| --- |
| dataset_info: |
| features: |
| - name: query |
| dtype: string |
| - name: positive |
| dtype: string |
| - name: negative |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 2766980301 |
| num_examples: 1391986 |
| download_size: 1589194354 |
| dataset_size: 2766980301 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| --- |
| |
| Training Data For Paper: ["Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?"](https://arxiv.org/abs/2409.02727) |
|
|
| Citation: |
| ``` |
| @misc{poolingattentioneffectivedesigns, |
| title={Pooling And Attention: What Are Effective Designs For LLM-Based Embedding Models?}, |
| author={Yixuan Tang and Yi Yang}, |
| year={2024}, |
| eprint={2409.02727}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2409.02727}, |
| } |
| |
| ``` |
|
|
