new feature to our dataset, called *near_dups_stkv2_idx*. This feature is a list of IDs of the near-duplicate files from the Java-Stack v2 corresponding to the current file in our dataset.
 The table below shows the number of files removed by each preprocessing method and the final number of files we are left with in the end (excluding near-duplicates). 
 Starting with **7.8 M** files, we are left with about **2.13 M** after applying all pre-processing methods (this includes near-duplicates). 
-Of the removed files, approximately **5.63 M** are exact duplicates (including about **0.87 M** from Java-Stack v2) and **0.8 M** are near-duplicates from Java-Stack v2. 
+Of the removed files, approximately **5.63 M** are exact duplicates (including about **0.87 M** from Java-Stack v2), and **0.8 M** are near-duplicates from Java-Stack v2. 
 This implies that training any LLM on Stack v2 will breach copy-left code licenses, despite the dataset creators’ claim that files under such licenses were removed.
 
 ### Files removed by each pre-processing method