---
license: apache-2.0
---

# TITE: Token-Independent Text Encoder

This model is presented in the paper [TITE: Token-Independent Text Encoder for Information Retrieval](https://dl.acm.org/doi/10.1145/3726302.3730094). It's an efficient bi-encoder model for creating embeddings for queries and documents.


We provide the following pre-trained models encoder models:

- [webis/tite-2-late](https://huggingface.co/webis/tite-2-late)
- [webis/tite-2-late-upscale](https://huggingface.co/webis/tite-2-late-upscale)

We provide the following fine-tuned bi-encoder models for text ranking:

| Model | TREC DL 19 | TREC DL 20 | BEIR (geometric mean) |
|-------|------------|------------|-----------------------|
| [`webis/tite-2-late-msmarco`](https://huggingface.co/webis/tite-2-late-msmarco) | 0.69 | 0.71 | 0.40 |
| [`webis/tite-2-late-upscale-msmarco`](https://huggingface.co/webis/tite-2-late-upscale-msmarco) | 0.68 | 0.71 | 0.41 |

## Usage

See the [repository](https://github.com/webis-de/tite) for more information on how to use the or reproduce the model.

## Citation

If you use this code or the models in your research, please cite our paper:

```bibtex
@InProceedings{schlatt:2025,
  author =                      {Ferdinand Schlatt and Tim Hagen and Martin Potthast and Matthias Hagen},
  booktitle =                   {48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025)},
  doi =                         {10.1145/3726302.3730094},
  month =                       jul,
  pages =                       {2493--2503},
  publisher =                   {ACM},
  site =                        {Padua, Italy},
  title =                       {{TITE: Token-Independent Text Encoder for Information Retrieval}},
  year =                        2025
}
```