[update] README.md
Browse files
README.md
CHANGED
@@ -6,7 +6,7 @@ tags:
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- sentence-similarity
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- mteb
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model-index:
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-
- name: tao
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results:
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- task:
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type: STS
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@@ -18,17 +18,17 @@ 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: 47.
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- type: cos_sim_spearman
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-
value: 49.
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- type: euclidean_pearson
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-
value: 48.
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- type: euclidean_spearman
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-
value: 49.
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- type: manhattan_pearson
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-
value: 48.
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- type: manhattan_spearman
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-
value: 49.
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- task:
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type: STS
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dataset:
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@@ -39,17 +39,17 @@ 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: 50.
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- type: cos_sim_spearman
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-
value: 53.
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- type: euclidean_pearson
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-
value: 55.
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- type: euclidean_spearman
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-
value: 53.
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- type: manhattan_pearson
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-
value: 55.
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- type: manhattan_spearman
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-
value: 53.
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- task:
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type: Classification
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dataset:
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@@ -60,9 +60,9 @@ model-index:
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d
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metrics:
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- type: accuracy
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63 |
-
value: 40.
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- type: f1
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-
value: 39.
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- task:
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type: STS
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dataset:
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@@ -73,17 +73,17 @@ 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: 62.
|
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- type: cos_sim_spearman
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-
value: 65.
|
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- type: euclidean_pearson
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-
value: 64.
|
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- type: euclidean_spearman
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-
value: 65.
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- type: manhattan_pearson
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-
value: 64.
|
85 |
- type: manhattan_spearman
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-
value: 65.
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- task:
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type: Clustering
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dataset:
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@@ -94,7 +94,7 @@ 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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@@ -105,7 +105,7 @@ 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: 38.
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- task:
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type: Reranking
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dataset:
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@@ -129,9 +129,9 @@ model-index:
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revision: None
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metrics:
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- type: map
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-
value: 85.
|
133 |
- type: mrr
|
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-
value: 88.
|
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- task:
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type: Retrieval
|
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dataset:
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@@ -142,65 +142,65 @@ 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
|
147 |
-
value: 36.
|
148 |
- type: map_at_100
|
149 |
-
value: 38.
|
150 |
- type: map_at_1000
|
151 |
-
value: 38.
|
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- type: map_at_3
|
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-
value: 32.
|
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- type: map_at_5
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-
value: 34.
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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: 45.
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- type: mrr_at_100
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-
value: 46.
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- type: mrr_at_1000
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-
value: 46.
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- type: mrr_at_3
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-
value: 42.
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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: 42.
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- type: ndcg_at_100
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-
value: 50.
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- type: ndcg_at_1000
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-
value: 52.
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- type: ndcg_at_3
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-
value: 37.
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- type: ndcg_at_5
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-
value: 39.
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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: 9.
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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: 0.183
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- type: precision_at_3
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-
value: 21.
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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:
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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: 98.
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- type: recall_at_3
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-
value: 37.
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- type: recall_at_5
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-
value:
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- task:
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type: PairClassification
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dataset:
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@@ -213,7 +213,7 @@ model-index:
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- type: cos_sim_accuracy
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value: 77.71497294046902
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- type: cos_sim_ap
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-
value: 86.
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- type: cos_sim_f1
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value: 79.31987247608926
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- type: cos_sim_precision
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@@ -223,7 +223,7 @@ model-index:
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- type: dot_accuracy
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value: 77.71497294046902
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- type: dot_ap
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-
value: 86.
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- type: dot_f1
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value: 79.31987247608926
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- type: dot_precision
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@@ -233,7 +233,7 @@ model-index:
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- type: euclidean_accuracy
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value: 77.71497294046902
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- type: euclidean_ap
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-
value: 86.
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- type: euclidean_f1
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value: 79.31987247608926
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- type: euclidean_precision
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@@ -243,19 +243,19 @@ model-index:
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- type: manhattan_accuracy
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value: 77.8111846061335
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- type: manhattan_ap
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-
value: 86.
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- type: manhattan_f1
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-
value: 79.
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- type: manhattan_precision
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-
value: 72.
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- type: manhattan_recall
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value: 87.74842179097499
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- type: max_accuracy
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value: 77.8111846061335
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- type: max_ap
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-
value: 86.
|
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- type: max_f1
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-
value: 79.
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- task:
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type: Retrieval
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dataset:
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@@ -266,65 +266,65 @@ 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: 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: 17.
|
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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:
|
@@ -335,65 +335,65 @@ model-index:
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|
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revision: None
|
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metrics:
|
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- type: map_at_1
|
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-
value: 25.
|
339 |
- type: map_at_10
|
340 |
-
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:
|
377 |
- type: precision_at_100
|
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-
value: 4.
|
379 |
- type: precision_at_1000
|
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value: 0.48900000000000005
|
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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: 25.
|
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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: 97.
|
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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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@@ -404,65 +404,65 @@ 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:
|
444 |
- type: precision_at_10
|
445 |
-
value:
|
446 |
- type: precision_at_100
|
447 |
-
value: 0.
|
448 |
- type: precision_at_1000
|
449 |
-
value: 0.
|
450 |
- type: precision_at_3
|
451 |
-
value:
|
452 |
- type: precision_at_5
|
453 |
-
value: 14.
|
454 |
- type: recall_at_1
|
455 |
-
value:
|
456 |
- type: recall_at_10
|
457 |
-
value:
|
458 |
- type: recall_at_100
|
459 |
-
value:
|
460 |
- type: recall_at_1000
|
461 |
-
value: 96.
|
462 |
- type: recall_at_3
|
463 |
-
value:
|
464 |
- type: recall_at_5
|
465 |
-
value:
|
466 |
- task:
|
467 |
type: Classification
|
468 |
dataset:
|
@@ -473,9 +473,9 @@ model-index:
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|
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revision: None
|
474 |
metrics:
|
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- type: accuracy
|
476 |
-
value:
|
477 |
- type: f1
|
478 |
-
value: 35.
|
479 |
- task:
|
480 |
type: Classification
|
481 |
dataset:
|
@@ -501,17 +501,17 @@ model-index:
|
|
501 |
revision: None
|
502 |
metrics:
|
503 |
- type: cos_sim_pearson
|
504 |
-
value: 71.
|
505 |
- type: cos_sim_spearman
|
506 |
-
value: 77.
|
507 |
- type: euclidean_pearson
|
508 |
-
value: 76.
|
509 |
- type: euclidean_spearman
|
510 |
-
value: 77.
|
511 |
- type: manhattan_pearson
|
512 |
-
value: 76.
|
513 |
- type: manhattan_spearman
|
514 |
-
value: 77.
|
515 |
- task:
|
516 |
type: Reranking
|
517 |
dataset:
|
@@ -522,7 +522,7 @@ model-index:
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|
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revision: None
|
523 |
metrics:
|
524 |
- type: map
|
525 |
-
value: 27.
|
526 |
- type: mrr
|
527 |
value: 26.92023809523809
|
528 |
- task:
|
@@ -535,65 +535,65 @@ model-index:
|
|
535 |
revision: None
|
536 |
metrics:
|
537 |
- type: map_at_1
|
538 |
-
value: 66.
|
539 |
- type: map_at_10
|
540 |
-
value: 75.
|
541 |
- type: map_at_100
|
542 |
-
value:
|
543 |
- type: map_at_1000
|
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-
value:
|
545 |
- 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:
|
553 |
- type: mrr_at_100
|
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-
value:
|
555 |
- type: mrr_at_1000
|
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-
value:
|
557 |
- type: mrr_at_3
|
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-
value:
|
559 |
- type: mrr_at_5
|
560 |
-
value: 75.
|
561 |
- type: ndcg_at_1
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-
value:
|
563 |
- type: ndcg_at_10
|
564 |
-
value:
|
565 |
- type: ndcg_at_100
|
566 |
-
value: 80.
|
567 |
- type: ndcg_at_1000
|
568 |
-
value:
|
569 |
- type: ndcg_at_3
|
570 |
-
value:
|
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- type: ndcg_at_5
|
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-
value:
|
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- type: precision_at_1
|
574 |
-
value:
|
575 |
- type: precision_at_10
|
576 |
-
value: 9.
|
577 |
- type: precision_at_100
|
578 |
-
value: 1.
|
579 |
- type: precision_at_1000
|
580 |
value: 0.105
|
581 |
- type: precision_at_3
|
582 |
-
value: 28.
|
583 |
- type: precision_at_5
|
584 |
-
value:
|
585 |
- type: recall_at_1
|
586 |
-
value: 66.
|
587 |
- type: recall_at_10
|
588 |
-
value:
|
589 |
- type: recall_at_100
|
590 |
-
value: 96.
|
591 |
- type: recall_at_1000
|
592 |
-
value: 98.
|
593 |
- type: recall_at_3
|
594 |
-
value:
|
595 |
- type: recall_at_5
|
596 |
-
value:
|
597 |
- task:
|
598 |
type: Classification
|
599 |
dataset:
|
@@ -604,9 +604,9 @@ model-index:
|
|
604 |
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
605 |
metrics:
|
606 |
- type: accuracy
|
607 |
-
value: 68.
|
608 |
- type: f1
|
609 |
-
value: 65.
|
610 |
- task:
|
611 |
type: Classification
|
612 |
dataset:
|
@@ -617,9 +617,9 @@ model-index:
|
|
617 |
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
618 |
metrics:
|
619 |
- type: accuracy
|
620 |
-
value: 73.
|
621 |
- type: f1
|
622 |
-
value: 72.
|
623 |
- task:
|
624 |
type: Retrieval
|
625 |
dataset:
|
@@ -630,65 +630,65 @@ model-index:
|
|
630 |
revision: None
|
631 |
metrics:
|
632 |
- type: map_at_1
|
633 |
-
value:
|
634 |
- type: map_at_10
|
635 |
-
value:
|
636 |
- type: map_at_100
|
637 |
-
value:
|
638 |
- type: map_at_1000
|
639 |
-
value:
|
640 |
- type: map_at_3
|
641 |
-
value:
|
642 |
- type: map_at_5
|
643 |
-
value:
|
644 |
- type: mrr_at_1
|
645 |
-
value:
|
646 |
- type: mrr_at_10
|
647 |
-
value:
|
648 |
- type: mrr_at_100
|
649 |
-
value:
|
650 |
- type: mrr_at_1000
|
651 |
-
value:
|
652 |
- type: mrr_at_3
|
653 |
-
value:
|
654 |
- type: mrr_at_5
|
655 |
-
value:
|
656 |
- type: ndcg_at_1
|
657 |
-
value:
|
658 |
- type: ndcg_at_10
|
659 |
-
value:
|
660 |
- type: ndcg_at_100
|
661 |
-
value:
|
662 |
- type: ndcg_at_1000
|
663 |
-
value: 64.
|
664 |
- type: ndcg_at_3
|
665 |
-
value:
|
666 |
- type: ndcg_at_5
|
667 |
-
value:
|
668 |
- type: precision_at_1
|
669 |
-
value:
|
670 |
- type: precision_at_10
|
671 |
-
value: 6.
|
672 |
- type: precision_at_100
|
673 |
-
value: 0.
|
674 |
- type: precision_at_1000
|
675 |
value: 0.096
|
676 |
- type: precision_at_3
|
677 |
-
value: 20.
|
678 |
- type: precision_at_5
|
679 |
-
value:
|
680 |
- type: recall_at_1
|
681 |
-
value:
|
682 |
- type: recall_at_10
|
683 |
-
value:
|
684 |
- type: recall_at_100
|
685 |
-
value:
|
686 |
- type: recall_at_1000
|
687 |
-
value: 95.
|
688 |
- type: recall_at_3
|
689 |
-
value:
|
690 |
- type: recall_at_5
|
691 |
-
value:
|
692 |
- task:
|
693 |
type: Classification
|
694 |
dataset:
|
@@ -699,9 +699,9 @@ model-index:
|
|
699 |
revision: None
|
700 |
metrics:
|
701 |
- type: accuracy
|
702 |
-
value: 73.
|
703 |
- type: f1
|
704 |
-
value: 72.
|
705 |
- task:
|
706 |
type: PairClassification
|
707 |
dataset:
|
@@ -714,37 +714,37 @@ model-index:
|
|
714 |
- type: cos_sim_accuracy
|
715 |
value: 73.36220898754738
|
716 |
- type: cos_sim_ap
|
717 |
-
value: 78.
|
718 |
- type: cos_sim_f1
|
719 |
-
value: 75.
|
720 |
- type: cos_sim_precision
|
721 |
-
value:
|
722 |
- type: cos_sim_recall
|
723 |
-
value:
|
724 |
- type: dot_accuracy
|
725 |
value: 73.36220898754738
|
726 |
- type: dot_ap
|
727 |
-
value: 78.
|
728 |
- type: dot_f1
|
729 |
-
value: 75.
|
730 |
- type: dot_precision
|
731 |
-
value:
|
732 |
- type: dot_recall
|
733 |
-
value:
|
734 |
- type: euclidean_accuracy
|
735 |
value: 73.36220898754738
|
736 |
- type: euclidean_ap
|
737 |
-
value: 78.
|
738 |
- type: euclidean_f1
|
739 |
-
value: 75.
|
740 |
- type: euclidean_precision
|
741 |
-
value:
|
742 |
- type: euclidean_recall
|
743 |
-
value:
|
744 |
- type: manhattan_accuracy
|
745 |
value: 73.09149972929075
|
746 |
- type: manhattan_ap
|
747 |
-
value: 78.
|
748 |
- type: manhattan_f1
|
749 |
value: 75.3623188405797
|
750 |
- type: manhattan_precision
|
@@ -754,9 +754,9 @@ model-index:
|
|
754 |
- type: max_accuracy
|
755 |
value: 73.36220898754738
|
756 |
- type: max_ap
|
757 |
-
value: 78.
|
758 |
- type: max_f1
|
759 |
-
value: 75.
|
760 |
- task:
|
761 |
type: Classification
|
762 |
dataset:
|
@@ -767,11 +767,11 @@ model-index:
|
|
767 |
revision: None
|
768 |
metrics:
|
769 |
- type: accuracy
|
770 |
-
value: 91.
|
771 |
- type: ap
|
772 |
-
value: 89.
|
773 |
- type: f1
|
774 |
-
value: 91.
|
775 |
- task:
|
776 |
type: STS
|
777 |
dataset:
|
@@ -782,17 +782,17 @@ model-index:
|
|
782 |
revision: None
|
783 |
metrics:
|
784 |
- type: cos_sim_pearson
|
785 |
-
value: 30.
|
786 |
- type: cos_sim_spearman
|
787 |
-
value: 36.
|
788 |
- type: euclidean_pearson
|
789 |
-
value: 36.
|
790 |
- type: euclidean_spearman
|
791 |
-
value: 36.
|
792 |
- type: manhattan_pearson
|
793 |
-
value: 36.
|
794 |
- type: manhattan_spearman
|
795 |
-
value: 36.
|
796 |
- task:
|
797 |
type: STS
|
798 |
dataset:
|
@@ -803,17 +803,17 @@ model-index:
|
|
803 |
revision: None
|
804 |
metrics:
|
805 |
- type: cos_sim_pearson
|
806 |
-
value: 36.
|
807 |
- type: cos_sim_spearman
|
808 |
-
value: 38.
|
809 |
- type: euclidean_pearson
|
810 |
-
value: 37.
|
811 |
- type: euclidean_spearman
|
812 |
-
value: 38.
|
813 |
- type: manhattan_pearson
|
814 |
-
value: 37.
|
815 |
- type: manhattan_spearman
|
816 |
-
value: 38.
|
817 |
- task:
|
818 |
type: STS
|
819 |
dataset:
|
@@ -824,17 +824,17 @@ model-index:
|
|
824 |
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
825 |
metrics:
|
826 |
- type: cos_sim_pearson
|
827 |
-
value: 65.
|
828 |
- type: cos_sim_spearman
|
829 |
-
value: 68.
|
830 |
- type: euclidean_pearson
|
831 |
-
value: 67.
|
832 |
- type: euclidean_spearman
|
833 |
-
value: 68.
|
834 |
- type: manhattan_pearson
|
835 |
-
value: 67.
|
836 |
- type: manhattan_spearman
|
837 |
-
value: 68.
|
838 |
- task:
|
839 |
type: STS
|
840 |
dataset:
|
@@ -845,17 +845,17 @@ model-index:
|
|
845 |
revision: None
|
846 |
metrics:
|
847 |
- type: cos_sim_pearson
|
848 |
-
value: 78.
|
849 |
- type: cos_sim_spearman
|
850 |
-
value: 79.
|
851 |
- type: euclidean_pearson
|
852 |
-
value: 78.
|
853 |
- type: euclidean_spearman
|
854 |
-
value: 79.
|
855 |
- type: manhattan_pearson
|
856 |
-
value: 78.
|
857 |
- type: manhattan_spearman
|
858 |
-
value: 79.
|
859 |
- task:
|
860 |
type: Reranking
|
861 |
dataset:
|
@@ -866,9 +866,9 @@ model-index:
|
|
866 |
revision: None
|
867 |
metrics:
|
868 |
- type: map
|
869 |
-
value: 66.
|
870 |
- type: mrr
|
871 |
-
value: 76.
|
872 |
- task:
|
873 |
type: Retrieval
|
874 |
dataset:
|
@@ -879,65 +879,65 @@ model-index:
|
|
879 |
revision: None
|
880 |
metrics:
|
881 |
- type: map_at_1
|
882 |
-
value: 27.
|
883 |
- type: map_at_10
|
884 |
-
value: 77.
|
885 |
- type: map_at_100
|
886 |
-
value: 80.
|
887 |
- type: map_at_1000
|
888 |
-
value: 80.
|
889 |
- type: map_at_3
|
890 |
-
value: 54.
|
891 |
- type: map_at_5
|
892 |
-
value: 66.
|
893 |
- type: mrr_at_1
|
894 |
value: 90.049
|
895 |
- type: mrr_at_10
|
896 |
-
value: 92.
|
897 |
- type: mrr_at_100
|
898 |
-
value: 92.
|
899 |
- type: mrr_at_1000
|
900 |
-
value: 92.
|
901 |
- type: mrr_at_3
|
902 |
-
value: 92.
|
903 |
- type: mrr_at_5
|
904 |
-
value: 92.
|
905 |
- type: ndcg_at_1
|
906 |
value: 90.049
|
907 |
- type: ndcg_at_10
|
908 |
-
value: 84.
|
909 |
- type: ndcg_at_100
|
910 |
-
value: 88.
|
911 |
- type: ndcg_at_1000
|
912 |
-
value: 88.
|
913 |
- type: ndcg_at_3
|
914 |
-
value: 86.
|
915 |
- type: ndcg_at_5
|
916 |
-
value: 84.
|
917 |
- type: precision_at_1
|
918 |
value: 90.049
|
919 |
- type: precision_at_10
|
920 |
-
value: 42.
|
921 |
- type: precision_at_100
|
922 |
-
value: 5.
|
923 |
- type: precision_at_1000
|
924 |
value: 0.516
|
925 |
- type: precision_at_3
|
926 |
-
value: 75.
|
927 |
- type: precision_at_5
|
928 |
-
value: 63.
|
929 |
- type: recall_at_1
|
930 |
-
value: 27.
|
931 |
- type: recall_at_10
|
932 |
-
value: 83.
|
933 |
- type: recall_at_100
|
934 |
value: 95.21
|
935 |
- type: recall_at_1000
|
936 |
value: 98.503
|
937 |
- type: recall_at_3
|
938 |
-
value: 55.
|
939 |
- type: recall_at_5
|
940 |
-
value: 69.
|
941 |
- task:
|
942 |
type: Classification
|
943 |
dataset:
|
@@ -948,9 +948,9 @@ model-index:
|
|
948 |
revision: None
|
949 |
metrics:
|
950 |
- type: accuracy
|
951 |
-
value: 51.
|
952 |
- type: f1
|
953 |
-
value: 50.
|
954 |
- task:
|
955 |
type: Clustering
|
956 |
dataset:
|
@@ -961,7 +961,7 @@ model-index:
|
|
961 |
revision: None
|
962 |
metrics:
|
963 |
- type: v_measure
|
964 |
-
value: 60.
|
965 |
- task:
|
966 |
type: Clustering
|
967 |
dataset:
|
@@ -972,7 +972,7 @@ model-index:
|
|
972 |
revision: None
|
973 |
metrics:
|
974 |
- type: v_measure
|
975 |
-
value: 57.
|
976 |
- task:
|
977 |
type: Retrieval
|
978 |
dataset:
|
@@ -983,65 +983,65 @@ model-index:
|
|
983 |
revision: None
|
984 |
metrics:
|
985 |
- type: map_at_1
|
986 |
-
value:
|
987 |
- type: map_at_10
|
988 |
-
value:
|
989 |
- type: map_at_100
|
990 |
-
value:
|
991 |
- type: map_at_1000
|
992 |
-
value:
|
993 |
- type: map_at_3
|
994 |
-
value:
|
995 |
- type: map_at_5
|
996 |
-
value:
|
997 |
- type: mrr_at_1
|
998 |
-
value:
|
999 |
- type: mrr_at_10
|
1000 |
-
value:
|
1001 |
- type: mrr_at_100
|
1002 |
-
value:
|
1003 |
- type: mrr_at_1000
|
1004 |
-
value:
|
1005 |
- type: mrr_at_3
|
1006 |
-
value:
|
1007 |
- type: mrr_at_5
|
1008 |
-
value:
|
1009 |
- type: ndcg_at_1
|
1010 |
-
value:
|
1011 |
- type: ndcg_at_10
|
1012 |
-
value:
|
1013 |
- type: ndcg_at_100
|
1014 |
-
value:
|
1015 |
- type: ndcg_at_1000
|
1016 |
-
value:
|
1017 |
- type: ndcg_at_3
|
1018 |
-
value:
|
1019 |
- type: ndcg_at_5
|
1020 |
-
value:
|
1021 |
- type: precision_at_1
|
1022 |
-
value:
|
1023 |
- type: precision_at_10
|
1024 |
-
value: 8.
|
1025 |
- type: precision_at_100
|
1026 |
-
value: 0.
|
1027 |
- type: precision_at_1000
|
1028 |
-
value: 0.
|
1029 |
- type: precision_at_3
|
1030 |
-
value:
|
1031 |
- type: precision_at_5
|
1032 |
-
value:
|
1033 |
- type: recall_at_1
|
1034 |
-
value:
|
1035 |
- type: recall_at_10
|
1036 |
-
value:
|
1037 |
- type: recall_at_100
|
1038 |
-
value:
|
1039 |
- type: recall_at_1000
|
1040 |
-
value: 98.
|
1041 |
- type: recall_at_3
|
1042 |
-
value:
|
1043 |
- type: recall_at_5
|
1044 |
-
value:
|
1045 |
- task:
|
1046 |
type: Classification
|
1047 |
dataset:
|
|
|
6 |
- sentence-similarity
|
7 |
- mteb
|
8 |
model-index:
|
9 |
+
- name: tao-8k-origin
|
10 |
results:
|
11 |
- task:
|
12 |
type: STS
|
|
|
18 |
revision: None
|
19 |
metrics:
|
20 |
- type: cos_sim_pearson
|
21 |
+
value: 47.33644889578121
|
22 |
- type: cos_sim_spearman
|
23 |
+
value: 49.93968642502866
|
24 |
- type: euclidean_pearson
|
25 |
+
value: 48.12029792973887
|
26 |
- type: euclidean_spearman
|
27 |
+
value: 49.939666315145494
|
28 |
- type: manhattan_pearson
|
29 |
+
value: 48.07449594650583
|
30 |
- type: manhattan_spearman
|
31 |
+
value: 49.892461433911166
|
32 |
- task:
|
33 |
type: STS
|
34 |
dataset:
|
|
|
39 |
revision: None
|
40 |
metrics:
|
41 |
- type: cos_sim_pearson
|
42 |
+
value: 50.976148098905746
|
43 |
- type: cos_sim_spearman
|
44 |
+
value: 53.11230114448237
|
45 |
- type: euclidean_pearson
|
46 |
+
value: 55.119977161851054
|
47 |
- type: euclidean_spearman
|
48 |
+
value: 53.11229776647941
|
49 |
- type: manhattan_pearson
|
50 |
+
value: 55.096968162828034
|
51 |
- type: manhattan_spearman
|
52 |
+
value: 53.107481302419465
|
53 |
- task:
|
54 |
type: Classification
|
55 |
dataset:
|
|
|
60 |
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
61 |
metrics:
|
62 |
- type: accuracy
|
63 |
+
value: 40.804
|
64 |
- type: f1
|
65 |
+
value: 39.01066543513968
|
66 |
- task:
|
67 |
type: STS
|
68 |
dataset:
|
|
|
73 |
revision: None
|
74 |
metrics:
|
75 |
- type: cos_sim_pearson
|
76 |
+
value: 62.843816050026824
|
77 |
- type: cos_sim_spearman
|
78 |
+
value: 65.54142642656706
|
79 |
- type: euclidean_pearson
|
80 |
+
value: 64.08809634876388
|
81 |
- type: euclidean_spearman
|
82 |
+
value: 65.54142642558392
|
83 |
- type: manhattan_pearson
|
84 |
+
value: 64.09391522108272
|
85 |
- type: manhattan_spearman
|
86 |
+
value: 65.55445491162718
|
87 |
- task:
|
88 |
type: Clustering
|
89 |
dataset:
|
|
|
94 |
revision: None
|
95 |
metrics:
|
96 |
- type: v_measure
|
97 |
+
value: 40.028061591547804
|
98 |
- task:
|
99 |
type: Clustering
|
100 |
dataset:
|
|
|
105 |
revision: None
|
106 |
metrics:
|
107 |
- type: v_measure
|
108 |
+
value: 38.1897102944254
|
109 |
- task:
|
110 |
type: Reranking
|
111 |
dataset:
|
|
|
129 |
revision: None
|
130 |
metrics:
|
131 |
- type: map
|
132 |
+
value: 85.81294364673899
|
133 |
- type: mrr
|
134 |
+
value: 88.52146825396825
|
135 |
- task:
|
136 |
type: Retrieval
|
137 |
dataset:
|
|
|
142 |
revision: None
|
143 |
metrics:
|
144 |
- type: map_at_1
|
145 |
+
value: 23.982
|
146 |
- type: map_at_10
|
147 |
+
value: 36.21
|
148 |
- type: map_at_100
|
149 |
+
value: 38.072
|
150 |
- type: map_at_1000
|
151 |
+
value: 38.194
|
152 |
- type: map_at_3
|
153 |
+
value: 32.239000000000004
|
154 |
- type: map_at_5
|
155 |
+
value: 34.377
|
156 |
- type: mrr_at_1
|
157 |
+
value: 36.858999999999995
|
158 |
- type: mrr_at_10
|
159 |
+
value: 45.084999999999994
|
160 |
- type: mrr_at_100
|
161 |
+
value: 46.104
|
162 |
- type: mrr_at_1000
|
163 |
+
value: 46.154
|
164 |
- type: mrr_at_3
|
165 |
+
value: 42.623
|
166 |
- type: mrr_at_5
|
167 |
+
value: 43.995
|
168 |
- type: ndcg_at_1
|
169 |
+
value: 36.858999999999995
|
170 |
- type: ndcg_at_10
|
171 |
+
value: 42.735
|
172 |
- type: ndcg_at_100
|
173 |
+
value: 50.181
|
174 |
- type: ndcg_at_1000
|
175 |
+
value: 52.309000000000005
|
176 |
- type: ndcg_at_3
|
177 |
+
value: 37.728
|
178 |
- type: ndcg_at_5
|
179 |
+
value: 39.664
|
180 |
- type: precision_at_1
|
181 |
+
value: 36.858999999999995
|
182 |
- type: precision_at_10
|
183 |
+
value: 9.615
|
184 |
- type: precision_at_100
|
185 |
+
value: 1.564
|
186 |
- type: precision_at_1000
|
187 |
value: 0.183
|
188 |
- type: precision_at_3
|
189 |
+
value: 21.514
|
190 |
- type: precision_at_5
|
191 |
+
value: 15.568999999999999
|
192 |
- type: recall_at_1
|
193 |
+
value: 23.982
|
194 |
- type: recall_at_10
|
195 |
+
value: 53.04600000000001
|
196 |
- type: recall_at_100
|
197 |
+
value: 84.113
|
198 |
- type: recall_at_1000
|
199 |
+
value: 98.37
|
200 |
- type: recall_at_3
|
201 |
+
value: 37.824999999999996
|
202 |
- type: recall_at_5
|
203 |
+
value: 44.023
|
204 |
- task:
|
205 |
type: PairClassification
|
206 |
dataset:
|
|
|
213 |
- type: cos_sim_accuracy
|
214 |
value: 77.71497294046902
|
215 |
- type: cos_sim_ap
|
216 |
+
value: 86.84526989595028
|
217 |
- type: cos_sim_f1
|
218 |
value: 79.31987247608926
|
219 |
- type: cos_sim_precision
|
|
|
223 |
- type: dot_accuracy
|
224 |
value: 77.71497294046902
|
225 |
- type: dot_ap
|
226 |
+
value: 86.83880734247957
|
227 |
- type: dot_f1
|
228 |
value: 79.31987247608926
|
229 |
- type: dot_precision
|
|
|
233 |
- type: euclidean_accuracy
|
234 |
value: 77.71497294046902
|
235 |
- type: euclidean_ap
|
236 |
+
value: 86.84526869685902
|
237 |
- type: euclidean_f1
|
238 |
value: 79.31987247608926
|
239 |
- type: euclidean_precision
|
|
|
243 |
- type: manhattan_accuracy
|
244 |
value: 77.8111846061335
|
245 |
- type: manhattan_ap
|
246 |
+
value: 86.81142881585656
|
247 |
- type: manhattan_f1
|
248 |
+
value: 79.4201671780764
|
249 |
- type: manhattan_precision
|
250 |
+
value: 72.53575570158485
|
251 |
- type: manhattan_recall
|
252 |
value: 87.74842179097499
|
253 |
- type: max_accuracy
|
254 |
value: 77.8111846061335
|
255 |
- type: max_ap
|
256 |
+
value: 86.84526989595028
|
257 |
- type: max_f1
|
258 |
+
value: 79.4201671780764
|
259 |
- task:
|
260 |
type: Retrieval
|
261 |
dataset:
|
|
|
266 |
revision: None
|
267 |
metrics:
|
268 |
- type: map_at_1
|
269 |
+
value: 70.706
|
270 |
- type: map_at_10
|
271 |
+
value: 78.619
|
272 |
- type: map_at_100
|
273 |
+
value: 78.915
|
274 |
- type: map_at_1000
|
275 |
+
value: 78.918
|
276 |
- type: map_at_3
|
277 |
+
value: 76.967
|
278 |
- type: map_at_5
|
279 |
+
value: 77.922
|
280 |
- type: mrr_at_1
|
281 |
+
value: 70.917
|
282 |
- type: mrr_at_10
|
283 |
+
value: 78.64
|
284 |
- type: mrr_at_100
|
285 |
+
value: 78.935
|
286 |
- type: mrr_at_1000
|
287 |
+
value: 78.938
|
288 |
- type: mrr_at_3
|
289 |
+
value: 77.081
|
290 |
- type: mrr_at_5
|
291 |
+
value: 77.972
|
292 |
- type: ndcg_at_1
|
293 |
+
value: 70.917
|
294 |
- type: ndcg_at_10
|
295 |
+
value: 82.186
|
296 |
- type: ndcg_at_100
|
297 |
+
value: 83.487
|
298 |
- type: ndcg_at_1000
|
299 |
+
value: 83.589
|
300 |
- type: ndcg_at_3
|
301 |
+
value: 78.874
|
302 |
- type: ndcg_at_5
|
303 |
+
value: 80.548
|
304 |
- type: precision_at_1
|
305 |
+
value: 70.917
|
306 |
- type: precision_at_10
|
307 |
+
value: 9.431000000000001
|
308 |
- type: precision_at_100
|
309 |
+
value: 1.001
|
310 |
- type: precision_at_1000
|
311 |
value: 0.101
|
312 |
- type: precision_at_3
|
313 |
+
value: 28.275
|
314 |
- type: precision_at_5
|
315 |
+
value: 17.829
|
316 |
- type: recall_at_1
|
317 |
+
value: 70.706
|
318 |
- type: recall_at_10
|
319 |
+
value: 93.256
|
320 |
- type: recall_at_100
|
321 |
+
value: 99.05199999999999
|
322 |
- type: recall_at_1000
|
323 |
+
value: 99.895
|
324 |
- type: recall_at_3
|
325 |
+
value: 84.247
|
326 |
- type: recall_at_5
|
327 |
+
value: 88.251
|
328 |
- task:
|
329 |
type: Retrieval
|
330 |
dataset:
|
|
|
335 |
revision: None
|
336 |
metrics:
|
337 |
- type: map_at_1
|
338 |
+
value: 25.989
|
339 |
- type: map_at_10
|
340 |
+
value: 80.882
|
341 |
- type: map_at_100
|
342 |
+
value: 83.63199999999999
|
343 |
- type: map_at_1000
|
344 |
+
value: 83.663
|
345 |
- type: map_at_3
|
346 |
+
value: 55.772
|
347 |
- type: map_at_5
|
348 |
+
value: 70.598
|
349 |
- type: mrr_at_1
|
350 |
+
value: 90.14999999999999
|
351 |
- type: mrr_at_10
|
352 |
+
value: 93.30000000000001
|
353 |
- type: mrr_at_100
|
354 |
+
value: 93.363
|
355 |
- type: mrr_at_1000
|
356 |
+
value: 93.366
|
357 |
- type: mrr_at_3
|
358 |
+
value: 93.083
|
359 |
- type: mrr_at_5
|
360 |
+
value: 93.206
|
361 |
- type: ndcg_at_1
|
362 |
+
value: 90.14999999999999
|
363 |
- type: ndcg_at_10
|
364 |
+
value: 88.016
|
365 |
- type: ndcg_at_100
|
366 |
+
value: 90.52900000000001
|
367 |
- type: ndcg_at_1000
|
368 |
+
value: 90.84400000000001
|
369 |
- type: ndcg_at_3
|
370 |
+
value: 86.529
|
371 |
- type: ndcg_at_5
|
372 |
+
value: 85.65899999999999
|
373 |
- type: precision_at_1
|
374 |
+
value: 90.14999999999999
|
375 |
- type: precision_at_10
|
376 |
+
value: 42.295
|
377 |
- type: precision_at_100
|
378 |
+
value: 4.826
|
379 |
- type: precision_at_1000
|
380 |
value: 0.48900000000000005
|
381 |
- type: precision_at_3
|
382 |
+
value: 77.717
|
383 |
- type: precision_at_5
|
384 |
+
value: 65.81
|
385 |
- type: recall_at_1
|
386 |
+
value: 25.989
|
387 |
- type: recall_at_10
|
388 |
+
value: 89.446
|
389 |
- type: recall_at_100
|
390 |
+
value: 97.832
|
391 |
- type: recall_at_1000
|
392 |
+
value: 99.568
|
393 |
- type: recall_at_3
|
394 |
+
value: 58.223
|
395 |
- type: recall_at_5
|
396 |
+
value: 75.411
|
397 |
- task:
|
398 |
type: Retrieval
|
399 |
dataset:
|
|
|
404 |
revision: None
|
405 |
metrics:
|
406 |
- type: map_at_1
|
407 |
+
value: 49.6
|
408 |
- type: map_at_10
|
409 |
+
value: 59.512
|
410 |
- type: map_at_100
|
411 |
+
value: 60.059
|
412 |
- type: map_at_1000
|
413 |
+
value: 60.077999999999996
|
414 |
- type: map_at_3
|
415 |
+
value: 56.882999999999996
|
416 |
- type: map_at_5
|
417 |
+
value: 58.298
|
418 |
- type: mrr_at_1
|
419 |
+
value: 49.6
|
420 |
- type: mrr_at_10
|
421 |
+
value: 59.512
|
422 |
- type: mrr_at_100
|
423 |
+
value: 60.059
|
424 |
- type: mrr_at_1000
|
425 |
+
value: 60.077999999999996
|
426 |
- type: mrr_at_3
|
427 |
+
value: 56.882999999999996
|
428 |
- type: mrr_at_5
|
429 |
+
value: 58.298
|
430 |
- type: ndcg_at_1
|
431 |
+
value: 49.6
|
432 |
- type: ndcg_at_10
|
433 |
+
value: 64.71000000000001
|
434 |
- type: ndcg_at_100
|
435 |
+
value: 67.238
|
436 |
- type: ndcg_at_1000
|
437 |
+
value: 67.74
|
438 |
- type: ndcg_at_3
|
439 |
+
value: 59.275
|
440 |
- type: ndcg_at_5
|
441 |
+
value: 61.805
|
442 |
- type: precision_at_1
|
443 |
+
value: 49.6
|
444 |
- type: precision_at_10
|
445 |
+
value: 8.12
|
446 |
- type: precision_at_100
|
447 |
+
value: 0.927
|
448 |
- type: precision_at_1000
|
449 |
+
value: 0.097
|
450 |
- type: precision_at_3
|
451 |
+
value: 22.067
|
452 |
- type: precision_at_5
|
453 |
+
value: 14.46
|
454 |
- type: recall_at_1
|
455 |
+
value: 49.6
|
456 |
- type: recall_at_10
|
457 |
+
value: 81.2
|
458 |
- type: recall_at_100
|
459 |
+
value: 92.7
|
460 |
- type: recall_at_1000
|
461 |
+
value: 96.6
|
462 |
- type: recall_at_3
|
463 |
+
value: 66.2
|
464 |
- type: recall_at_5
|
465 |
+
value: 72.3
|
466 |
- task:
|
467 |
type: Classification
|
468 |
dataset:
|
|
|
473 |
revision: None
|
474 |
metrics:
|
475 |
- type: accuracy
|
476 |
+
value: 47.98768757214313
|
477 |
- type: f1
|
478 |
+
value: 35.24243089488371
|
479 |
- task:
|
480 |
type: Classification
|
481 |
dataset:
|
|
|
501 |
revision: None
|
502 |
metrics:
|
503 |
- type: cos_sim_pearson
|
504 |
+
value: 71.17874301231225
|
505 |
- type: cos_sim_spearman
|
506 |
+
value: 77.47936067899236
|
507 |
- type: euclidean_pearson
|
508 |
+
value: 76.3241109984839
|
509 |
- type: euclidean_spearman
|
510 |
+
value: 77.47936511149533
|
511 |
- type: manhattan_pearson
|
512 |
+
value: 76.3334642249198
|
513 |
- type: manhattan_spearman
|
514 |
+
value: 77.48889610190774
|
515 |
- task:
|
516 |
type: Reranking
|
517 |
dataset:
|
|
|
522 |
revision: None
|
523 |
metrics:
|
524 |
- type: map
|
525 |
+
value: 27.96872431410137
|
526 |
- type: mrr
|
527 |
value: 26.92023809523809
|
528 |
- task:
|
|
|
535 |
revision: None
|
536 |
metrics:
|
537 |
- type: map_at_1
|
538 |
+
value: 66.83099999999999
|
539 |
- type: map_at_10
|
540 |
+
value: 75.945
|
541 |
- type: map_at_100
|
542 |
+
value: 76.259
|
543 |
- type: map_at_1000
|
544 |
+
value: 76.27000000000001
|
545 |
- type: map_at_3
|
546 |
+
value: 74.22999999999999
|
547 |
- type: map_at_5
|
548 |
+
value: 75.318
|
549 |
- type: mrr_at_1
|
550 |
+
value: 69.069
|
551 |
- type: mrr_at_10
|
552 |
+
value: 76.491
|
553 |
- type: mrr_at_100
|
554 |
+
value: 76.764
|
555 |
- type: mrr_at_1000
|
556 |
+
value: 76.775
|
557 |
- type: mrr_at_3
|
558 |
+
value: 75.01
|
559 |
- type: mrr_at_5
|
560 |
+
value: 75.934
|
561 |
- type: ndcg_at_1
|
562 |
+
value: 69.069
|
563 |
- type: ndcg_at_10
|
564 |
+
value: 79.557
|
565 |
- type: ndcg_at_100
|
566 |
+
value: 80.946
|
567 |
- type: ndcg_at_1000
|
568 |
+
value: 81.23700000000001
|
569 |
- type: ndcg_at_3
|
570 |
+
value: 76.31099999999999
|
571 |
- type: ndcg_at_5
|
572 |
+
value: 78.121
|
573 |
- type: precision_at_1
|
574 |
+
value: 69.069
|
575 |
- type: precision_at_10
|
576 |
+
value: 9.58
|
577 |
- type: precision_at_100
|
578 |
+
value: 1.027
|
579 |
- type: precision_at_1000
|
580 |
value: 0.105
|
581 |
- type: precision_at_3
|
582 |
+
value: 28.73
|
583 |
- type: precision_at_5
|
584 |
+
value: 18.201
|
585 |
- type: recall_at_1
|
586 |
+
value: 66.83099999999999
|
587 |
- type: recall_at_10
|
588 |
+
value: 90.118
|
589 |
- type: recall_at_100
|
590 |
+
value: 96.377
|
591 |
- type: recall_at_1000
|
592 |
+
value: 98.656
|
593 |
- type: recall_at_3
|
594 |
+
value: 81.516
|
595 |
- type: recall_at_5
|
596 |
+
value: 85.798
|
597 |
- task:
|
598 |
type: Classification
|
599 |
dataset:
|
|
|
604 |
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
605 |
metrics:
|
606 |
- type: accuracy
|
607 |
+
value: 68.2649630127774
|
608 |
- type: f1
|
609 |
+
value: 65.96868218344183
|
610 |
- task:
|
611 |
type: Classification
|
612 |
dataset:
|
|
|
617 |
revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
618 |
metrics:
|
619 |
- type: accuracy
|
620 |
+
value: 73.13382649630127
|
621 |
- type: f1
|
622 |
+
value: 72.69980239148315
|
623 |
- task:
|
624 |
type: Retrieval
|
625 |
dataset:
|
|
|
630 |
revision: None
|
631 |
metrics:
|
632 |
- type: map_at_1
|
633 |
+
value: 51.2
|
634 |
- type: map_at_10
|
635 |
+
value: 57.715
|
636 |
- type: map_at_100
|
637 |
+
value: 58.233999999999995
|
638 |
- type: map_at_1000
|
639 |
+
value: 58.289
|
640 |
- type: map_at_3
|
641 |
+
value: 56.483000000000004
|
642 |
- type: map_at_5
|
643 |
+
value: 57.193000000000005
|
644 |
- type: mrr_at_1
|
645 |
+
value: 51.2
|
646 |
- type: mrr_at_10
|
647 |
+
value: 57.714
|
648 |
- type: mrr_at_100
|
649 |
+
value: 58.233000000000004
|
650 |
- type: mrr_at_1000
|
651 |
+
value: 58.288
|
652 |
- type: mrr_at_3
|
653 |
+
value: 56.483000000000004
|
654 |
- type: mrr_at_5
|
655 |
+
value: 57.193000000000005
|
656 |
- type: ndcg_at_1
|
657 |
+
value: 51.2
|
658 |
- type: ndcg_at_10
|
659 |
+
value: 60.63499999999999
|
660 |
- type: ndcg_at_100
|
661 |
+
value: 63.458000000000006
|
662 |
- type: ndcg_at_1000
|
663 |
+
value: 64.992
|
664 |
- type: ndcg_at_3
|
665 |
+
value: 58.11300000000001
|
666 |
- type: ndcg_at_5
|
667 |
+
value: 59.391000000000005
|
668 |
- type: precision_at_1
|
669 |
+
value: 51.2
|
670 |
- type: precision_at_10
|
671 |
+
value: 6.97
|
672 |
- type: precision_at_100
|
673 |
+
value: 0.836
|
674 |
- type: precision_at_1000
|
675 |
value: 0.096
|
676 |
- type: precision_at_3
|
677 |
+
value: 20.933
|
678 |
- type: precision_at_5
|
679 |
+
value: 13.18
|
680 |
- type: recall_at_1
|
681 |
+
value: 51.2
|
682 |
- type: recall_at_10
|
683 |
+
value: 69.69999999999999
|
684 |
- type: recall_at_100
|
685 |
+
value: 83.6
|
686 |
- type: recall_at_1000
|
687 |
+
value: 95.8
|
688 |
- type: recall_at_3
|
689 |
+
value: 62.8
|
690 |
- type: recall_at_5
|
691 |
+
value: 65.9
|
692 |
- task:
|
693 |
type: Classification
|
694 |
dataset:
|
|
|
699 |
revision: None
|
700 |
metrics:
|
701 |
- type: accuracy
|
702 |
+
value: 73.39
|
703 |
- type: f1
|
704 |
+
value: 72.85739851837214
|
705 |
- task:
|
706 |
type: PairClassification
|
707 |
dataset:
|
|
|
714 |
- type: cos_sim_accuracy
|
715 |
value: 73.36220898754738
|
716 |
- type: cos_sim_ap
|
717 |
+
value: 78.50045169678386
|
718 |
- type: cos_sim_f1
|
719 |
+
value: 75.3875968992248
|
720 |
- type: cos_sim_precision
|
721 |
+
value: 69.65085049239033
|
722 |
- type: cos_sim_recall
|
723 |
+
value: 82.15417106652588
|
724 |
- type: dot_accuracy
|
725 |
value: 73.36220898754738
|
726 |
- type: dot_ap
|
727 |
+
value: 78.50039148302838
|
728 |
- type: dot_f1
|
729 |
+
value: 75.3875968992248
|
730 |
- type: dot_precision
|
731 |
+
value: 69.65085049239033
|
732 |
- type: dot_recall
|
733 |
+
value: 82.15417106652588
|
734 |
- type: euclidean_accuracy
|
735 |
value: 73.36220898754738
|
736 |
- type: euclidean_ap
|
737 |
+
value: 78.50045169678386
|
738 |
- type: euclidean_f1
|
739 |
+
value: 75.3875968992248
|
740 |
- type: euclidean_precision
|
741 |
+
value: 69.65085049239033
|
742 |
- type: euclidean_recall
|
743 |
+
value: 82.15417106652588
|
744 |
- type: manhattan_accuracy
|
745 |
value: 73.09149972929075
|
746 |
- type: manhattan_ap
|
747 |
+
value: 78.40911589236852
|
748 |
- type: manhattan_f1
|
749 |
value: 75.3623188405797
|
750 |
- type: manhattan_precision
|
|
|
754 |
- type: max_accuracy
|
755 |
value: 73.36220898754738
|
756 |
- type: max_ap
|
757 |
+
value: 78.50045169678386
|
758 |
- type: max_f1
|
759 |
+
value: 75.3875968992248
|
760 |
- task:
|
761 |
type: Classification
|
762 |
dataset:
|
|
|
767 |
revision: None
|
768 |
metrics:
|
769 |
- type: accuracy
|
770 |
+
value: 91.81000000000002
|
771 |
- type: ap
|
772 |
+
value: 89.35809579688139
|
773 |
- type: f1
|
774 |
+
value: 91.79220350456818
|
775 |
- task:
|
776 |
type: STS
|
777 |
dataset:
|
|
|
782 |
revision: None
|
783 |
metrics:
|
784 |
- type: cos_sim_pearson
|
785 |
+
value: 30.06960208048424
|
786 |
- type: cos_sim_spearman
|
787 |
+
value: 36.21568893707218
|
788 |
- type: euclidean_pearson
|
789 |
+
value: 36.3789158810154
|
790 |
- type: euclidean_spearman
|
791 |
+
value: 36.21568740241203
|
792 |
- type: manhattan_pearson
|
793 |
+
value: 36.318190228955935
|
794 |
- type: manhattan_spearman
|
795 |
+
value: 36.16813420759451
|
796 |
- task:
|
797 |
type: STS
|
798 |
dataset:
|
|
|
803 |
revision: None
|
804 |
metrics:
|
805 |
- type: cos_sim_pearson
|
806 |
+
value: 36.779942621488736
|
807 |
- type: cos_sim_spearman
|
808 |
+
value: 38.73716529566492
|
809 |
- type: euclidean_pearson
|
810 |
+
value: 37.134107612179605
|
811 |
- type: euclidean_spearman
|
812 |
+
value: 38.737099842399545
|
813 |
- type: manhattan_pearson
|
814 |
+
value: 37.17579625045808
|
815 |
- type: manhattan_spearman
|
816 |
+
value: 38.746051563332315
|
817 |
- task:
|
818 |
type: STS
|
819 |
dataset:
|
|
|
824 |
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
825 |
metrics:
|
826 |
- type: cos_sim_pearson
|
827 |
+
value: 65.97416499132073
|
828 |
- type: cos_sim_spearman
|
829 |
+
value: 68.87894646940939
|
830 |
- type: euclidean_pearson
|
831 |
+
value: 67.2366929400408
|
832 |
- type: euclidean_spearman
|
833 |
+
value: 68.87894646940939
|
834 |
- type: manhattan_pearson
|
835 |
+
value: 67.30590304353478
|
836 |
- type: manhattan_spearman
|
837 |
+
value: 68.90546655032796
|
838 |
- task:
|
839 |
type: STS
|
840 |
dataset:
|
|
|
845 |
revision: None
|
846 |
metrics:
|
847 |
- type: cos_sim_pearson
|
848 |
+
value: 78.99420906581649
|
849 |
- type: cos_sim_spearman
|
850 |
+
value: 79.36553449000968
|
851 |
- type: euclidean_pearson
|
852 |
+
value: 78.77734144763518
|
853 |
- type: euclidean_spearman
|
854 |
+
value: 79.36545230850567
|
855 |
- type: manhattan_pearson
|
856 |
+
value: 78.82512507141092
|
857 |
- type: manhattan_spearman
|
858 |
+
value: 79.43977311125059
|
859 |
- task:
|
860 |
type: Reranking
|
861 |
dataset:
|
|
|
866 |
revision: None
|
867 |
metrics:
|
868 |
- type: map
|
869 |
+
value: 66.38018284846501
|
870 |
- type: mrr
|
871 |
+
value: 76.11180965277104
|
872 |
- task:
|
873 |
type: Retrieval
|
874 |
dataset:
|
|
|
879 |
revision: None
|
880 |
metrics:
|
881 |
- type: map_at_1
|
882 |
+
value: 27.423
|
883 |
- type: map_at_10
|
884 |
+
value: 77.206
|
885 |
- type: map_at_100
|
886 |
+
value: 80.83500000000001
|
887 |
- type: map_at_1000
|
888 |
+
value: 80.9
|
889 |
- type: map_at_3
|
890 |
+
value: 54.190000000000005
|
891 |
- type: map_at_5
|
892 |
+
value: 66.662
|
893 |
- type: mrr_at_1
|
894 |
value: 90.049
|
895 |
- type: mrr_at_10
|
896 |
+
value: 92.48100000000001
|
897 |
- type: mrr_at_100
|
898 |
+
value: 92.567
|
899 |
- type: mrr_at_1000
|
900 |
+
value: 92.571
|
901 |
- type: mrr_at_3
|
902 |
+
value: 92.07
|
903 |
- type: mrr_at_5
|
904 |
+
value: 92.32900000000001
|
905 |
- type: ndcg_at_1
|
906 |
value: 90.049
|
907 |
- type: ndcg_at_10
|
908 |
+
value: 84.69
|
909 |
- type: ndcg_at_100
|
910 |
+
value: 88.254
|
911 |
- type: ndcg_at_1000
|
912 |
+
value: 88.89399999999999
|
913 |
- type: ndcg_at_3
|
914 |
+
value: 86.091
|
915 |
- type: ndcg_at_5
|
916 |
+
value: 84.685
|
917 |
- type: precision_at_1
|
918 |
value: 90.049
|
919 |
- type: precision_at_10
|
920 |
+
value: 42.141
|
921 |
- type: precision_at_100
|
922 |
+
value: 5.016
|
923 |
- type: precision_at_1000
|
924 |
value: 0.516
|
925 |
- type: precision_at_3
|
926 |
+
value: 75.352
|
927 |
- type: precision_at_5
|
928 |
+
value: 63.176
|
929 |
- type: recall_at_1
|
930 |
+
value: 27.423
|
931 |
- type: recall_at_10
|
932 |
+
value: 83.595
|
933 |
- type: recall_at_100
|
934 |
value: 95.21
|
935 |
- type: recall_at_1000
|
936 |
value: 98.503
|
937 |
- type: recall_at_3
|
938 |
+
value: 55.84400000000001
|
939 |
- type: recall_at_5
|
940 |
+
value: 69.987
|
941 |
- task:
|
942 |
type: Classification
|
943 |
dataset:
|
|
|
948 |
revision: None
|
949 |
metrics:
|
950 |
- type: accuracy
|
951 |
+
value: 51.927
|
952 |
- type: f1
|
953 |
+
value: 50.16838216110367
|
954 |
- task:
|
955 |
type: Clustering
|
956 |
dataset:
|
|
|
961 |
revision: None
|
962 |
metrics:
|
963 |
- type: v_measure
|
964 |
+
value: 60.85131720842154
|
965 |
- task:
|
966 |
type: Clustering
|
967 |
dataset:
|
|
|
972 |
revision: None
|
973 |
metrics:
|
974 |
- type: v_measure
|
975 |
+
value: 57.0921610946628
|
976 |
- task:
|
977 |
type: Retrieval
|
978 |
dataset:
|
|
|
983 |
revision: None
|
984 |
metrics:
|
985 |
- type: map_at_1
|
986 |
+
value: 56.99999999999999
|
987 |
- type: map_at_10
|
988 |
+
value: 67.611
|
989 |
- type: map_at_100
|
990 |
+
value: 68.095
|
991 |
- type: map_at_1000
|
992 |
+
value: 68.10300000000001
|
993 |
- type: map_at_3
|
994 |
+
value: 65.75
|
995 |
- type: map_at_5
|
996 |
+
value: 66.93
|
997 |
- type: mrr_at_1
|
998 |
+
value: 56.89999999999999
|
999 |
- type: mrr_at_10
|
1000 |
+
value: 67.561
|
1001 |
- type: mrr_at_100
|
1002 |
+
value: 68.045
|
1003 |
- type: mrr_at_1000
|
1004 |
+
value: 68.053
|
1005 |
- type: mrr_at_3
|
1006 |
+
value: 65.7
|
1007 |
- type: mrr_at_5
|
1008 |
+
value: 66.88
|
1009 |
- type: ndcg_at_1
|
1010 |
+
value: 56.99999999999999
|
1011 |
- type: ndcg_at_10
|
1012 |
+
value: 72.25200000000001
|
1013 |
- type: ndcg_at_100
|
1014 |
+
value: 74.542
|
1015 |
- type: ndcg_at_1000
|
1016 |
+
value: 74.725
|
1017 |
- type: ndcg_at_3
|
1018 |
+
value: 68.47
|
1019 |
- type: ndcg_at_5
|
1020 |
+
value: 70.583
|
1021 |
- type: precision_at_1
|
1022 |
+
value: 56.99999999999999
|
1023 |
- type: precision_at_10
|
1024 |
+
value: 8.66
|
1025 |
- type: precision_at_100
|
1026 |
+
value: 0.972
|
1027 |
- type: precision_at_1000
|
1028 |
+
value: 0.099
|
1029 |
- type: precision_at_3
|
1030 |
+
value: 25.433
|
1031 |
- type: precision_at_5
|
1032 |
+
value: 16.28
|
1033 |
- type: recall_at_1
|
1034 |
+
value: 56.99999999999999
|
1035 |
- type: recall_at_10
|
1036 |
+
value: 86.6
|
1037 |
- type: recall_at_100
|
1038 |
+
value: 97.2
|
1039 |
- type: recall_at_1000
|
1040 |
+
value: 98.6
|
1041 |
- type: recall_at_3
|
1042 |
+
value: 76.3
|
1043 |
- type: recall_at_5
|
1044 |
+
value: 81.39999999999999
|
1045 |
- task:
|
1046 |
type: Classification
|
1047 |
dataset:
|