Update paper link, task categories and add sample usage

README.md

@@ -1,20 +1,23 @@
 ---
-pretty_name: SWE-CI
-license: apache-2.0
-version: 1.0.0
 language:
 - en
+license: apache-2.0
+size_categories:
+- n<1K
+task_categories:
+- text-generation
+pretty_name: SWE-CI
+version: 1.0.0
 tags:
 - code
 - agent
-size_categories:
-- n<1K
 ---
+
 # SWE-CI: Evaluating Agent Capabilities in Maintaining Codebases via Continuous Integration
 
 🔗 Github Link: [https://github.com/SKYLENAGE-AI/SWE-CI](https://github.com/SKYLENAGE-AI/SWE-CI)
 
-🔗 Paper Link: Coming soon ...
+🔗 Paper Link: [https://huggingface.co/papers/2603.03823](https://huggingface.co/papers/2603.03823)
 
 *🏆 What is SWE-CI? A Coding benchmark for continuous integration*
 
@@ -31,9 +34,30 @@ SWE-CI introduces four key shifts:
 *   **From Snapshots to Evolution**: Instead of fixing a single bug, SWE-CI focuses on the trajectory between two commits (from ‘current’ commit to ‘target’ commits). It models how codebases actually grow, refactor, and evolve over time.
 *   **From Static Descriptions to CI-Driven Requirements**: Instead of relying on human-written issue descriptions, SWE-CI uses the "Test Gap" as the primary driver. By running the Target Tests on the Current Source, we generate raw CI failure reports. In the SWE-CI framework, an Architect Agent then translates these technical logs into natural language requirements, which a Programmer Agent uses to implement the changes.
 *   **Iterative Refinement (The CI Loop)**: SWE-CI is built for a closed-loop evaluation. The model doesn't just "guess" the fix; it must interact with the environment, analyze test failures, and refine the code iteratively—exactly like a developer working against a CI pipeline.
-
 *   **From One-off Fixes to Sustainable Maintenance**: SWE-CI emphasizes the long-term maintainability of the software. It evaluates whether agents can accurately comprehend complex systems and propose the most essential requirements. It challenges agents to write readable and extensible code that facilitates future evolution cycles, rather than just "hacking" a solution that passes the current tests but hinders future growth.
 
+## Sample Usage
+
+### Installation
+```bash
+git clone https://github.com/SKYLENAGE-AI/SWE-CI.git
+cd SWE-CI
+
+conda create --name sweci python=3.11 -y
+conda activate sweci
+pip install -r requirements.txt
+```
+
+### Download the Dataset
+```bash
+# Using default parameters
+PYTHONPATH=src python -m swe_ci.download
+
+# Using custom parameters
+PYTHONPATH=src python -m swe_ci.download \
+    --splitting full \
+    --hf_token <HF_TOKEN>
+```
 
 ## Dataset description
 
@@ -55,9 +79,6 @@ SWE-CI-full contains a total of 137 tasks. Each task consists of three parts: me
 
 For more detailed information: including the data collection, cleaning, and filtering processes, the construction of environment images, and further explanations of the metadata, please refer to our paper.
 
-
-
-
 ## Licensing 
 
 This dataset is released under a multi-licensing model to account for the different sources and types of content:
@@ -71,12 +92,9 @@ The source code contained in `code.zip` consists of sampled files from various o
 *   **Ownership:** These files remain the property of their original authors. 
 *   **License:** They are governed by the **original open-source licenses** (e.g., MIT, Apache 2.0, BSD) of the respective upstream projects. 
 *   **Disclaimer:** We redistribute these code snippets for research and benchmarking purposes under the redistribution permissions granted by their original licenses.
-Content Removal
 *   **Content Removal:** We respect open-source intellectual property. If you are a copyright holder and wish to have your repository removed from this dataset, please open an Issue in the "Community" tab of this Hugging Face repository. We will process valid requests promptly.
 
-
-
 ### 3. Environment Images (`/data/**/image.tar.gz` files)
 The pre-built Docker images are provided as **Aggregate Works** for reproducibility.
 *   **Curation Work:** The logic, environment configurations (e.g., specific library versions, dependency resolution), and setup scripts integrated within these images are licensed under the **Apache License 2.0**.
-*   **Third-Party Components:** The underlying Operating System (e.g., Debian/Ubuntu), system libraries, and pre-installed packages within the images are subject to their own respective upstream licenses.
+*   **Third-Party Components:** The underlying Operating System (e.g., Debian/Ubuntu), system libraries, and pre-installed packages within the images are subject to their own respective upstream licenses.