Update README.md

README.md

@@ -30,7 +30,7 @@ Overall, the v2 series of models have better search relevance, efficiency and in
 - **Paper**: [Towards Competitive Search Relevance For Inference-Free Learned Sparse Retrievers](https://arxiv.org/abs/2411.04403)
 - **Fine-tuning sample**: [opensearch-sparse-model-tuning-sample](https://github.com/zhichao-aws/opensearch-sparse-model-tuning-sample)
 
-This is a learned sparse retrieval model. It encodes the documents to 30522 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors. In the real-world use case, the search performance of opensearch-neural-sparse-encoding-v1 is comparable to BM25.
+This is a learned sparse retrieval model. It encodes the documents to 30522 dimensional **sparse vectors**. For queries, it just use a tokenizer and a weight look-up table to generate sparse vectors. The non-zero dimension index means the corresponding token in the vocabulary, and the weight means the importance of the token. And the similarity score is the inner product of query/document sparse vectors.
 
 This model is trained on MS MARCO dataset.