Update README.md

README.md

@@ -94,23 +94,21 @@ configs:
     path: data/test-*
 ---
 # Dataset Summary
-SWE-bench Extra V2 is a dataset that can be used to train or evaluate agentic systems specializing in resolving GitHub issues. It is based on the methodology used to build SWE-bench benchmark and our previous dataset SWE-bench Extra and includes 21,000 Issue-Pull Request pairs sourced from 6,000 Python repositories. 
+SWE-rebench is a large-scale dataset designed to support the training and evaluation of LLM-based software engineering (SWE) agents. It is built using a fully automated pipeline that continuously extracts real-world GitHub tasks at scale. The dataset includes over 21,000 issue–pull request pairs from 6,000+ Python repositories, each validated for correctness through environment setup and test execution.
 
-# Dataset Description
-The SWE-bench Extra v2 dataset supports the development of software engineering agents capable of autonomously solving GitHub issues. The data collection process, based on the SWE-bench methodology, involves the following steps:
+* SWE-rebench expands on the methodology introduced in SWE-bench by adding:
 
-1. **Issue and Pull Request Collection**: Issues are gathered and linked with pull requests that successfully resolve them.
-2. **Filtering**: Instances are filtered based on attributes such as issue descriptions, relevant code paths, and test patches.
-3. **Automated extracting of project dependencies**: Extracting project dependencies using LLM.
-4. **Execution-based Validation**: The project environments are set up and tests are run to verify that they execute correctly.
+* Continuous task collection to prevent benchmark staleness
 
-For a more detailed description of the data collection process, please refer to our blog post (TBD) [Scaling data collection for training software engineering agents](https://nebius.com/blog/posts/scaling-data-collection-for-training-swe-agents).
+* Decontamination mechanisms to mitigate data leakage into pretrained LLMs
+
+* Automatic environment extraction and validation to ensure high-quality execution
 
 # How to Use
 
 ```python
 from datasets import load_dataset
-ds = load_dataset('nebius/SWE-bench-extra-v2')
+ds = load_dataset('nebius/SWE-rebench')
 ```
 
 # Dataset Statistics
@@ -149,7 +147,7 @@ The dataset contains the following fields. It includes all fields from SWE-bench
 | `requirements`             | str    | Freezed requirements for the repository.                                                       |
 | `environment`              | str    | Environment configuration for the repository.                                                  |
 
-To execute instances within SWE-bench, you need to provide a default recipe for dependency installation. The constants required for running these instances are described in this [constants.py](https://huggingface.co/datasets/nebius/SWE-bench-extra/blob/main/constants.py).
+To execute instances within SWE-rebench use this fork of SWE-bench [SWE-rebench/SWE-bench-fork](https://github.com/SWE-rebench/SWE-bench-fork)
 
 # License
 The dataset is licensed under the Creative Commons Attribution 4.0 license. However, please respect the license of each specific repository on which a particular instance is based. To facilitate this, the license of each repository at the time of the commit is provided for every instance.