update readme file

README.md

@@ -50,7 +50,7 @@ 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 **2023** 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** 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.
 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.
@@ -95,7 +95,7 @@ The features we extract for each repository are illustrated in the example below
 - **retrieval_date**: date when the repo was scraped from GitHub
 
 We start by retrieving repositories with more than **900** stars using **two-month tumbling windows**. If we hit the **1000** repository limit per window (for a personal GitHub account), we shorten the
-search space to a **one-month window** and restart the iteration. Otherwise, the window advances by two months. Once the 2001-2023 timeframe is covered, we reduce the star search space: between **900** and **100** stars, we decrease the interval by **50** (e.g. search between [900, 850]), between **100** and **10** stars, we decrease the interval by **10**, and for the last **10** stars, we decrease by **1**. Figure 1 showcases the distribution of repositories with up to **500** stars. Since most repositories fall within the **0-100 star range**, using the **creation date** and **star count** filters helps us avoid API limits and scrape more data by narrowing the search space. 
+search space to a **one-month window** and restart the iteration. Otherwise, the window advances by two months. Once the 2001-2024 timeframe is covered, we reduce the star search space: between **900** and **100** stars, we decrease the interval by **50** (e.g. search between [900, 850]), between **100** and **10** stars, we decrease the interval by **10**, and for the last **10** stars, we decrease by **1**. Figure 1 showcases the distribution of repositories with up to **500** stars. Since most repositories fall within the **0-100 star range**, using the **creation date** and **star count** filters helps us avoid API limits and scrape more data by narrowing the search space. 
 Although the creation date window can be reduced even further (week or day level), a one-month window was enough for our needs. After retrieving the repositories, we extract all the files with a
 *.java* extension. 
 
@@ -177,7 +177,7 @@ computational efficiency, crucial for handling the **Java-Stack v2’s size** of
 Lastly, we use a **Jaccard similarity threshold** of **0.7**, which proved to be efficient for both [SantaCoder](https://arxiv.org/abs/2301.03988) and [StarCoder](https://arxiv.org/abs/2305.06161) models. Such a high threshold
 reduces false positives, leading to fewer unnecessary comparisons and lower computational overhead. 
 
-Instead of removing near-duplicates, we introduce a new feature to our dataset, called *near_dups_stkv2_idx*. This feature includes the IDs of the near-duplicate files from the Java-Stack v2 corresponding to the current file in our dataset.
+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. 
@@ -195,6 +195,11 @@ This implies that training any LLM on Stack v2 will breach copy-left code licens
 
 # Usage 
 
+By default, the dataset includes near-duplicate entries from Java-Stack v2, with their IDs listed in the *near_dups_stkv2_idx* field. 
+*An entry with an empty list in this field indicates that no near-duplicate files were found in Java-Stack v2 for that specific file.* 
+
+Near-duplicates can be removed as shown in the example below.
+
 ```python
 from datasets import load_dataset
 
@@ -209,6 +214,10 @@ data = load_dataset("LaughingLogits/Stackless_Java_V2", split="train", streaming
 for sample in iter(data):
     print(sample["content"])
 
+# Filter dataset to not include near-duplicates from Java-Stack v2
+dataset = load_dataset("LaughingLogits/Stackless_Java_V2", split="train")
+near_deduplicated_dataset = dataset.filter(lambda sample: len(sample["near_dups_stkv2_idx"]) == 0)
+
 ```