Update README.md
Browse files
README.md
CHANGED
@@ -14,17 +14,17 @@ model-index:
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revision: None
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metrics:
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- type: cos_sim_pearson
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-
value:
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- type: cos_sim_spearman
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-
value:
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- type: euclidean_pearson
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-
value:
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- type: euclidean_spearman
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-
value:
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- type: manhattan_pearson
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-
value:
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- type: manhattan_spearman
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-
value:
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- task:
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type: STS
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dataset:
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@@ -35,17 +35,17 @@ model-index:
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revision: None
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metrics:
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- type: cos_sim_pearson
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-
value:
|
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- type: cos_sim_spearman
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-
value:
|
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- type: euclidean_pearson
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-
value:
|
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- type: euclidean_spearman
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-
value:
|
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- type: manhattan_pearson
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-
value:
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- type: manhattan_spearman
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-
value:
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- task:
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type: Classification
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dataset:
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@@ -56,9 +56,9 @@ model-index:
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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-
value:
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- type: f1
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-
value: 46.
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- task:
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type: STS
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dataset:
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@@ -69,17 +69,17 @@ model-index:
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revision: None
|
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metrics:
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- type: cos_sim_pearson
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-
value:
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- type: cos_sim_spearman
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-
value:
|
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- type: euclidean_pearson
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-
value:
|
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- type: euclidean_spearman
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-
value:
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- type: manhattan_pearson
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-
value:
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- type: manhattan_spearman
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-
value:
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- task:
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type: Clustering
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dataset:
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@@ -90,7 +90,7 @@ model-index:
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revision: None
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metrics:
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- type: v_measure
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-
value:
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- task:
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type: Clustering
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dataset:
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@@ -101,7 +101,7 @@ model-index:
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revision: None
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metrics:
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- type: v_measure
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-
value:
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- task:
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type: Reranking
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dataset:
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@@ -112,9 +112,9 @@ model-index:
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revision: None
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metrics:
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- type: map
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-
value: 88.
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- type: mrr
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-
value: 90.
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- task:
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type: Reranking
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dataset:
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@@ -125,9 +125,9 @@ model-index:
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revision: None
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metrics:
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- type: map
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-
value:
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- type: mrr
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-
value:
|
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- task:
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type: Retrieval
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dataset:
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@@ -138,65 +138,65 @@ model-index:
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revision: None
|
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metrics:
|
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- type: map_at_1
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-
value: 26.
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- type: map_at_10
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-
value: 40.
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- type: map_at_100
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-
value:
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- type: map_at_1000
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-
value: 42.
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- type: map_at_3
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-
value: 35.
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- type: map_at_5
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-
value: 38.
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- type: mrr_at_1
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-
value: 40.
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- type: mrr_at_10
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-
value:
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- type: mrr_at_100
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-
value:
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- type: mrr_at_1000
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-
value:
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- type: mrr_at_3
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-
value: 46.
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- type: mrr_at_5
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-
value:
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- type: ndcg_at_1
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-
value: 40.
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- type: ndcg_at_10
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-
value: 46.
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- type: ndcg_at_100
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-
value:
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- type: ndcg_at_1000
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-
value: 55.
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- type: ndcg_at_3
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-
value:
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- type: ndcg_at_5
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-
value: 43.
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- type: precision_at_1
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-
value: 40.
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- type: precision_at_10
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-
value: 10.
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- type: precision_at_100
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-
value: 1.
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- type: precision_at_1000
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value: 0.184
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- type: precision_at_3
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-
value: 23.
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- type: precision_at_5
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-
value:
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- type: recall_at_1
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-
value: 26.
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- type: recall_at_10
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-
value: 57.
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- type: recall_at_100
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-
value: 87.
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- type: recall_at_1000
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-
value: 98.
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- type: recall_at_3
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-
value:
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- type: recall_at_5
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-
value: 48.
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- task:
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type: PairClassification
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dataset:
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@@ -207,51 +207,51 @@ model-index:
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revision: None
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metrics:
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- type: cos_sim_accuracy
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-
value:
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- type: cos_sim_ap
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-
value:
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- type: cos_sim_f1
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-
value:
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- type: cos_sim_precision
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-
value:
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- type: cos_sim_recall
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-
value:
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- type: dot_accuracy
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-
value:
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- type: dot_ap
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-
value:
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- type: dot_f1
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-
value:
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- type: dot_precision
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-
value:
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- type: dot_recall
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-
value:
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- type: euclidean_accuracy
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-
value:
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- type: euclidean_ap
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-
value:
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- type: euclidean_f1
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-
value:
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- type: euclidean_precision
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-
value:
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- type: euclidean_recall
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-
value:
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- type: manhattan_accuracy
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-
value:
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- type: manhattan_ap
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-
value:
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- type: manhattan_f1
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-
value:
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- type: manhattan_precision
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-
value:
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- type: manhattan_recall
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-
value:
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- type: max_accuracy
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-
value:
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- type: max_ap
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-
value:
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- type: max_f1
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-
value:
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- task:
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type: Retrieval
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dataset:
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@@ -262,65 +262,65 @@ model-index:
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revision: None
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metrics:
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- type: map_at_1
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-
value: 68.
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- type: map_at_10
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-
value:
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- type: map_at_100
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-
value:
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- type: map_at_1000
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-
value:
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- type: map_at_3
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-
value:
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- type: map_at_5
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-
value:
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- type: mrr_at_1
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-
value: 68.
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- type: mrr_at_10
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-
value:
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- type: mrr_at_100
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-
value:
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- type: mrr_at_1000
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-
value:
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- type: mrr_at_3
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-
value:
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- type: mrr_at_5
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-
value:
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- type: ndcg_at_1
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-
value: 68.
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- type: ndcg_at_10
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-
value:
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- type: ndcg_at_100
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-
value:
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- type: ndcg_at_1000
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-
value:
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- type: ndcg_at_3
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-
value:
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- type: ndcg_at_5
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-
value:
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- type: precision_at_1
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-
value: 68.
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- type: precision_at_10
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-
value: 9.
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- type: precision_at_100
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-
value:
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- type: precision_at_1000
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value: 0.101
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- type: precision_at_3
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-
value:
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- type: precision_at_5
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-
value:
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- type: recall_at_1
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-
value: 68.
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- type: recall_at_10
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-
value:
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- type: recall_at_100
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-
value:
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- type: recall_at_1000
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-
value: 99.
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- type: recall_at_3
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-
value:
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- type: recall_at_5
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-
value:
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- task:
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type: Retrieval
|
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dataset:
|
@@ -331,65 +331,65 @@ model-index:
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revision: None
|
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metrics:
|
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- type: map_at_1
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-
value:
|
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- type: map_at_10
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-
value:
|
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- type: map_at_100
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-
value:
|
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- type: map_at_1000
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-
value:
|
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- type: map_at_3
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-
value:
|
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- type: map_at_5
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-
value:
|
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- type: mrr_at_1
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-
value:
|
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- type: mrr_at_10
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-
value:
|
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- type: mrr_at_100
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-
value:
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- type: mrr_at_1000
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-
value:
|
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- type: mrr_at_3
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-
value:
|
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- type: mrr_at_5
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-
value:
|
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- type: ndcg_at_1
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-
value:
|
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- type: ndcg_at_10
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-
value:
|
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- type: ndcg_at_100
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-
value:
|
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- type: ndcg_at_1000
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-
value:
|
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- type: ndcg_at_3
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-
value:
|
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- type: ndcg_at_5
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-
value:
|
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- type: precision_at_1
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-
value:
|
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- type: precision_at_10
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-
value: 41.
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- type: precision_at_100
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-
value: 4.
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- type: precision_at_1000
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-
value: 0.
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- type: precision_at_3
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-
value:
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- type: precision_at_5
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-
value:
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- type: recall_at_1
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-
value:
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- type: recall_at_10
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-
value:
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- type: recall_at_100
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-
value:
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- type: recall_at_1000
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-
value: 99.
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- type: recall_at_3
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-
value:
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- type: recall_at_5
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-
value:
|
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- task:
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type: Retrieval
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dataset:
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@@ -400,65 +400,65 @@ model-index:
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revision: None
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metrics:
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- type: map_at_1
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-
value: 52.
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- type: map_at_10
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-
value: 62.
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- type: map_at_100
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-
value:
|
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- type: map_at_1000
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-
value:
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- type: map_at_3
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-
value:
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- type: map_at_5
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-
value: 61.
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- type: mrr_at_1
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-
value: 52.
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- type: mrr_at_10
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-
value: 62.
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- type: mrr_at_100
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-
value:
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- type: mrr_at_1000
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-
value:
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- type: mrr_at_3
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-
value:
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- type: mrr_at_5
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-
value: 61.
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- type: ndcg_at_1
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-
value: 52.
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- type: ndcg_at_10
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-
value: 67.
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- type: ndcg_at_100
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-
value: 69.
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- type: ndcg_at_1000
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-
value: 70.
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- type: ndcg_at_3
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-
value: 62.
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- type: ndcg_at_5
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-
value:
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- type: precision_at_1
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-
value: 52.
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- type: precision_at_10
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-
value: 8.
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- type: precision_at_100
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-
value: 0.
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- type: precision_at_1000
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value: 0.097
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- type: precision_at_3
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-
value: 23.
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- type: precision_at_5
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-
value: 15.
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- type: recall_at_1
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-
value: 52.
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- type: recall_at_10
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-
value:
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- type: recall_at_100
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-
value: 94.
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- type: recall_at_1000
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-
value: 97.
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- type: recall_at_3
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-
value:
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- type: recall_at_5
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-
value:
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- task:
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type: Classification
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dataset:
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@@ -469,9 +469,9 @@ model-index:
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revision: None
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metrics:
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- type: accuracy
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-
value:
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- type: f1
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-
value:
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- task:
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type: Classification
|
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dataset:
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@@ -482,11 +482,11 @@ model-index:
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revision: None
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metrics:
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- type: accuracy
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-
value:
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- type: ap
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-
value:
|
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- type: f1
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-
value:
|
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- task:
|
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type: STS
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dataset:
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@@ -497,17 +497,17 @@ model-index:
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revision: None
|
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metrics:
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- type: cos_sim_pearson
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-
value:
|
501 |
- type: cos_sim_spearman
|
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-
value:
|
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- type: euclidean_pearson
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-
value:
|
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- type: euclidean_spearman
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-
value:
|
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- type: manhattan_pearson
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-
value:
|
509 |
- type: manhattan_spearman
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-
value:
|
511 |
- task:
|
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type: Reranking
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513 |
dataset:
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@@ -518,9 +518,9 @@ model-index:
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revision: None
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metrics:
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- type: map
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-
value:
|
522 |
- type: mrr
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-
value:
|
524 |
- task:
|
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type: Retrieval
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dataset:
|
@@ -531,65 +531,65 @@ model-index:
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revision: None
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metrics:
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- type: map_at_1
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-
value:
|
535 |
- type: map_at_10
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-
value:
|
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- type: map_at_100
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-
value: 75.
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539 |
- type: map_at_1000
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-
value: 75.
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- type: map_at_3
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-
value: 73.
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- type: map_at_5
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-
value:
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- type: mrr_at_1
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-
value:
|
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- type: mrr_at_10
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-
value:
|
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- type: mrr_at_100
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-
value:
|
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- type: mrr_at_1000
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-
value:
|
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- type: mrr_at_3
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-
value:
|
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- type: mrr_at_5
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-
value:
|
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- type: ndcg_at_1
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-
value:
|
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- type: ndcg_at_10
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-
value:
|
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- type: ndcg_at_100
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-
value:
|
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- type: ndcg_at_1000
|
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-
value:
|
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- type: ndcg_at_3
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-
value: 75.
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- type: ndcg_at_5
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-
value: 77.
|
569 |
- type: precision_at_1
|
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-
value:
|
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- type: precision_at_10
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-
value: 9.
|
573 |
- type: precision_at_100
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-
value: 1.
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- type: precision_at_1000
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value: 0.105
|
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- type: precision_at_3
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-
value: 28.
|
579 |
- type: precision_at_5
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-
value:
|
581 |
- type: recall_at_1
|
582 |
-
value:
|
583 |
- type: recall_at_10
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584 |
-
value: 89.
|
585 |
- type: recall_at_100
|
586 |
-
value: 96.
|
587 |
- type: recall_at_1000
|
588 |
-
value: 98.
|
589 |
- type: recall_at_3
|
590 |
-
value: 80.
|
591 |
- type: recall_at_5
|
592 |
-
value:
|
593 |
- task:
|
594 |
type: Classification
|
595 |
dataset:
|
@@ -600,9 +600,9 @@ model-index:
|
|
600 |
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
601 |
metrics:
|
602 |
- type: accuracy
|
603 |
-
value:
|
604 |
- type: f1
|
605 |
-
value:
|
606 |
- task:
|
607 |
type: Classification
|
608 |
dataset:
|
@@ -613,9 +613,9 @@ model-index:
|
|
613 |
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
614 |
metrics:
|
615 |
- type: accuracy
|
616 |
-
value:
|
617 |
- type: f1
|
618 |
-
value:
|
619 |
- task:
|
620 |
type: Retrieval
|
621 |
dataset:
|
@@ -626,65 +626,65 @@ model-index:
|
|
626 |
revision: None
|
627 |
metrics:
|
628 |
- type: map_at_1
|
629 |
-
value: 54.
|
630 |
- type: map_at_10
|
631 |
-
value:
|
632 |
- type: map_at_100
|
633 |
-
value: 61.
|
634 |
- type: map_at_1000
|
635 |
-
value: 61.
|
636 |
- type: map_at_3
|
637 |
-
value: 59.
|
638 |
- type: map_at_5
|
639 |
-
value: 60.
|
640 |
- type: mrr_at_1
|
641 |
-
value:
|
642 |
- type: mrr_at_10
|
643 |
-
value: 61.
|
644 |
- type: mrr_at_100
|
645 |
-
value: 61.
|
646 |
- type: mrr_at_1000
|
647 |
-
value:
|
648 |
- type: mrr_at_3
|
649 |
-
value: 59.
|
650 |
- type: mrr_at_5
|
651 |
-
value: 60.
|
652 |
- type: ndcg_at_1
|
653 |
-
value: 54.
|
654 |
- type: ndcg_at_10
|
655 |
-
value: 64.
|
656 |
- type: ndcg_at_100
|
657 |
-
value: 67.
|
658 |
- type: ndcg_at_1000
|
659 |
-
value: 68.
|
660 |
- type: ndcg_at_3
|
661 |
-
value:
|
662 |
- type: ndcg_at_5
|
663 |
-
value: 62.
|
664 |
- type: precision_at_1
|
665 |
-
value: 54.
|
666 |
- type: precision_at_10
|
667 |
-
value: 7.
|
668 |
- type: precision_at_100
|
669 |
-
value: 0.
|
670 |
- type: precision_at_1000
|
671 |
value: 0.098
|
672 |
- type: precision_at_3
|
673 |
-
value:
|
674 |
- type: precision_at_5
|
675 |
-
value: 13.
|
676 |
- type: recall_at_1
|
677 |
-
value: 54.
|
678 |
- type: recall_at_10
|
679 |
-
value:
|
680 |
- type: recall_at_100
|
681 |
-
value:
|
682 |
- type: recall_at_1000
|
683 |
-
value: 97.
|
684 |
- type: recall_at_3
|
685 |
-
value:
|
686 |
- type: recall_at_5
|
687 |
-
value:
|
688 |
- task:
|
689 |
type: Classification
|
690 |
dataset:
|
@@ -695,9 +695,9 @@ model-index:
|
|
695 |
revision: None
|
696 |
metrics:
|
697 |
- type: accuracy
|
698 |
-
value:
|
699 |
- type: f1
|
700 |
-
value:
|
701 |
- task:
|
702 |
type: PairClassification
|
703 |
dataset:
|
@@ -708,51 +708,51 @@ model-index:
|
|
708 |
revision: None
|
709 |
metrics:
|
710 |
- type: cos_sim_accuracy
|
711 |
-
value:
|
712 |
- type: cos_sim_ap
|
713 |
-
value:
|
714 |
- type: cos_sim_f1
|
715 |
-
value:
|
716 |
- type: cos_sim_precision
|
717 |
-
value:
|
718 |
- type: cos_sim_recall
|
719 |
-
value:
|
720 |
- type: dot_accuracy
|
721 |
-
value:
|
722 |
- type: dot_ap
|
723 |
-
value:
|
724 |
- type: dot_f1
|
725 |
-
value:
|
726 |
- type: dot_precision
|
727 |
-
value:
|
728 |
- type: dot_recall
|
729 |
-
value:
|
730 |
- type: euclidean_accuracy
|
731 |
-
value:
|
732 |
- type: euclidean_ap
|
733 |
-
value:
|
734 |
- type: euclidean_f1
|
735 |
-
value:
|
736 |
- type: euclidean_precision
|
737 |
-
value:
|
738 |
- type: euclidean_recall
|
739 |
-
value:
|
740 |
- type: manhattan_accuracy
|
741 |
-
value:
|
742 |
- type: manhattan_ap
|
743 |
-
value:
|
744 |
- type: manhattan_f1
|
745 |
-
value:
|
746 |
- type: manhattan_precision
|
747 |
-
value:
|
748 |
- type: manhattan_recall
|
749 |
-
value:
|
750 |
- type: max_accuracy
|
751 |
-
value:
|
752 |
- type: max_ap
|
753 |
-
value:
|
754 |
- type: max_f1
|
755 |
-
value:
|
756 |
- task:
|
757 |
type: Classification
|
758 |
dataset:
|
@@ -763,11 +763,11 @@ model-index:
|
|
763 |
revision: None
|
764 |
metrics:
|
765 |
- type: accuracy
|
766 |
-
value:
|
767 |
- type: ap
|
768 |
-
value:
|
769 |
- type: f1
|
770 |
-
value:
|
771 |
- task:
|
772 |
type: STS
|
773 |
dataset:
|
@@ -778,17 +778,17 @@ model-index:
|
|
778 |
revision: None
|
779 |
metrics:
|
780 |
- type: cos_sim_pearson
|
781 |
-
value:
|
782 |
- type: cos_sim_spearman
|
783 |
-
value:
|
784 |
- type: euclidean_pearson
|
785 |
-
value:
|
786 |
- type: euclidean_spearman
|
787 |
-
value:
|
788 |
- type: manhattan_pearson
|
789 |
-
value:
|
790 |
- type: manhattan_spearman
|
791 |
-
value:
|
792 |
- task:
|
793 |
type: STS
|
794 |
dataset:
|
@@ -799,17 +799,17 @@ model-index:
|
|
799 |
revision: None
|
800 |
metrics:
|
801 |
- type: cos_sim_pearson
|
802 |
-
value:
|
803 |
- type: cos_sim_spearman
|
804 |
-
value:
|
805 |
- type: euclidean_pearson
|
806 |
-
value:
|
807 |
- type: euclidean_spearman
|
808 |
-
value:
|
809 |
- type: manhattan_pearson
|
810 |
-
value:
|
811 |
- type: manhattan_spearman
|
812 |
-
value:
|
813 |
- task:
|
814 |
type: STS
|
815 |
dataset:
|
@@ -820,17 +820,17 @@ model-index:
|
|
820 |
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
821 |
metrics:
|
822 |
- type: cos_sim_pearson
|
823 |
-
value:
|
824 |
- type: cos_sim_spearman
|
825 |
-
value:
|
826 |
- type: euclidean_pearson
|
827 |
-
value:
|
828 |
- type: euclidean_spearman
|
829 |
-
value:
|
830 |
- type: manhattan_pearson
|
831 |
-
value:
|
832 |
- type: manhattan_spearman
|
833 |
-
value:
|
834 |
- task:
|
835 |
type: STS
|
836 |
dataset:
|
@@ -841,17 +841,17 @@ model-index:
|
|
841 |
revision: None
|
842 |
metrics:
|
843 |
- type: cos_sim_pearson
|
844 |
-
value:
|
845 |
- type: cos_sim_spearman
|
846 |
-
value:
|
847 |
- type: euclidean_pearson
|
848 |
-
value:
|
849 |
- type: euclidean_spearman
|
850 |
-
value:
|
851 |
- type: manhattan_pearson
|
852 |
-
value:
|
853 |
- type: manhattan_spearman
|
854 |
-
value:
|
855 |
- task:
|
856 |
type: Reranking
|
857 |
dataset:
|
@@ -862,9 +862,9 @@ model-index:
|
|
862 |
revision: None
|
863 |
metrics:
|
864 |
- type: map
|
865 |
-
value: 66.
|
866 |
- type: mrr
|
867 |
-
value:
|
868 |
- task:
|
869 |
type: Retrieval
|
870 |
dataset:
|
@@ -875,65 +875,65 @@ model-index:
|
|
875 |
revision: None
|
876 |
metrics:
|
877 |
- type: map_at_1
|
878 |
-
value:
|
879 |
- type: map_at_10
|
880 |
-
value:
|
881 |
- type: map_at_100
|
882 |
-
value:
|
883 |
- type: map_at_1000
|
884 |
-
value:
|
885 |
- type: map_at_3
|
886 |
-
value:
|
887 |
- type: map_at_5
|
888 |
-
value:
|
889 |
- type: mrr_at_1
|
890 |
-
value:
|
891 |
- type: mrr_at_10
|
892 |
-
value:
|
893 |
- type: mrr_at_100
|
894 |
-
value:
|
895 |
- type: mrr_at_1000
|
896 |
-
value:
|
897 |
- type: mrr_at_3
|
898 |
-
value:
|
899 |
- type: mrr_at_5
|
900 |
-
value:
|
901 |
- type: ndcg_at_1
|
902 |
-
value:
|
903 |
- type: ndcg_at_10
|
904 |
-
value:
|
905 |
- type: ndcg_at_100
|
906 |
-
value:
|
907 |
- type: ndcg_at_1000
|
908 |
-
value:
|
909 |
- type: ndcg_at_3
|
910 |
-
value:
|
911 |
- type: ndcg_at_5
|
912 |
-
value:
|
913 |
- type: precision_at_1
|
914 |
-
value:
|
915 |
- type: precision_at_10
|
916 |
-
value:
|
917 |
- type: precision_at_100
|
918 |
-
value:
|
919 |
- type: precision_at_1000
|
920 |
-
value: 0.
|
921 |
- type: precision_at_3
|
922 |
-
value:
|
923 |
- type: precision_at_5
|
924 |
-
value:
|
925 |
- type: recall_at_1
|
926 |
-
value:
|
927 |
- type: recall_at_10
|
928 |
-
value:
|
929 |
- type: recall_at_100
|
930 |
-
value:
|
931 |
- type: recall_at_1000
|
932 |
-
value: 98.
|
933 |
- type: recall_at_3
|
934 |
-
value:
|
935 |
- type: recall_at_5
|
936 |
-
value:
|
937 |
- task:
|
938 |
type: Classification
|
939 |
dataset:
|
@@ -944,9 +944,9 @@ model-index:
|
|
944 |
revision: None
|
945 |
metrics:
|
946 |
- type: accuracy
|
947 |
-
value:
|
948 |
- type: f1
|
949 |
-
value:
|
950 |
- task:
|
951 |
type: Clustering
|
952 |
dataset:
|
@@ -957,7 +957,7 @@ model-index:
|
|
957 |
revision: None
|
958 |
metrics:
|
959 |
- type: v_measure
|
960 |
-
value:
|
961 |
- task:
|
962 |
type: Clustering
|
963 |
dataset:
|
@@ -968,7 +968,7 @@ model-index:
|
|
968 |
revision: None
|
969 |
metrics:
|
970 |
- type: v_measure
|
971 |
-
value:
|
972 |
- task:
|
973 |
type: Retrieval
|
974 |
dataset:
|
@@ -979,65 +979,65 @@ model-index:
|
|
979 |
revision: None
|
980 |
metrics:
|
981 |
- type: map_at_1
|
982 |
-
value:
|
983 |
- type: map_at_10
|
984 |
-
value:
|
985 |
- type: map_at_100
|
986 |
-
value:
|
987 |
- type: map_at_1000
|
988 |
-
value:
|
989 |
- type: map_at_3
|
990 |
-
value:
|
991 |
- type: map_at_5
|
992 |
-
value:
|
993 |
- type: mrr_at_1
|
994 |
-
value:
|
995 |
- type: mrr_at_10
|
996 |
-
value:
|
997 |
- type: mrr_at_100
|
998 |
-
value:
|
999 |
- type: mrr_at_1000
|
1000 |
-
value:
|
1001 |
- type: mrr_at_3
|
1002 |
-
value:
|
1003 |
- type: mrr_at_5
|
1004 |
-
value:
|
1005 |
- type: ndcg_at_1
|
1006 |
-
value:
|
1007 |
- type: ndcg_at_10
|
1008 |
-
value:
|
1009 |
- type: ndcg_at_100
|
1010 |
-
value:
|
1011 |
- type: ndcg_at_1000
|
1012 |
-
value:
|
1013 |
- type: ndcg_at_3
|
1014 |
-
value:
|
1015 |
- type: ndcg_at_5
|
1016 |
-
value:
|
1017 |
- type: precision_at_1
|
1018 |
-
value:
|
1019 |
- type: precision_at_10
|
1020 |
-
value: 8.
|
1021 |
- type: precision_at_100
|
1022 |
-
value: 0.
|
1023 |
- type: precision_at_1000
|
1024 |
value: 0.099
|
1025 |
- type: precision_at_3
|
1026 |
-
value:
|
1027 |
- type: precision_at_5
|
1028 |
-
value:
|
1029 |
- type: recall_at_1
|
1030 |
-
value:
|
1031 |
- type: recall_at_10
|
1032 |
-
value:
|
1033 |
- type: recall_at_100
|
1034 |
-
value: 96.
|
1035 |
- type: recall_at_1000
|
1036 |
-
value:
|
1037 |
- type: recall_at_3
|
1038 |
-
value:
|
1039 |
- type: recall_at_5
|
1040 |
-
value:
|
1041 |
- task:
|
1042 |
type: Classification
|
1043 |
dataset:
|
@@ -1048,11 +1048,11 @@ model-index:
|
|
1048 |
revision: None
|
1049 |
metrics:
|
1050 |
- type: accuracy
|
1051 |
-
value:
|
1052 |
- type: ap
|
1053 |
-
value:
|
1054 |
- type: f1
|
1055 |
-
value:
|
1056 |
---
|
1057 |
|
1058 |
### 使用方法
|
|
|
14 |
revision: None
|
15 |
metrics:
|
16 |
- type: cos_sim_pearson
|
17 |
+
value: 57.03519449697447
|
18 |
- type: cos_sim_spearman
|
19 |
+
value: 61.05687780613
|
20 |
- type: euclidean_pearson
|
21 |
+
value: 59.92928475064863
|
22 |
- type: euclidean_spearman
|
23 |
+
value: 61.05685769955894
|
24 |
- type: manhattan_pearson
|
25 |
+
value: 59.91091069371023
|
26 |
- type: manhattan_spearman
|
27 |
+
value: 61.01906162919386
|
28 |
- task:
|
29 |
type: STS
|
30 |
dataset:
|
|
|
35 |
revision: None
|
36 |
metrics:
|
37 |
- type: cos_sim_pearson
|
38 |
+
value: 56.81511631314823
|
39 |
- type: cos_sim_spearman
|
40 |
+
value: 59.017410073656826
|
41 |
- type: euclidean_pearson
|
42 |
+
value: 63.44414716754522
|
43 |
- type: euclidean_spearman
|
44 |
+
value: 59.017407821544175
|
45 |
- type: manhattan_pearson
|
46 |
+
value: 63.4171455580894
|
47 |
- type: manhattan_spearman
|
48 |
+
value: 59.00005143754492
|
49 |
- task:
|
50 |
type: Classification
|
51 |
dataset:
|
|
|
56 |
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
57 |
metrics:
|
58 |
- type: accuracy
|
59 |
+
value: 49.28
|
60 |
- type: f1
|
61 |
+
value: 46.84433761170775
|
62 |
- task:
|
63 |
type: STS
|
64 |
dataset:
|
|
|
69 |
revision: None
|
70 |
metrics:
|
71 |
- type: cos_sim_pearson
|
72 |
+
value: 71.06047581825707
|
73 |
- type: cos_sim_spearman
|
74 |
+
value: 72.63091479940526
|
75 |
- type: euclidean_pearson
|
76 |
+
value: 71.33861457006756
|
77 |
- type: euclidean_spearman
|
78 |
+
value: 72.63091479809789
|
79 |
- type: manhattan_pearson
|
80 |
+
value: 71.3148241099811
|
81 |
- type: manhattan_spearman
|
82 |
+
value: 72.60884847026323
|
83 |
- task:
|
84 |
type: Clustering
|
85 |
dataset:
|
|
|
90 |
revision: None
|
91 |
metrics:
|
92 |
- type: v_measure
|
93 |
+
value: 55.11593452044331
|
94 |
- task:
|
95 |
type: Clustering
|
96 |
dataset:
|
|
|
101 |
revision: None
|
102 |
metrics:
|
103 |
- type: v_measure
|
104 |
+
value: 45.0556727269734
|
105 |
- task:
|
106 |
type: Reranking
|
107 |
dataset:
|
|
|
112 |
revision: None
|
113 |
metrics:
|
114 |
- type: map
|
115 |
+
value: 88.88589952904408
|
116 |
- type: mrr
|
117 |
+
value: 90.94142857142857
|
118 |
- task:
|
119 |
type: Reranking
|
120 |
dataset:
|
|
|
125 |
revision: None
|
126 |
metrics:
|
127 |
- type: map
|
128 |
+
value: 89.98162054042666
|
129 |
- type: mrr
|
130 |
+
value: 92.06119047619048
|
131 |
- task:
|
132 |
type: Retrieval
|
133 |
dataset:
|
|
|
138 |
revision: None
|
139 |
metrics:
|
140 |
- type: map_at_1
|
141 |
+
value: 26.99
|
142 |
- type: map_at_10
|
143 |
+
value: 40.187
|
144 |
- type: map_at_100
|
145 |
+
value: 42.057
|
146 |
- type: map_at_1000
|
147 |
+
value: 42.156
|
148 |
- type: map_at_3
|
149 |
+
value: 35.704
|
150 |
- type: map_at_5
|
151 |
+
value: 38.307
|
152 |
- type: mrr_at_1
|
153 |
+
value: 40.835
|
154 |
- type: mrr_at_10
|
155 |
+
value: 49.207
|
156 |
- type: mrr_at_100
|
157 |
+
value: 50.163999999999994
|
158 |
- type: mrr_at_1000
|
159 |
+
value: 50.2
|
160 |
- type: mrr_at_3
|
161 |
+
value: 46.649
|
162 |
- type: mrr_at_5
|
163 |
+
value: 48.082
|
164 |
- type: ndcg_at_1
|
165 |
+
value: 40.835
|
166 |
- type: ndcg_at_10
|
167 |
+
value: 46.976
|
168 |
- type: ndcg_at_100
|
169 |
+
value: 54.162
|
170 |
- type: ndcg_at_1000
|
171 |
+
value: 55.84
|
172 |
- type: ndcg_at_3
|
173 |
+
value: 41.417
|
174 |
- type: ndcg_at_5
|
175 |
+
value: 43.864999999999995
|
176 |
- type: precision_at_1
|
177 |
+
value: 40.835
|
178 |
- type: precision_at_10
|
179 |
+
value: 10.403
|
180 |
- type: precision_at_100
|
181 |
+
value: 1.6219999999999999
|
182 |
- type: precision_at_1000
|
183 |
value: 0.184
|
184 |
- type: precision_at_3
|
185 |
+
value: 23.473
|
186 |
- type: precision_at_5
|
187 |
+
value: 17.094
|
188 |
- type: recall_at_1
|
189 |
+
value: 26.99
|
190 |
- type: recall_at_10
|
191 |
+
value: 57.949
|
192 |
- type: recall_at_100
|
193 |
+
value: 87.578
|
194 |
- type: recall_at_1000
|
195 |
+
value: 98.741
|
196 |
- type: recall_at_3
|
197 |
+
value: 41.244
|
198 |
- type: recall_at_5
|
199 |
+
value: 48.727
|
200 |
- task:
|
201 |
type: PairClassification
|
202 |
dataset:
|
|
|
207 |
revision: None
|
208 |
metrics:
|
209 |
- type: cos_sim_accuracy
|
210 |
+
value: 85.07516536380037
|
211 |
- type: cos_sim_ap
|
212 |
+
value: 92.05034893565924
|
213 |
- type: cos_sim_f1
|
214 |
+
value: 85.86387434554975
|
215 |
- type: cos_sim_precision
|
216 |
+
value: 82.0
|
217 |
- type: cos_sim_recall
|
218 |
+
value: 90.10989010989012
|
219 |
- type: dot_accuracy
|
220 |
+
value: 85.07516536380037
|
221 |
- type: dot_ap
|
222 |
+
value: 92.05615563994219
|
223 |
- type: dot_f1
|
224 |
+
value: 85.86387434554975
|
225 |
- type: dot_precision
|
226 |
+
value: 82.0
|
227 |
- type: dot_recall
|
228 |
+
value: 90.10989010989012
|
229 |
- type: euclidean_accuracy
|
230 |
+
value: 85.07516536380037
|
231 |
- type: euclidean_ap
|
232 |
+
value: 92.05034675223959
|
233 |
- type: euclidean_f1
|
234 |
+
value: 85.86387434554975
|
235 |
- type: euclidean_precision
|
236 |
+
value: 82.0
|
237 |
- type: euclidean_recall
|
238 |
+
value: 90.10989010989012
|
239 |
- type: manhattan_accuracy
|
240 |
+
value: 85.13529765484064
|
241 |
- type: manhattan_ap
|
242 |
+
value: 92.02926780269996
|
243 |
- type: manhattan_f1
|
244 |
+
value: 85.87722240858771
|
245 |
- type: manhattan_precision
|
246 |
+
value: 82.29747106729532
|
247 |
- type: manhattan_recall
|
248 |
+
value: 89.78255786766425
|
249 |
- type: max_accuracy
|
250 |
+
value: 85.13529765484064
|
251 |
- type: max_ap
|
252 |
+
value: 92.05615563994219
|
253 |
- type: max_f1
|
254 |
+
value: 85.87722240858771
|
255 |
- task:
|
256 |
type: Retrieval
|
257 |
dataset:
|
|
|
262 |
revision: None
|
263 |
metrics:
|
264 |
- type: map_at_1
|
265 |
+
value: 68.072
|
266 |
- type: map_at_10
|
267 |
+
value: 76.31700000000001
|
268 |
- type: map_at_100
|
269 |
+
value: 76.667
|
270 |
- type: map_at_1000
|
271 |
+
value: 76.671
|
272 |
- type: map_at_3
|
273 |
+
value: 74.52600000000001
|
274 |
- type: map_at_5
|
275 |
+
value: 75.689
|
276 |
- type: mrr_at_1
|
277 |
+
value: 68.282
|
278 |
- type: mrr_at_10
|
279 |
+
value: 76.363
|
280 |
- type: mrr_at_100
|
281 |
+
value: 76.685
|
282 |
- type: mrr_at_1000
|
283 |
+
value: 76.688
|
284 |
- type: mrr_at_3
|
285 |
+
value: 74.517
|
286 |
- type: mrr_at_5
|
287 |
+
value: 75.75
|
288 |
- type: ndcg_at_1
|
289 |
+
value: 68.282
|
290 |
- type: ndcg_at_10
|
291 |
+
value: 80.123
|
292 |
- type: ndcg_at_100
|
293 |
+
value: 81.647
|
294 |
- type: ndcg_at_1000
|
295 |
+
value: 81.784
|
296 |
- type: ndcg_at_3
|
297 |
+
value: 76.595
|
298 |
- type: ndcg_at_5
|
299 |
+
value: 78.689
|
300 |
- type: precision_at_1
|
301 |
+
value: 68.282
|
302 |
- type: precision_at_10
|
303 |
+
value: 9.252
|
304 |
- type: precision_at_100
|
305 |
+
value: 0.997
|
306 |
- type: precision_at_1000
|
307 |
value: 0.101
|
308 |
- type: precision_at_3
|
309 |
+
value: 27.643
|
310 |
- type: precision_at_5
|
311 |
+
value: 17.64
|
312 |
- type: recall_at_1
|
313 |
+
value: 68.072
|
314 |
- type: recall_at_10
|
315 |
+
value: 91.807
|
316 |
- type: recall_at_100
|
317 |
+
value: 98.63
|
318 |
- type: recall_at_1000
|
319 |
+
value: 99.789
|
320 |
- type: recall_at_3
|
321 |
+
value: 82.50800000000001
|
322 |
- type: recall_at_5
|
323 |
+
value: 87.53999999999999
|
324 |
- task:
|
325 |
type: Retrieval
|
326 |
dataset:
|
|
|
331 |
revision: None
|
332 |
metrics:
|
333 |
- type: map_at_1
|
334 |
+
value: 26.511000000000003
|
335 |
- type: map_at_10
|
336 |
+
value: 81.28699999999999
|
337 |
- type: map_at_100
|
338 |
+
value: 84.028
|
339 |
- type: map_at_1000
|
340 |
+
value: 84.062
|
341 |
- type: map_at_3
|
342 |
+
value: 56.821
|
343 |
- type: map_at_5
|
344 |
+
value: 71.474
|
345 |
- type: mrr_at_1
|
346 |
+
value: 91.55
|
347 |
- type: mrr_at_10
|
348 |
+
value: 94.109
|
349 |
- type: mrr_at_100
|
350 |
+
value: 94.182
|
351 |
- type: mrr_at_1000
|
352 |
+
value: 94.18299999999999
|
353 |
- type: mrr_at_3
|
354 |
+
value: 93.833
|
355 |
- type: mrr_at_5
|
356 |
+
value: 94.041
|
357 |
- type: ndcg_at_1
|
358 |
+
value: 91.55
|
359 |
- type: ndcg_at_10
|
360 |
+
value: 88.24300000000001
|
361 |
- type: ndcg_at_100
|
362 |
+
value: 90.928
|
363 |
- type: ndcg_at_1000
|
364 |
+
value: 91.221
|
365 |
- type: ndcg_at_3
|
366 |
+
value: 87.558
|
367 |
- type: ndcg_at_5
|
368 |
+
value: 86.39099999999999
|
369 |
- type: precision_at_1
|
370 |
+
value: 91.55
|
371 |
- type: precision_at_10
|
372 |
+
value: 41.959999999999994
|
373 |
- type: precision_at_100
|
374 |
+
value: 4.812
|
375 |
- type: precision_at_1000
|
376 |
+
value: 0.48900000000000005
|
377 |
- type: precision_at_3
|
378 |
+
value: 78.38300000000001
|
379 |
- type: precision_at_5
|
380 |
+
value: 66.02
|
381 |
- type: recall_at_1
|
382 |
+
value: 26.511000000000003
|
383 |
- type: recall_at_10
|
384 |
+
value: 88.98
|
385 |
- type: recall_at_100
|
386 |
+
value: 97.941
|
387 |
- type: recall_at_1000
|
388 |
+
value: 99.367
|
389 |
- type: recall_at_3
|
390 |
+
value: 58.813
|
391 |
- type: recall_at_5
|
392 |
+
value: 75.69500000000001
|
393 |
- task:
|
394 |
type: Retrieval
|
395 |
dataset:
|
|
|
400 |
revision: None
|
401 |
metrics:
|
402 |
- type: map_at_1
|
403 |
+
value: 52.7
|
404 |
- type: map_at_10
|
405 |
+
value: 62.28399999999999
|
406 |
- type: map_at_100
|
407 |
+
value: 62.827
|
408 |
- type: map_at_1000
|
409 |
+
value: 62.842
|
410 |
- type: map_at_3
|
411 |
+
value: 59.917
|
412 |
- type: map_at_5
|
413 |
+
value: 61.327
|
414 |
- type: mrr_at_1
|
415 |
+
value: 52.7
|
416 |
- type: mrr_at_10
|
417 |
+
value: 62.28399999999999
|
418 |
- type: mrr_at_100
|
419 |
+
value: 62.827
|
420 |
- type: mrr_at_1000
|
421 |
+
value: 62.842
|
422 |
- type: mrr_at_3
|
423 |
+
value: 59.917
|
424 |
- type: mrr_at_5
|
425 |
+
value: 61.327
|
426 |
- type: ndcg_at_1
|
427 |
+
value: 52.7
|
428 |
- type: ndcg_at_10
|
429 |
+
value: 67.128
|
430 |
- type: ndcg_at_100
|
431 |
+
value: 69.74900000000001
|
432 |
- type: ndcg_at_1000
|
433 |
+
value: 70.108
|
434 |
- type: ndcg_at_3
|
435 |
+
value: 62.251
|
436 |
- type: ndcg_at_5
|
437 |
+
value: 64.84100000000001
|
438 |
- type: precision_at_1
|
439 |
+
value: 52.7
|
440 |
- type: precision_at_10
|
441 |
+
value: 8.24
|
442 |
- type: precision_at_100
|
443 |
+
value: 0.946
|
444 |
- type: precision_at_1000
|
445 |
value: 0.097
|
446 |
- type: precision_at_3
|
447 |
+
value: 23.0
|
448 |
- type: precision_at_5
|
449 |
+
value: 15.079999999999998
|
450 |
- type: recall_at_1
|
451 |
+
value: 52.7
|
452 |
- type: recall_at_10
|
453 |
+
value: 82.39999999999999
|
454 |
- type: recall_at_100
|
455 |
+
value: 94.6
|
456 |
- type: recall_at_1000
|
457 |
+
value: 97.39999999999999
|
458 |
- type: recall_at_3
|
459 |
+
value: 69.0
|
460 |
- type: recall_at_5
|
461 |
+
value: 75.4
|
462 |
- task:
|
463 |
type: Classification
|
464 |
dataset:
|
|
|
469 |
revision: None
|
470 |
metrics:
|
471 |
- type: accuracy
|
472 |
+
value: 52.751058099268946
|
473 |
- type: f1
|
474 |
+
value: 42.08257079453902
|
475 |
- task:
|
476 |
type: Classification
|
477 |
dataset:
|
|
|
482 |
revision: None
|
483 |
metrics:
|
484 |
- type: accuracy
|
485 |
+
value: 88.29268292682926
|
486 |
- type: ap
|
487 |
+
value: 58.92380933786006
|
488 |
- type: f1
|
489 |
+
value: 83.38194360730576
|
490 |
- task:
|
491 |
type: STS
|
492 |
dataset:
|
|
|
497 |
revision: None
|
498 |
metrics:
|
499 |
- type: cos_sim_pearson
|
500 |
+
value: 74.20476238217833
|
501 |
- type: cos_sim_spearman
|
502 |
+
value: 79.30229178361162
|
503 |
- type: euclidean_pearson
|
504 |
+
value: 79.24335190560299
|
505 |
- type: euclidean_spearman
|
506 |
+
value: 79.30229178105364
|
507 |
- type: manhattan_pearson
|
508 |
+
value: 79.22468300467371
|
509 |
- type: manhattan_spearman
|
510 |
+
value: 79.29290711369052
|
511 |
- task:
|
512 |
type: Reranking
|
513 |
dataset:
|
|
|
518 |
revision: None
|
519 |
metrics:
|
520 |
- type: map
|
521 |
+
value: 31.85453315055195
|
522 |
- type: mrr
|
523 |
+
value: 30.61468253968254
|
524 |
- task:
|
525 |
type: Retrieval
|
526 |
dataset:
|
|
|
531 |
revision: None
|
532 |
metrics:
|
533 |
- type: map_at_1
|
534 |
+
value: 66.671
|
535 |
- type: map_at_10
|
536 |
+
value: 75.656
|
537 |
- type: map_at_100
|
538 |
+
value: 75.978
|
539 |
- type: map_at_1000
|
540 |
+
value: 75.99000000000001
|
541 |
- type: map_at_3
|
542 |
+
value: 73.80499999999999
|
543 |
- type: map_at_5
|
544 |
+
value: 75.023
|
545 |
- type: mrr_at_1
|
546 |
+
value: 68.95400000000001
|
547 |
- type: mrr_at_10
|
548 |
+
value: 76.25
|
549 |
- type: mrr_at_100
|
550 |
+
value: 76.534
|
551 |
- type: mrr_at_1000
|
552 |
+
value: 76.545
|
553 |
- type: mrr_at_3
|
554 |
+
value: 74.632
|
555 |
- type: mrr_at_5
|
556 |
+
value: 75.69500000000001
|
557 |
- type: ndcg_at_1
|
558 |
+
value: 68.95400000000001
|
559 |
- type: ndcg_at_10
|
560 |
+
value: 79.293
|
561 |
- type: ndcg_at_100
|
562 |
+
value: 80.709
|
563 |
- type: ndcg_at_1000
|
564 |
+
value: 81.00500000000001
|
565 |
- type: ndcg_at_3
|
566 |
+
value: 75.815
|
567 |
- type: ndcg_at_5
|
568 |
+
value: 77.861
|
569 |
- type: precision_at_1
|
570 |
+
value: 68.95400000000001
|
571 |
- type: precision_at_10
|
572 |
+
value: 9.559
|
573 |
- type: precision_at_100
|
574 |
+
value: 1.026
|
575 |
- type: precision_at_1000
|
576 |
value: 0.105
|
577 |
- type: precision_at_3
|
578 |
+
value: 28.486
|
579 |
- type: precision_at_5
|
580 |
+
value: 18.178
|
581 |
- type: recall_at_1
|
582 |
+
value: 66.671
|
583 |
- type: recall_at_10
|
584 |
+
value: 89.904
|
585 |
- type: recall_at_100
|
586 |
+
value: 96.243
|
587 |
- type: recall_at_1000
|
588 |
+
value: 98.55199999999999
|
589 |
- type: recall_at_3
|
590 |
+
value: 80.778
|
591 |
- type: recall_at_5
|
592 |
+
value: 85.611
|
593 |
- task:
|
594 |
type: Classification
|
595 |
dataset:
|
|
|
600 |
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
601 |
metrics:
|
602 |
- type: accuracy
|
603 |
+
value: 77.64290517821115
|
604 |
- type: f1
|
605 |
+
value: 74.45829057694098
|
606 |
- task:
|
607 |
type: Classification
|
608 |
dataset:
|
|
|
613 |
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
614 |
metrics:
|
615 |
- type: accuracy
|
616 |
+
value: 85.09751176866173
|
617 |
- type: f1
|
618 |
+
value: 84.27719445179089
|
619 |
- task:
|
620 |
type: Retrieval
|
621 |
dataset:
|
|
|
626 |
revision: None
|
627 |
metrics:
|
628 |
- type: map_at_1
|
629 |
+
value: 54.7
|
630 |
- type: map_at_10
|
631 |
+
value: 61.422
|
632 |
- type: map_at_100
|
633 |
+
value: 61.870999999999995
|
634 |
- type: map_at_1000
|
635 |
+
value: 61.917
|
636 |
- type: map_at_3
|
637 |
+
value: 59.833000000000006
|
638 |
- type: map_at_5
|
639 |
+
value: 60.663
|
640 |
- type: mrr_at_1
|
641 |
+
value: 54.900000000000006
|
642 |
- type: mrr_at_10
|
643 |
+
value: 61.539
|
644 |
- type: mrr_at_100
|
645 |
+
value: 61.988
|
646 |
- type: mrr_at_1000
|
647 |
+
value: 62.034
|
648 |
- type: mrr_at_3
|
649 |
+
value: 59.95
|
650 |
- type: mrr_at_5
|
651 |
+
value: 60.78
|
652 |
- type: ndcg_at_1
|
653 |
+
value: 54.7
|
654 |
- type: ndcg_at_10
|
655 |
+
value: 64.816
|
656 |
- type: ndcg_at_100
|
657 |
+
value: 67.27499999999999
|
658 |
- type: ndcg_at_1000
|
659 |
+
value: 68.518
|
660 |
- type: ndcg_at_3
|
661 |
+
value: 61.446999999999996
|
662 |
- type: ndcg_at_5
|
663 |
+
value: 62.937
|
664 |
- type: precision_at_1
|
665 |
+
value: 54.7
|
666 |
- type: precision_at_10
|
667 |
+
value: 7.5600000000000005
|
668 |
- type: precision_at_100
|
669 |
+
value: 0.878
|
670 |
- type: precision_at_1000
|
671 |
value: 0.098
|
672 |
- type: precision_at_3
|
673 |
+
value: 22.033
|
674 |
- type: precision_at_5
|
675 |
+
value: 13.94
|
676 |
- type: recall_at_1
|
677 |
+
value: 54.7
|
678 |
- type: recall_at_10
|
679 |
+
value: 75.6
|
680 |
- type: recall_at_100
|
681 |
+
value: 87.8
|
682 |
- type: recall_at_1000
|
683 |
+
value: 97.6
|
684 |
- type: recall_at_3
|
685 |
+
value: 66.10000000000001
|
686 |
- type: recall_at_5
|
687 |
+
value: 69.69999999999999
|
688 |
- task:
|
689 |
type: Classification
|
690 |
dataset:
|
|
|
695 |
revision: None
|
696 |
metrics:
|
697 |
- type: accuracy
|
698 |
+
value: 78.61666666666667
|
699 |
- type: f1
|
700 |
+
value: 78.46001064447016
|
701 |
- task:
|
702 |
type: PairClassification
|
703 |
dataset:
|
|
|
708 |
revision: None
|
709 |
metrics:
|
710 |
- type: cos_sim_accuracy
|
711 |
+
value: 83.48673524634542
|
712 |
- type: cos_sim_ap
|
713 |
+
value: 86.97066512426397
|
714 |
- type: cos_sim_f1
|
715 |
+
value: 84.4467108618052
|
716 |
- type: cos_sim_precision
|
717 |
+
value: 81.65680473372781
|
718 |
- type: cos_sim_recall
|
719 |
+
value: 87.43400211193241
|
720 |
- type: dot_accuracy
|
721 |
+
value: 83.48673524634542
|
722 |
- type: dot_ap
|
723 |
+
value: 86.97070037115512
|
724 |
- type: dot_f1
|
725 |
+
value: 84.4467108618052
|
726 |
- type: dot_precision
|
727 |
+
value: 81.65680473372781
|
728 |
- type: dot_recall
|
729 |
+
value: 87.43400211193241
|
730 |
- type: euclidean_accuracy
|
731 |
+
value: 83.48673524634542
|
732 |
- type: euclidean_ap
|
733 |
+
value: 86.97066512426397
|
734 |
- type: euclidean_f1
|
735 |
+
value: 84.4467108618052
|
736 |
- type: euclidean_precision
|
737 |
+
value: 81.65680473372781
|
738 |
- type: euclidean_recall
|
739 |
+
value: 87.43400211193241
|
740 |
- type: manhattan_accuracy
|
741 |
+
value: 83.27016783974011
|
742 |
- type: manhattan_ap
|
743 |
+
value: 86.97839108799026
|
744 |
- type: manhattan_f1
|
745 |
+
value: 84.24273329933708
|
746 |
- type: manhattan_precision
|
747 |
+
value: 81.4595660749507
|
748 |
- type: manhattan_recall
|
749 |
+
value: 87.22280887011615
|
750 |
- type: max_accuracy
|
751 |
+
value: 83.48673524634542
|
752 |
- type: max_ap
|
753 |
+
value: 86.97839108799026
|
754 |
- type: max_f1
|
755 |
+
value: 84.4467108618052
|
756 |
- task:
|
757 |
type: Classification
|
758 |
dataset:
|
|
|
763 |
revision: None
|
764 |
metrics:
|
765 |
- type: accuracy
|
766 |
+
value: 94.58
|
767 |
- type: ap
|
768 |
+
value: 92.67235771989334
|
769 |
- type: f1
|
770 |
+
value: 94.56749048144864
|
771 |
- task:
|
772 |
type: STS
|
773 |
dataset:
|
|
|
778 |
revision: None
|
779 |
metrics:
|
780 |
- type: cos_sim_pearson
|
781 |
+
value: 41.13075780508077
|
782 |
- type: cos_sim_spearman
|
783 |
+
value: 46.23023927864047
|
784 |
- type: euclidean_pearson
|
785 |
+
value: 45.8745816995021
|
786 |
- type: euclidean_spearman
|
787 |
+
value: 46.230234996511186
|
788 |
- type: manhattan_pearson
|
789 |
+
value: 45.87257756266397
|
790 |
- type: manhattan_spearman
|
791 |
+
value: 46.23023501384774
|
792 |
- task:
|
793 |
type: STS
|
794 |
dataset:
|
|
|
799 |
revision: None
|
800 |
metrics:
|
801 |
- type: cos_sim_pearson
|
802 |
+
value: 44.584801951997676
|
803 |
- type: cos_sim_spearman
|
804 |
+
value: 45.80390449641642
|
805 |
- type: euclidean_pearson
|
806 |
+
value: 41.235476712471055
|
807 |
- type: euclidean_spearman
|
808 |
+
value: 45.80391504205642
|
809 |
- type: manhattan_pearson
|
810 |
+
value: 41.282727075778766
|
811 |
- type: manhattan_spearman
|
812 |
+
value: 45.80885691191199
|
813 |
- task:
|
814 |
type: STS
|
815 |
dataset:
|
|
|
820 |
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
821 |
metrics:
|
822 |
- type: cos_sim_pearson
|
823 |
+
value: 60.07699182332446
|
824 |
- type: cos_sim_spearman
|
825 |
+
value: 61.30742120893451
|
826 |
- type: euclidean_pearson
|
827 |
+
value: 57.975507370373805
|
828 |
- type: euclidean_spearman
|
829 |
+
value: 61.30742120893451
|
830 |
- type: manhattan_pearson
|
831 |
+
value: 57.981532129657566
|
832 |
- type: manhattan_spearman
|
833 |
+
value: 61.35516394120813
|
834 |
- task:
|
835 |
type: STS
|
836 |
dataset:
|
|
|
841 |
revision: None
|
842 |
metrics:
|
843 |
- type: cos_sim_pearson
|
844 |
+
value: 77.33873897664922
|
845 |
- type: cos_sim_spearman
|
846 |
+
value: 78.48046279063745
|
847 |
- type: euclidean_pearson
|
848 |
+
value: 78.22561405005021
|
849 |
- type: euclidean_spearman
|
850 |
+
value: 78.48054253550603
|
851 |
- type: manhattan_pearson
|
852 |
+
value: 78.15799842348594
|
853 |
- type: manhattan_spearman
|
854 |
+
value: 78.4163953888659
|
855 |
- task:
|
856 |
type: Reranking
|
857 |
dataset:
|
|
|
862 |
revision: None
|
863 |
metrics:
|
864 |
- type: map
|
865 |
+
value: 66.75988051767987
|
866 |
- type: mrr
|
867 |
+
value: 77.18975346852801
|
868 |
- task:
|
869 |
type: Retrieval
|
870 |
dataset:
|
|
|
875 |
revision: None
|
876 |
metrics:
|
877 |
- type: map_at_1
|
878 |
+
value: 26.873
|
879 |
- type: map_at_10
|
880 |
+
value: 75.21900000000001
|
881 |
- type: map_at_100
|
882 |
+
value: 78.94200000000001
|
883 |
- type: map_at_1000
|
884 |
+
value: 79.01599999999999
|
885 |
- type: map_at_3
|
886 |
+
value: 52.885000000000005
|
887 |
- type: map_at_5
|
888 |
+
value: 65.062
|
889 |
- type: mrr_at_1
|
890 |
+
value: 88.646
|
891 |
- type: mrr_at_10
|
892 |
+
value: 91.604
|
893 |
- type: mrr_at_100
|
894 |
+
value: 91.69500000000001
|
895 |
- type: mrr_at_1000
|
896 |
+
value: 91.69800000000001
|
897 |
- type: mrr_at_3
|
898 |
+
value: 91.115
|
899 |
- type: mrr_at_5
|
900 |
+
value: 91.444
|
901 |
- type: ndcg_at_1
|
902 |
+
value: 88.646
|
903 |
- type: ndcg_at_10
|
904 |
+
value: 83.19800000000001
|
905 |
- type: ndcg_at_100
|
906 |
+
value: 87.04899999999999
|
907 |
- type: ndcg_at_1000
|
908 |
+
value: 87.754
|
909 |
- type: ndcg_at_3
|
910 |
+
value: 84.63199999999999
|
911 |
- type: ndcg_at_5
|
912 |
+
value: 83.295
|
913 |
- type: precision_at_1
|
914 |
+
value: 88.646
|
915 |
- type: precision_at_10
|
916 |
+
value: 41.339
|
917 |
- type: precision_at_100
|
918 |
+
value: 4.977
|
919 |
- type: precision_at_1000
|
920 |
+
value: 0.515
|
921 |
- type: precision_at_3
|
922 |
+
value: 74.009
|
923 |
- type: precision_at_5
|
924 |
+
value: 62.104000000000006
|
925 |
- type: recall_at_1
|
926 |
+
value: 26.873
|
927 |
- type: recall_at_10
|
928 |
+
value: 82.268
|
929 |
- type: recall_at_100
|
930 |
+
value: 94.675
|
931 |
- type: recall_at_1000
|
932 |
+
value: 98.226
|
933 |
- type: recall_at_3
|
934 |
+
value: 54.761
|
935 |
- type: recall_at_5
|
936 |
+
value: 68.905
|
937 |
- task:
|
938 |
type: Classification
|
939 |
dataset:
|
|
|
944 |
revision: None
|
945 |
metrics:
|
946 |
- type: accuracy
|
947 |
+
value: 54.498000000000005
|
948 |
- type: f1
|
949 |
+
value: 52.67480963825165
|
950 |
- task:
|
951 |
type: Clustering
|
952 |
dataset:
|
|
|
957 |
revision: None
|
958 |
metrics:
|
959 |
- type: v_measure
|
960 |
+
value: 71.20219333478684
|
961 |
- task:
|
962 |
type: Clustering
|
963 |
dataset:
|
|
|
968 |
revision: None
|
969 |
metrics:
|
970 |
- type: v_measure
|
971 |
+
value: 68.2649587922088
|
972 |
- task:
|
973 |
type: Retrieval
|
974 |
dataset:
|
|
|
979 |
revision: None
|
980 |
metrics:
|
981 |
- type: map_at_1
|
982 |
+
value: 56.39999999999999
|
983 |
- type: map_at_10
|
984 |
+
value: 66.245
|
985 |
- type: map_at_100
|
986 |
+
value: 66.838
|
987 |
- type: map_at_1000
|
988 |
+
value: 66.849
|
989 |
- type: map_at_3
|
990 |
+
value: 64.533
|
991 |
- type: map_at_5
|
992 |
+
value: 65.593
|
993 |
- type: mrr_at_1
|
994 |
+
value: 56.39999999999999
|
995 |
- type: mrr_at_10
|
996 |
+
value: 66.245
|
997 |
- type: mrr_at_100
|
998 |
+
value: 66.838
|
999 |
- type: mrr_at_1000
|
1000 |
+
value: 66.849
|
1001 |
- type: mrr_at_3
|
1002 |
+
value: 64.533
|
1003 |
- type: mrr_at_5
|
1004 |
+
value: 65.593
|
1005 |
- type: ndcg_at_1
|
1006 |
+
value: 56.39999999999999
|
1007 |
- type: ndcg_at_10
|
1008 |
+
value: 70.575
|
1009 |
- type: ndcg_at_100
|
1010 |
+
value: 73.324
|
1011 |
- type: ndcg_at_1000
|
1012 |
+
value: 73.617
|
1013 |
- type: ndcg_at_3
|
1014 |
+
value: 67.147
|
1015 |
- type: ndcg_at_5
|
1016 |
+
value: 69.05
|
1017 |
- type: precision_at_1
|
1018 |
+
value: 56.39999999999999
|
1019 |
- type: precision_at_10
|
1020 |
+
value: 8.39
|
1021 |
- type: precision_at_100
|
1022 |
+
value: 0.964
|
1023 |
- type: precision_at_1000
|
1024 |
value: 0.099
|
1025 |
- type: precision_at_3
|
1026 |
+
value: 24.9
|
1027 |
- type: precision_at_5
|
1028 |
+
value: 15.86
|
1029 |
- type: recall_at_1
|
1030 |
+
value: 56.39999999999999
|
1031 |
- type: recall_at_10
|
1032 |
+
value: 83.89999999999999
|
1033 |
- type: recall_at_100
|
1034 |
+
value: 96.39999999999999
|
1035 |
- type: recall_at_1000
|
1036 |
+
value: 98.7
|
1037 |
- type: recall_at_3
|
1038 |
+
value: 74.7
|
1039 |
- type: recall_at_5
|
1040 |
+
value: 79.3
|
1041 |
- task:
|
1042 |
type: Classification
|
1043 |
dataset:
|
|
|
1048 |
revision: None
|
1049 |
metrics:
|
1050 |
- type: accuracy
|
1051 |
+
value: 89.63000000000001
|
1052 |
- type: ap
|
1053 |
+
value: 75.78836247276601
|
1054 |
- type: f1
|
1055 |
+
value: 88.24687781823513
|
1056 |
---
|
1057 |
|
1058 |
### 使用方法
|