Update README.md

README.md

@@ -61,7 +61,10 @@ print(scores)
 ## Evaluation
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-We evaluated our model on 500 random queries from the mMARCO-fr train set (which were excluded from training). Each of these queries has at least one relevant and up to 200 irrelevant passages. Below, we compare the model performance with other cross-encoder models fine-tuned on the same dataset. We report the R-precision (RP), mean reciprocal rank (MRR), and recall at various cut-offs (R@k).
+We evaluated the model on 500 random queries from the mMARCO-fr train set (which were excluded from training). Each of these queries has at least one relevant and up to 200 irrelevant passages. 
+
+Below, we compare the model performance with other cross-encoder models fine-tuned on the same dataset. We report the R-precision (RP), mean reciprocal rank (MRR), and recall at various cut-offs (R@k).
+
 |    | model                                                                                                                                                                                  |            RP |   MRR@10 |    R@10 (↑) |        R@20 |        R@50 |        R@100 |
 |---:|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------:|---------:|------------:|------------:|------------:|-------------:|
 |  1 | **crossencoder-camembert-base-mmarcoFR**                                                                                                                                               |         35.65 |    50.44 |       82.95 |       91.5  |       96.8  |        98.8  |
@@ -85,7 +88,7 @@ We trained the model on a single Tesla V100 GPU with 32GBs of memory during 10 e
 
 #### Data
 
-We used the French version of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset to fine-tune our model. mMARCO is a multi-lingual machine-translated version of the MS MARCO dataset, a popular large-scale IR dataset.
+We used the French version of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) dataset to fine-tune the model. mMARCO is a multi-lingual machine-translated version of the MS MARCO dataset, a popular large-scale IR dataset.
 
 ## Citation
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