Update README.md

README.md

@@ -25,7 +25,7 @@ Under Construction, please come back in a few days!
 Most retrieval methods have strong tradeoffs: 
  * __Traditional sparse approaches__, such as BM25, are strong baselines, __but__ do not leverage any semantic understanding, and thus hit a hard ceiling.
  * __Cross-encoder__ retriever methods are powerful, __but__ prohibitively expensive over large datasets: they must process the query against every single known document to be able to output scores.
- * __Dense retrieval__ methods, using dense embeddings in vector databases, are lightweight and perform well, __but__ are data-inefficient (they often require hundreds of millions if not billions of  training examples pairs to reach state-of-the-art performance) and generalise poorly in a lot of cases. This makes sense: representing every single aspect of a document, to be able to match it to any potential query, into a single vector is an extremely hard problem.
+ * __Dense retrieval__ methods, using dense embeddings in vector databases, are lightweight and perform well, __but__ are __not__ data-efficient (they often require hundreds of millions if not billions of  training examples pairs to reach state-of-the-art performance) and generalise poorly in a lot of cases. This makes sense: representing every single aspect of a document, to be able to match it to any potential query, into a single vector is an extremely hard problem.
 
 ColBERT and its variants, including JaColBERT, aim to combine the best of all worlds: by representing the documents as essentially *bags-of-embeddings*, we obtain superior performance and strong out-of-domain generalisation at much lower compute cost than cross-encoders.