Update README.md

README.md

@@ -21,7 +21,7 @@ It is a bi-encoder trained on a corpus of context/query pairs, with 50% in Engli
 Benchmark
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-Based on the SQuAD evaluation dataset (comprising 6000 queries distributed over 1200 contexts grouped into 35 themes), we compare the performance in terms of the average top contexter value for a query (Top-mean), the standard deviation of the average top (Top-std), and the percentage of correct queries within the top-1, top-5, and top-10. We compare the model with a TF-IDF trained on the SQuAD train sub-dataset, CamemBERT, Sentence-BERT, and finally our model. We observe these performances in both monolingual and cross-language contexts (query in French and context in English).
+Based on the SQuAD evaluation dataset (comprising 6000 queries distributed over 1200 contexts grouped into 35 themes), we compare the performance in terms of the average top contexter value for a query (Top-mean), the standard deviation of the average top (Top-std), and the percentage of correct queries within the top-1, top-5, and top-10. We compare the model with a TF-IDF trained on the SQuAD train sub-dataset (we want a fixed algebraic structure for the vector database instead of a variable structure every time we add a new document, then the IDF part has frozen), CamemBERT, Sentence-BERT, and finally our model. We observe these performances in both monolingual and cross-language contexts (query in French and context in English).
 
  Model (FR/FR)                                                                                        | Top-mean | Top-std | Top-1 (%) | Top-5 (%) | Top-10 (%) |
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