| language: | |
| - en | |
| license: apache-2.0 | |
| size_categories: | |
| - 1M<n<10M | |
| task_categories: | |
| - text-retrieval | |
| - feature-extraction | |
| # F2LLM Dataset | |
| [Paper](https://huggingface.co/papers/2510.02294) | [Code](https://github.com/codefuse-ai/CodeFuse-Embeddings/tree/main/F2LLM) | |
| 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} | |
| } | |
| ``` |