Update README.md

README.md

@@ -13,7 +13,7 @@ license: gemma
 This is an improved version of reranker based on [sdadas/polish-roberta-large-v2](https://huggingface.co/sdadas/polish-roberta-large-v2) trained with [RankNet loss](https://icml.cc/Conferences/2015/wp-content/uploads/2015/06/icml_ranking.pdf) on a large dataset of text pairs.
 The model was trained in the same way and on the same data as [sdadas/polish-roberta-large-ranknet](https://huggingface.co/sdadas/polish-roberta-large-ranknet), with the following improvements:
 - We used predictions from [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) for distillation instead of [unicamp-dl/mt5-13b-mmarco-100k](https://huggingface.co/unicamp-dl/mt5-13b-mmarco-100k).
-- We used a custom implementation of the RoBERTa with support for Flash Attention 2. If you want to use these features, load the model with the arguments `trust_remote_code=True` and `attn_implementation="flash_attention_2"`.
+- We used a custom implementation of the RoBERTa model with support for Flash Attention 2. If you want to use these features, load the model with the arguments `trust_remote_code=True` and `attn_implementation="flash_attention_2"`.
 
 Our reranker achieves results close to [BAAI/bge-reranker-v2.5-gemma2-lightweight](https://huggingface.co/BAAI/bge-reranker-v2.5-gemma2-lightweight) on the PIRB benchmark, even outperforming it on some datasets. At the same time, it is over 21 times smaller — 435M vs. 9.24B parameters.