Update README.md

README.md

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 A flax-based MonoBERT cross encoder trained on the [Baidu-ULTR](https://arxiv.org/abs/2207.03051) dataset with an **additivie two tower architecture** as suggested by [Yan et al](https://research.google/pubs/revisiting-two-tower-models-for-unbiased-learning-to-rank/). Similar to a position-based click model (PBM), a two tower model jointly learns item relevance (with a BERT model) and position bias (in our case using a single embedding per rank). For more info, [read our paper](https://arxiv.org/abs/2404.02543) and [find the code for this model here](https://github.com/philipphager/baidu-bert-model).
 
 ## Test Results on Baidu-ULTR
-You can download all pre-trained models from hugging face hub by clicking the model names below. We also list the evaluation results on the Baidu-ULTR test set. Ranking performance is measured in DCG, nDCG, and MRR on expert annotations (6,985 queries). Click prediction performance is measured in log-likelihood on one test partition of user clicks (≈297k queries).
+Ranking performance is measured in DCG, nDCG, and MRR on expert annotations (6,985 queries). Click prediction performance is measured in log-likelihood on one test partition of user clicks (≈297k queries).
 
 | Model                                                                                          | Log-likelihood | DCG@1 | DCG@3 | DCG@5 | DCG@10 | nDCG@10 | MRR@10 |
 |------------------------------------------------------------------------------------------------|----------------|-------|-------|-------|--------|---------|--------|