Update README.md

README.md

@@ -44,7 +44,7 @@ model-index:
 
 # biencoder-camemberta-base-mmarcoFR
 
-This is a dense single-vector bi-encoder model. It maps sentences and paragraphs to a 768 dimensional dense vector space and should be used for semantic search. The model was trained on the **French** portion of the [mMARCO](https://huggingface.co/datasets/unicamp-dl/mmarco) retrieval dataset.
+This is a dense single-vector bi-encoder model for **French** that can be used for semantic search. The model maps queries and passages to 768-dimensional dense vectors which are used to compute relevance through cosine similarity.
 
 ## Usage
 
@@ -121,24 +121,11 @@ similarity = q_embeddings @ p_embeddings.T
 print(similarity)
 ```
 
-***
-
 ## Evaluation
 
-We evaluate the model on the smaller development set of [mMARCO-fr](https://ir-datasets.com/mmarco.html#mmarco/v2/fr/), which consists of 6,980 queries for a corpus of 8.8M candidate passages. Below, we compare the model performance with other biencoder models fine-tuned on the same dataset. We report the mean reciprocal rank (MRR), normalized discounted cumulative gainand (NDCG), mean average precision (MAP), and recall at various cut-offs (R@k).
-
-|    | model                                                                                                                   | Vocab. | #Param. |  Size |   MRR@10 |   NDCG@10 |   MAP@10 |   R@10 |   R@100(↑) |   R@500 |
-|---:|:------------------------------------------------------------------------------------------------------------------------|:-------|--------:|------:|---------:|----------:|---------:|-------:|-----------:|--------:|
-|  1 | [biencoder-camembert-base-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-camembert-base-mmarcoFR)              |     🇫🇷 |    110M | 443MB |    28.53 |     33.72 |    27.93 |  51.46 |      77.82 |   89.13 |
-|  2 | [biencoder-mpnet-base-all-v2-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mpnet-base-all-v2-mmarcoFR)        |     🇬🇧 |    109M | 438MB |    28.04 |     33.28 |    27.50 |  51.07 |      77.68 |   88.67 |
-|  3 | [biencoder-distilcamembert-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-distilcamembert-mmarcoFR)            |     🇫🇷 |     68M | 272MB |    26.80 |     31.87 |    26.23 |  49.20 |      76.44 |   87.87 |
-|  4 | [biencoder-MiniLM-L6-all-v2-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-MiniLM-L6-all-v2-mmarcoFR)          |     🇬🇧 |     23M |  91MB |    25.49 |     30.39 |    24.99 |  47.10 |      73.48 |   86.09 |
-|  5 | [biencoder-mMiniLMv2-L12-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mMiniLMv2-L12-mmarcoFR)                | 🇫🇷,99+ |    117M | 471MB |    24.74 |     29.41 |    24.23 |  45.40 |      71.52 |   84.42 |
-|  6 | **biencoder-camemberta-base-mmarcoFR**                                                                                  |     🇫🇷 |    112M | 447MB |    24.78 |     29.24 |    24.23 |  44.58 |      69.59 |   82.18 |
-|  7 | [biencoder-electra-base-french-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-electra-base-french-mmarcoFR)    |     🇫🇷 |    110M | 440MB |    23.38 |     27.97 |    22.91 |  43.50 |      68.96 |   81.61 |
-|  8 | [biencoder-mMiniLMv2-L6-mmarcoFR](https://huggingface.co/antoinelouis/biencoder-mMiniLMv2-L6-mmarcoFR)                  | 🇫🇷,99+ |    107M | 428MB |    22.29 |     26.57 |    21.80 |  41.25 |      66.78 |   79.83 |
-
-***
+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 a corpus of 
+8.8M candidate passages. 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.
 
 ## Training
 
@@ -153,17 +140,15 @@ The model is initialized from the [almanach/camemberta-base](https://huggingface
 using the AdamW optimizer with a batch size of 64, a peak learning rate of 2e-5 with warm up along the first 500 steps and linear scheduling. We set the maximum 
 sequence lengths for both the questions and passages to 128 tokens. We use the cosine similarity to compute relevance scores.
 
-***
-
 ## Citation
 
 ```bibtex
-@online{louis2023,
-   author    = 'Antoine Louis',
-   title     = 'biencoder-camemberta-base-mmarcoFR: A Biencoder Model Trained on French mMARCO',
-   publisher = 'Hugging Face',
-   month     = 'may',
-   year      = '2023',
-   url       = 'https://huggingface.co/antoinelouis/biencoder-camemberta-base-mmarcoFR',
+@online{louis2024decouvrir,
+	author    = 'Antoine Louis',
+	title     = 'DécouvrIR: A Benchmark for Evaluating the Robustness of Information Retrieval Models in French',
+	publisher = 'Hugging Face',
+	month     = 'mar',
+	year      = '2024',
+	url       = 'https://huggingface.co/spaces/antoinelouis/decouvrir',
 }
 ```