Update README.md

README.md

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-license: apache-2.0
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+---
+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
+}
+```