Update README.md

README.md

@@ -1,3 +1,48 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+language:
+- en
+size_categories:
+- 1M<n<10M
+---
+
+The F2LLM dataset includes 6 million query-document-negative tuples curated solely from open-source, non-synthetic data, serving as a strong, budget-friendly baseline for training embedding models.
+
+## Data Format
+
+Data are compiled into three categories: retrieval, classification, and clustering. Each retrieval and clustering data sample is accompanied by 24 hard negatives. Each classification data sample is accompanied by 1 hard negative.
+
+The data fields are:
+```json
+{
+  "query": ...
+  "passage": ...
+  "negative_1": ...
+  ...
+  "negative_n": ...
+}
+```
+
+For more details, please refer to our [technical report](https://arxiv.org/abs/2510.02294).
+
+## Usage
+
+Code for training embedding models on the F2LLM data is available in our [Github repo](https://github.com/codefuse-ai/CodeFuse-Embeddings/tree/main/F2LLM).
+
+## Citation
+
+If you use the F2LLM models, data, or code, please cite the following technical report.
+
+```
+@article{2025F2LLM,
+  title={F2LLM Technical Report: Matching SOTA Embedding Performance with 6 Million Open-Source Data}, 
+  author={Ziyin Zhang and Zihan Liao and Hang Yu and Peng Di and Rui Wang},
+  journal      = {CoRR},
+  volume       = {abs/2510.02294},
+  year         = {2025},
+  url          = {https://doi.org/10.48550/arXiv.2510.02294},
+  doi          = {10.48550/ARXIV.2510.02294},
+  eprinttype    = {arXiv},
+  eprint       = {2510.02294}
+}
+```