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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# LB Reranker v1.0 |
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The LB Reranker has been trained to determine the relatedness of a given query to a piece of text, therefore allowing it to be used as a ranker or reranker in various retrieval-based tasks. |
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This model is fine-tuned from a [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) model checkpoint. |
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The training data for this model can be found at [lightblue/reranker_continuous_filt_max7_train](https://huggingface.co/datasets/lightblue/reranker_continuous_filt_max7_train) and the code for generating this data as well as running the training of the model can be found on [our Github repo](https://github.com/lightblue-tech/lb-reranker). |
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Trained on data in over 95 languages, this model is applicable to a broad range of use cases. |
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# Evaluation |
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We perform an evaluation on 9 datasets from the [BEIR benchmark](https://github.com/beir-cellar/beir) that none of the evaluated models have been trained upon (to our knowledge). |
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We evaluate on a subset of all queries (the first 250) to save evaluation time. |
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We find that our model performs similarly or better than many of the state-of-the-art reranker models in our evaluation, without compromising on inference speed. |
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We make our evaluation code and results available [on our Github](https://github.com/lightblue-tech/lb-reranker/blob/main/run_bier.ipynb). |
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