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README.md

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 ## Performance
 The performance was evaluated on three datasets:
-- **TREC-DL19 EN-EN**: The original [TREC 2019 Deep Learning Track](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html): Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score 95.47. 
+- **TREC-DL19 EN-EN**: The original [TREC 2019 Deep Learning Track](https://microsoft.github.io/msmarco/TREC-Deep-Learning-2019.html): Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47. 
 - **TREC-DL19 DE-EN**: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10.
-- **GermanDPR DE-DE**: The [GermanDPR](https://www.deepset.ai/germanquad) dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score 76.27.
+- **GermanDPR DE-DE**: The [GermanDPR](https://www.deepset.ai/germanquad) dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27.
 
 We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search.