Update README.md

README.md

@@ -24,22 +24,16 @@ model-index:
       metrics:
         - type: recall_at_500
           name: Recall@500
-          value: 0.0
+          value: 95.11
         - type: recall_at_100
           name: Recall@100
-          value: 0.0
+          value: 82.72
         - type: recall_at_10
           name: Recall@10
-          value: 0.0
-        - type: map_at_10
-          name: MAP@10
-          value: 0.0
-        - type: ndcg_at_10
-          name: nDCG@10
-          value: 0.0
+          value: 56.03
         - type: mrr_at_10
           name: MRR@10
-          value: 0.0
+          value: 31.70
 ---
 
 # crossencoder-electra-base-french-mmarcoFR
@@ -107,8 +101,8 @@ print(scores)
 
 The model is evaluated on the smaller development set of [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/), which consists of 6,980 queries for which 
 an ensemble of 1000 passages containing the positive(s) and [ColBERTv2 hard negatives](https://huggingface.co/datasets/antoinelouis/msmarco-dev-small-negatives) need 
-to be reranked. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs 
-(R@k). To see how it compares to other neural retrievers in French, check out the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
+to be reranked. We report the mean reciprocal rank (MRR) and recall at various cut-offs (R@k). To see how it compares to other neural retrievers in French, check out 
+the [*DécouvrIR*](https://huggingface.co/spaces/antoinelouis/decouvrir) leaderboard.
 
 ***