In information retrieval, given a query and a set of candidate documents, the goal is to produce a ranking of the documents.

information_retrieval

How many models this model outperform on average (over columns).

The MS MARCO benchmark's regular track for passage retrieval in information retrieval [(https://microsoft.github.io/msmarco/)](https://microsoft.github.io/msmarco/).

RR@10: Mean reciprocal rank at 10 in information retrieval.

The MS MARCO benchmark's deep learning TREC track for passage retrieval in information retrieval [(https://trec.nist.gov)](https://microsoft.github.io/msmarco/).

NDCG@10: Normalized discounted cumulative gain at 10 in information retrieval.

accuracy

min=0.173, mean=0.21, max=0.234, sum=0.63 (3)

min=0.316, mean=0.363, max=0.406, sum=1.089 (3)

min=0.12, mean=0.147, max=0.178, sum=0.441 (3)

min=0.266, mean=0.292, max=0.338, sum=0.877 (3)

min=0.152, mean=0.161, max=0.179, sum=0.483 (3)

min=0.31, mean=0.341, max=0.389, sum=1.022 (3)

min=0.264, mean=0.285, max=0.316, sum=0.855 (3)

min=0.401, mean=0.46, max=0.51, sum=1.38 (3)

min=0.396, mean=0.398, max=0.401, sum=1.195 (3)

min=0.62, mean=0.661, max=0.706, sum=1.982 (3)

min=0.253, mean=0.293, max=0.322, sum=0.879 (3)

min=0.473, mean=0.514, max=0.577, sum=1.543 (3)

min=0.24, mean=0.247, max=0.257, sum=0.741 (3)

min=0.454, mean=0.464, max=0.479, sum=1.393 (3)

min=0.34, mean=0.382, max=0.407, sum=1.146 (3)

min=0.625, mean=0.642, max=0.66, sum=1.925 (3)

min=0.212, mean=0.236, max=0.256, sum=0.709 (3)

min=0.364, mean=0.386, max=0.429, sum=1.158 (3)

min=0.252, mean=0.273, max=0.294, sum=0.82 (3)

min=0.429, mean=0.459, max=0.479, sum=1.378 (3)

min=0.164, mean=0.19, max=0.212, sum=0.571 (3)

min=0.292, mean=0.33, max=0.382, sum=0.991 (3)

min=0.143, mean=0.152, max=0.161, sum=0.456 (3)

min=0.337, mean=0.374, max=0.416, sum=1.122 (3)

min=0.258, mean=0.304, max=0.338, sum=0.911 (3)

min=0.291, mean=0.315, max=0.351, sum=0.945 (3)

min=0.526, mean=0.55, max=0.573, sum=1.65 (3)

min=0.147, mean=0.175, max=0.195, sum=0.525 (3)

min=0.329, mean=0.373, max=0.4, sum=1.118 (3)

min=0.423, mean=0.434, max=0.444, sum=1.301 (3)

min=0.702, mean=0.709, max=0.717, sum=2.128 (3)

min=0.467, mean=0.472, max=0.477, sum=1.416 (3)

min=0.761, mean=0.762, max=0.765, sum=2.287 (3)

min=0.127, mean=0.152, max=0.17, sum=0.457 (3)

min=0.315, mean=0.345, max=0.362, sum=1.035 (3)

min=0.171, mean=0.184, max=0.203, sum=0.551 (3)

min=0.37, mean=0.398, max=0.436, sum=1.195 (3)

min=0.257, mean=0.288, max=0.316, sum=0.864 (3)

min=0.425, mean=0.448, max=0.467, sum=1.344 (3)

min=0.231, mean=0.237, max=0.243, sum=0.711 (3)

min=0.467, mean=0.482, max=0.511, sum=1.446 (3)

min=0.368, mean=0.377, max=0.394, sum=1.132 (3)

min=0.621, mean=0.643, max=0.662, sum=1.93 (3)

min=0.124, mean=0.158, max=0.185, sum=0.475 (3)

min=0.273, mean=0.332, max=0.382, sum=0.997 (3)

min=0.205, mean=0.211, max=0.221, sum=0.632 (3)

min=0.343, mean=0.378, max=0.397, sum=1.135 (3)

min=0.14, mean=0.162, max=0.189, sum=0.486 (3)

min=0.279, mean=0.3, max=0.31, sum=0.899 (3)

min=0.093, mean=0.122, max=0.145, sum=0.367 (3)

min=0.291, mean=0.317, max=0.362, sum=0.95 (3)

min=0.063, mean=0.102, max=0.156, sum=0.306 (3)

min=0.184, mean=0.29, max=0.427, sum=0.871 (3)

min=0.353, mean=0.368, max=0.389, sum=1.103 (3)

min=0.611, mean=0.644, max=0.662, sum=1.931 (3)

min=0.407, mean=0.421, max=0.437, sum=1.262 (3)

min=0.642, mean=0.664, max=0.685, sum=1.991 (3)

min=0.256, mean=0.271, max=0.287, sum=0.814 (3)

min=0.476, mean=0.507, max=0.545, sum=1.522 (3)

min=0.185, mean=0.208, max=0.25, sum=0.623 (3)

min=0.42, mean=0.449, max=0.493, sum=1.347 (3)

min=0.096, mean=0.134, max=0.158, sum=0.403 (3)

min=0.21, mean=0.302, max=0.353, sum=0.905 (3)

min=0.167, mean=0.167, max=0.167, sum=0.167 (1)

min=0.376, mean=0.376, max=0.376, sum=0.376 (1)

Accuracy

The MS MARCO benchmark's regular track for passage retrieval in information retrieval [(https://microsoft.github.io/msmarco/)](https://microsoft.github.io/msmarco/).

RR@10: Mean reciprocal rank at 10 in information retrieval.
- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).

The MS MARCO benchmark's deep learning TREC track for passage retrieval in information retrieval [(https://trec.nist.gov)](https://microsoft.github.io/msmarco/).

NDCG@10: Normalized discounted cumulative gain at 10 in information retrieval.
- Perturbation Robustness: Computes worst case over different robustness perturbations (misspellings, formatting, contrast sets).

robustness

min=0.131, mean=0.144, max=0.157, sum=0.433 (3)

min=0.276, mean=0.307, max=0.347, sum=0.921 (3)

min=0.089, mean=0.105, max=0.128, sum=0.315 (3)

min=0.231, mean=0.248, max=0.274, sum=0.743 (3)

min=0.105, mean=0.121, max=0.133, sum=0.362 (3)

min=0.27, mean=0.297, max=0.328, sum=0.89 (3)

min=0.207, mean=0.222, max=0.244, sum=0.666 (3)

min=0.361, mean=0.407, max=0.448, sum=1.222 (3)

min=0.333, mean=0.337, max=0.343, sum=1.012 (3)

min=0.569, mean=0.607, max=0.639, sum=1.821 (3)

min=0.18, mean=0.227, max=0.253, sum=0.681 (3)

min=0.406, mean=0.423, max=0.451, sum=1.269 (3)

min=0.168, mean=0.177, max=0.186, sum=0.532 (3)

min=0.391, mean=0.397, max=0.403, sum=1.192 (3)

min=0.28, mean=0.308, max=0.326, sum=0.925 (3)

min=0.589, mean=0.592, max=0.594, sum=1.776 (3)

min=0.158, mean=0.19, max=0.218, sum=0.57 (3)

min=0.304, mean=0.333, max=0.385, sum=0.998 (3)

min=0.191, mean=0.207, max=0.223, sum=0.622 (3)

min=0.371, mean=0.397, max=0.414, sum=1.19 (3)

min=0.109, mean=0.13, max=0.147, sum=0.39 (3)

min=0.195, mean=0.257, max=0.323, sum=0.772 (3)

min=0.101, mean=0.109, max=0.12, sum=0.326 (3)

min=0.294, mean=0.315, max=0.354, sum=0.945 (3)

min=0.22, mean=0.252, max=0.287, sum=0.757 (3)

min=0.201, mean=0.242, max=0.292, sum=0.725 (3)

min=0.449, mean=0.482, max=0.527, sum=1.446 (3)

min=0.11, mean=0.13, max=0.144, sum=0.389 (3)

min=0.265, mean=0.314, max=0.339, sum=0.942 (3)

min=0.372, mean=0.387, max=0.401, sum=1.161 (3)

min=0.68, mean=0.685, max=0.689, sum=2.054 (3)

min=0.429, mean=0.434, max=0.438, sum=1.303 (3)

min=0.726, mean=0.734, max=0.743, sum=2.202 (3)

min=0.094, mean=0.116, max=0.131, sum=0.349 (3)

min=0.29, mean=0.319, max=0.336, sum=0.957 (3)

min=0.082, mean=0.096, max=0.107, sum=0.288 (3)

min=0.338, mean=0.351, max=0.365, sum=1.053 (3)

min=0.198, mean=0.235, max=0.263, sum=0.705 (3)

min=0.386, mean=0.408, max=0.422, sum=1.225 (3)

min=0.175, mean=0.179, max=0.187, sum=0.537 (3)

min=0.421, mean=0.437, max=0.46, sum=1.31 (3)

min=0.264, mean=0.287, max=0.315, sum=0.86 (3)

min=0.54, mean=0.565, max=0.586, sum=1.696 (3)

min=0.074, mean=0.105, max=0.125, sum=0.315 (3)

min=0.227, mean=0.278, max=0.312, sum=0.835 (3)

min=0.15, mean=0.154, max=0.157, sum=0.462 (3)

min=0.31, mean=0.332, max=0.352, sum=0.996 (3)

min=0.086, mean=0.11, max=0.14, sum=0.33 (3)

min=0.25, mean=0.253, max=0.254, sum=0.759 (3)

min=0.055, mean=0.073, max=0.086, sum=0.219 (3)

min=0.206, mean=0.246, max=0.285, sum=0.739 (3)

min=0.039, mean=0.072, max=0.111, sum=0.215 (3)

min=0.148, mean=0.247, max=0.358, sum=0.741 (3)

min=0.292, mean=0.304, max=0.319, sum=0.911 (3)

min=0.578, mean=0.616, max=0.645, sum=1.848 (3)

min=0.327, mean=0.344, max=0.366, sum=1.031 (3)

min=0.615, mean=0.628, max=0.641, sum=1.884 (3)

min=0.171, mean=0.198, max=0.222, sum=0.594 (3)

min=0.393, mean=0.444, max=0.486, sum=1.331 (3)

min=0.099, mean=0.122, max=0.16, sum=0.366 (3)

min=0.315, mean=0.356, max=0.413, sum=1.069 (3)

min=0.044, mean=0.069, max=0.091, sum=0.207 (3)

min=0.172, mean=0.252, max=0.302, sum=0.757 (3)

min=0.116, mean=0.116, max=0.116, sum=0.116 (1)

min=0.348, mean=0.348, max=0.348, sum=0.348 (1)

Robustness

The MS MARCO benchmark's regular track for passage retrieval in information retrieval [(https://microsoft.github.io/msmarco/)](https://microsoft.github.io/msmarco/).

RR@10: Mean reciprocal rank at 10 in information retrieval.
- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).

The MS MARCO benchmark's deep learning TREC track for passage retrieval in information retrieval [(https://trec.nist.gov)](https://microsoft.github.io/msmarco/).

NDCG@10: Normalized discounted cumulative gain at 10 in information retrieval.
- Perturbation Fairness: Computes worst case over different fairness perturbations (changing dialect, race of names, gender).

fairness

min=0.151, mean=0.18, max=0.202, sum=0.54 (3)

min=0.308, mean=0.348, max=0.386, sum=1.044 (3)

min=0.096, mean=0.117, max=0.143, sum=0.351 (3)

min=0.258, mean=0.28, max=0.322, sum=0.841 (3)

min=0.126, mean=0.138, max=0.155, sum=0.414 (3)

min=0.296, mean=0.328, max=0.372, sum=0.985 (3)

min=0.23, mean=0.253, max=0.284, sum=0.76 (3)

min=0.371, mean=0.435, max=0.486, sum=1.304 (3)

min=0.339, mean=0.342, max=0.346, sum=1.027 (3)

min=0.578, mean=0.62, max=0.66, sum=1.861 (3)

min=0.206, mean=0.243, max=0.271, sum=0.728 (3)

min=0.438, mean=0.471, max=0.522, sum=1.413 (3)

min=0.21, mean=0.215, max=0.221, sum=0.646 (3)

min=0.432, mean=0.44, max=0.457, sum=1.32 (3)

min=0.305, mean=0.345, max=0.369, sum=1.036 (3)

min=0.589, mean=0.609, max=0.63, sum=1.828 (3)

min=0.189, mean=0.211, max=0.231, sum=0.633 (3)

min=0.345, mean=0.371, max=0.418, sum=1.114 (3)

min=0.211, mean=0.233, max=0.251, sum=0.698 (3)

min=0.394, mean=0.431, max=0.457, sum=1.292 (3)

min=0.136, mean=0.164, max=0.189, sum=0.493 (3)

min=0.273, mean=0.312, max=0.361, sum=0.936 (3)

min=0.126, mean=0.132, max=0.136, sum=0.396 (3)

min=0.321, mean=0.357, max=0.398, sum=1.072 (3)

min=0.228, mean=0.28, max=0.324, sum=0.84 (3)

min=0.239, mean=0.267, max=0.302, sum=0.802 (3)

min=0.51, mean=0.522, max=0.544, sum=1.565 (3)

min=0.123, mean=0.145, max=0.162, sum=0.436 (3)

min=0.311, mean=0.353, max=0.384, sum=1.058 (3)

min=0.402, mean=0.411, max=0.42, sum=1.232 (3)

min=0.68, mean=0.69, max=0.696, sum=2.069 (3)

min=0.444, mean=0.45, max=0.453, sum=1.35 (3)

min=0.745, mean=0.748, max=0.752, sum=2.245 (3)

min=0.106, mean=0.129, max=0.144, sum=0.387 (3)

min=0.303, mean=0.332, max=0.348, sum=0.997 (3)

min=0.137, mean=0.148, max=0.163, sum=0.445 (3)

min=0.347, mean=0.381, max=0.416, sum=1.144 (3)

min=0.229, mean=0.26, max=0.288, sum=0.779 (3)

min=0.4, mean=0.419, max=0.428, sum=1.256 (3)

min=0.21, mean=0.214, max=0.221, sum=0.642 (3)

min=0.45, mean=0.471, max=0.501, sum=1.412 (3)

min=0.332, mean=0.341, max=0.354, sum=1.024 (3)

min=0.592, mean=0.612, max=0.629, sum=1.836 (3)

min=0.109, mean=0.14, max=0.166, sum=0.419 (3)

min=0.256, mean=0.317, max=0.363, sum=0.95 (3)

min=0.179, mean=0.185, max=0.192, sum=0.554 (3)

min=0.324, mean=0.357, max=0.375, sum=1.072 (3)

min=0.119, mean=0.14, max=0.167, sum=0.42 (3)

min=0.266, mean=0.284, max=0.295, sum=0.852 (3)

min=0.082, mean=0.105, max=0.123, sum=0.316 (3)

min=0.275, mean=0.301, max=0.346, sum=0.902 (3)

min=0.051, mean=0.086, max=0.134, sum=0.258 (3)

min=0.17, mean=0.268, max=0.399, sum=0.804 (3)

min=0.322, mean=0.335, max=0.353, sum=1.005 (3)

min=0.603, mean=0.633, max=0.652, sum=1.898 (3)

min=0.357, mean=0.373, max=0.39, sum=1.12 (3)

min=0.614, mean=0.639, max=0.663, sum=1.917 (3)