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--- |
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license: apache-2.0 |
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pipeline_tag: text-ranking |
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library_name: lightning-ir |
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base_model: |
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- webis/tite-2-late |
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tags: |
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- bi-encoder |
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--- |
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# TITE: Token-Independent Text Encoder |
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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. |
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We provide the following pre-trained models encoder models: |
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- [webis/tite-2-late](https://huggingface.co/webis/tite-2-late) |
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- [webis/tite-2-late-upscale](https://huggingface.co/webis/tite-2-late-upscale) |
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We provide the following fine-tuned bi-encoder models for text ranking: |
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| Model | TREC DL 19 | TREC DL 20 | BEIR (geometric mean) | |
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|-------|------------|------------|-----------------------| |
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| [`webis/tite-2-late-msmarco`](https://huggingface.co/webis/tite-2-late-msmarco) | 0.69 | 0.71 | 0.40 | |
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| [`webis/tite-2-late-upscale-msmarco`](https://huggingface.co/webis/tite-2-late-upscale-msmarco) | 0.68 | 0.71 | 0.41 | |
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## Usage |
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See the [repository](https://github.com/webis-de/tite) for more information on how to use the or reproduce the model. |
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## Citation |
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If you use this code or the models in your research, please cite our paper: |
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```bibtex |
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@InProceedings{schlatt:2025, |
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author = {Ferdinand Schlatt and Tim Hagen and Martin Potthast and Matthias Hagen}, |
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booktitle = {48th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2025)}, |
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doi = {10.1145/3726302.3730094}, |
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month = jul, |
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pages = {2493--2503}, |
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publisher = {ACM}, |
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site = {Padua, Italy}, |
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title = {{TITE: Token-Independent Text Encoder for Information Retrieval}}, |
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year = 2025 |
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} |
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``` |
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