Sentence Similarity
Safetensors
Japanese
bert
feature-extraction
hpprc commited on
Commit
8d8012a
1 Parent(s): 59543b3

Upload 17 files

Browse files
result/Classification/scores_amazon_counterfactual_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.8080806321853091,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.9163090128755365,
9
+ "macro_f1": 0.6680366047454656
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.924892703862661,
13
+ "macro_f1": 0.7781405155410461
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.936830835117773,
19
+ "macro_f1": 0.8080806321853091
20
+ }
21
+ }
22
+ }
23
+ }
result/Classification/scores_amazon_review_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.5680171450057119,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.4306,
9
+ "macro_f1": 0.42021222867279706
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.579,
13
+ "macro_f1": 0.5741023378981216
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.5726,
19
+ "macro_f1": 0.5680171450057119
20
+ }
21
+ }
22
+ }
23
+ }
result/Classification/scores_massive_intent_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.8255898596881264,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.794392523364486,
9
+ "macro_f1": 0.777118788798846
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.8484997540580423,
13
+ "macro_f1": 0.820880408759503
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.8527236045729657,
19
+ "macro_f1": 0.8255898596881264
20
+ }
21
+ }
22
+ }
23
+ }
result/Classification/scores_massive_scenario_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.8956410349938264,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.868175110673881,
9
+ "macro_f1": 0.8608390250049474
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.8898180029513035,
13
+ "macro_f1": 0.885017174493131
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.8971082716879624,
19
+ "macro_f1": 0.8956410349938264
20
+ }
21
+ }
22
+ }
23
+ }
result/Clustering/scores_livedoor_news.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "v_measure_score",
3
+ "metric_value": 0.5139491572866559,
4
+ "details": {
5
+ "optimal_clustering_model_name": "BisectingKMeans",
6
+ "val_scores": {
7
+ "MiniBatchKMeans": {
8
+ "v_measure_score": 0.534380650758092,
9
+ "homogeneity_score": 0.5317945677498351,
10
+ "completeness_score": 0.5369920085891019
11
+ },
12
+ "AgglomerativeClustering": {
13
+ "v_measure_score": 0.5087884029896779,
14
+ "homogeneity_score": 0.5086363161581664,
15
+ "completeness_score": 0.5089405807990526
16
+ },
17
+ "BisectingKMeans": {
18
+ "v_measure_score": 0.553601060540702,
19
+ "homogeneity_score": 0.5441430236373349,
20
+ "completeness_score": 0.56339370366749
21
+ },
22
+ "Birch": {
23
+ "v_measure_score": 0.5128405854529453,
24
+ "homogeneity_score": 0.5036525351610802,
25
+ "completeness_score": 0.5223700971469907
26
+ }
27
+ },
28
+ "test_scores": {
29
+ "BisectingKMeans": {
30
+ "v_measure_score": 0.5139491572866559,
31
+ "homogeneity_score": 0.5138923967226684,
32
+ "completeness_score": 0.5140059303906908
33
+ }
34
+ }
35
+ }
36
+ }
result/Clustering/scores_mewsc16.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "v_measure_score",
3
+ "metric_value": 0.5225025331595674,
4
+ "details": {
5
+ "optimal_clustering_model_name": "MiniBatchKMeans",
6
+ "val_scores": {
7
+ "MiniBatchKMeans": {
8
+ "v_measure_score": 0.552492789911379,
9
+ "homogeneity_score": 0.6042939880544443,
10
+ "completeness_score": 0.5088713930700391
11
+ },
12
+ "AgglomerativeClustering": {
13
+ "v_measure_score": 0.5320671961178625,
14
+ "homogeneity_score": 0.5784321195121053,
15
+ "completeness_score": 0.4925835668838936
16
+ },
17
+ "BisectingKMeans": {
18
+ "v_measure_score": 0.5213239117720363,
19
+ "homogeneity_score": 0.5707139382787456,
20
+ "completeness_score": 0.47980151668476945
21
+ },
22
+ "Birch": {
23
+ "v_measure_score": 0.5365993974126925,
24
+ "homogeneity_score": 0.5836494458740594,
25
+ "completeness_score": 0.4965691962164629
26
+ }
27
+ },
28
+ "test_scores": {
29
+ "MiniBatchKMeans": {
30
+ "v_measure_score": 0.5225025331595674,
31
+ "homogeneity_score": 0.5674992006449608,
32
+ "completeness_score": 0.48411717585646613
33
+ }
34
+ }
35
+ }
36
+ }
result/PairClassification/scores_paws_x_ja.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "binary_f1",
3
+ "metric_value": 0.6228813559322034,
4
+ "details": {
5
+ "optimal_distance_metric": "manhatten_distances",
6
+ "val_scores": {
7
+ "cosine_distances": {
8
+ "accuracy": 0.5725,
9
+ "accuracy_threshold": 0.6882385611534119,
10
+ "binary_f1": 0.5979670522257273,
11
+ "binary_f1_threshold": 1.0
12
+ },
13
+ "manhatten_distances": {
14
+ "accuracy": 0.605,
15
+ "accuracy_threshold": 28.748050689697266,
16
+ "binary_f1": 0.6024691358024691,
17
+ "binary_f1_threshold": 441.396484375
18
+ },
19
+ "euclidean_distances": {
20
+ "accuracy": 0.6055,
21
+ "accuracy_threshold": 1.1335991621017456,
22
+ "binary_f1": 0.6024691358024691,
23
+ "binary_f1_threshold": 17.27420425415039
24
+ },
25
+ "dot_similarities": {
26
+ "accuracy": 0.575,
27
+ "accuracy_threshold": 592.8396606445312,
28
+ "binary_f1": 0.6016949152542372,
29
+ "binary_f1_threshold": 467.1964111328125
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "manhatten_distances": {
34
+ "accuracy": 0.566,
35
+ "accuracy_threshold": 28.748050689697266,
36
+ "binary_f1": 0.6228813559322034,
37
+ "binary_f1_threshold": 441.396484375
38
+ }
39
+ }
40
+ }
41
+ }
result/Reranking/scores_esci.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.9298524733536755,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "ndcg@10": 0.9444370520979605,
9
+ "ndcg@20": 0.9564705129231158,
10
+ "ndcg@40": 0.9642100034266439
11
+ },
12
+ "dot_score": {
13
+ "ndcg@10": 0.9397851998624545,
14
+ "ndcg@20": 0.953415991071425,
15
+ "ndcg@40": 0.9616766756410274
16
+ },
17
+ "euclidean_distance": {
18
+ "ndcg@10": 0.9440129544685741,
19
+ "ndcg@20": 0.9562443399797927,
20
+ "ndcg@40": 0.9641731260066199
21
+ }
22
+ },
23
+ "test_scores": {
24
+ "cosine_similarity": {
25
+ "ndcg@10": 0.9298524733536755,
26
+ "ndcg@20": 0.9466833513010471,
27
+ "ndcg@40": 0.9562527070963045
28
+ }
29
+ }
30
+ }
31
+ }
result/Retrieval/scores_jagovfaqs_22k.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.7667506664925435,
4
+ "details": {
5
+ "optimal_distance_metric": "euclidean_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.6267914594910793,
9
+ "accuracy@3": 0.8028663351857268,
10
+ "accuracy@5": 0.8520035097981866,
11
+ "accuracy@10": 0.896753436677391,
12
+ "ndcg@10": 0.7669189607189386,
13
+ "mrr@10": 0.7247629957706004
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.5141854343375256,
17
+ "accuracy@3": 0.7002047382275519,
18
+ "accuracy@5": 0.7627961392219947,
19
+ "accuracy@10": 0.8329921029540801,
20
+ "ndcg@10": 0.6726648184545547,
21
+ "mrr@10": 0.6214657121501219
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.6329336063176367,
25
+ "accuracy@3": 0.805791167007897,
26
+ "accuracy@5": 0.8546358584381398,
27
+ "accuracy@10": 0.8964609534951741,
28
+ "ndcg@10": 0.7698508124664031,
29
+ "mrr@10": 0.7287346156167448
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "euclidean_distance": {
34
+ "accuracy@1": 0.631578947368421,
35
+ "accuracy@3": 0.8023391812865497,
36
+ "accuracy@5": 0.8467836257309942,
37
+ "accuracy@10": 0.8923976608187134,
38
+ "ndcg@10": 0.7667506664925435,
39
+ "mrr@10": 0.7259732664995826
40
+ }
41
+ }
42
+ }
43
+ }
result/Retrieval/scores_jaqket.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.6173871224245404,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.42512562814070354,
9
+ "accuracy@3": 0.6412060301507537,
10
+ "accuracy@5": 0.7266331658291457,
11
+ "accuracy@10": 0.7879396984924623,
12
+ "ndcg@10": 0.6071501889023596,
13
+ "mrr@10": 0.548947914174044
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.4,
17
+ "accuracy@3": 0.6180904522613065,
18
+ "accuracy@5": 0.6814070351758794,
19
+ "accuracy@10": 0.7587939698492462,
20
+ "ndcg@10": 0.5801732695748337,
21
+ "mrr@10": 0.5229046023769638
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.421105527638191,
25
+ "accuracy@3": 0.6311557788944724,
26
+ "accuracy@5": 0.7045226130653266,
27
+ "accuracy@10": 0.7798994974874371,
28
+ "ndcg@10": 0.5993385752717826,
29
+ "mrr@10": 0.541523889287708
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "cosine_similarity": {
34
+ "accuracy@1": 0.436308926780341,
35
+ "accuracy@3": 0.6619859578736209,
36
+ "accuracy@5": 0.7211634904714143,
37
+ "accuracy@10": 0.7963891675025075,
38
+ "ndcg@10": 0.6173871224245404,
39
+ "mrr@10": 0.5600018308894937
40
+ }
41
+ }
42
+ }
43
+ }
result/Retrieval/scores_mrtydi.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.3803302462897418,
4
+ "details": {
5
+ "optimal_distance_metric": "euclidean_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.25323275862068967,
9
+ "accuracy@3": 0.4073275862068966,
10
+ "accuracy@5": 0.47737068965517243,
11
+ "accuracy@10": 0.5775862068965517,
12
+ "ndcg@10": 0.4050275055597,
13
+ "mrr@10": 0.35105406746031714
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.18426724137931033,
17
+ "accuracy@3": 0.3308189655172414,
18
+ "accuracy@5": 0.39870689655172414,
19
+ "accuracy@10": 0.5021551724137931,
20
+ "ndcg@10": 0.3312247600613045,
21
+ "mrr@10": 0.2781006260262726
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.2521551724137931,
25
+ "accuracy@3": 0.41163793103448276,
26
+ "accuracy@5": 0.4827586206896552,
27
+ "accuracy@10": 0.5808189655172413,
28
+ "ndcg@10": 0.40565667279158407,
29
+ "mrr@10": 0.35095657156540727
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "euclidean_distance": {
34
+ "accuracy@1": 0.24722222222222223,
35
+ "accuracy@3": 0.41944444444444445,
36
+ "accuracy@5": 0.5027777777777778,
37
+ "accuracy@10": 0.6166666666666667,
38
+ "ndcg@10": 0.3803302462897418,
39
+ "mrr@10": 0.3568121693121691
40
+ }
41
+ }
42
+ }
43
+ }
result/Retrieval/scores_nlp_journal_abs_intro.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.8712459719069233,
4
+ "details": {
5
+ "optimal_distance_metric": "euclidean_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.87,
9
+ "accuracy@3": 0.93,
10
+ "accuracy@5": 0.95,
11
+ "accuracy@10": 0.95,
12
+ "ndcg@10": 0.9160310788673668,
13
+ "mrr@10": 0.9045000000000001
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.79,
17
+ "accuracy@3": 0.89,
18
+ "accuracy@5": 0.93,
19
+ "accuracy@10": 0.94,
20
+ "ndcg@10": 0.870417853868121,
21
+ "mrr@10": 0.8474166666666666
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.87,
25
+ "accuracy@3": 0.94,
26
+ "accuracy@5": 0.95,
27
+ "accuracy@10": 0.95,
28
+ "ndcg@10": 0.916724313286633,
29
+ "mrr@10": 0.9053333333333333
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "euclidean_distance": {
34
+ "accuracy@1": 0.7920792079207921,
35
+ "accuracy@3": 0.8960396039603961,
36
+ "accuracy@5": 0.9207920792079208,
37
+ "accuracy@10": 0.943069306930693,
38
+ "ndcg@10": 0.8712459719069233,
39
+ "mrr@10": 0.8478096023888101
40
+ }
41
+ }
42
+ }
43
+ }
result/Retrieval/scores_nlp_journal_title_abs.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.9657898747088243,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.9,
9
+ "accuracy@3": 0.97,
10
+ "accuracy@5": 0.99,
11
+ "accuracy@10": 1.0,
12
+ "ndcg@10": 0.9532838532027325,
13
+ "mrr@10": 0.9378333333333333
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.85,
17
+ "accuracy@3": 0.96,
18
+ "accuracy@5": 0.97,
19
+ "accuracy@10": 0.98,
20
+ "ndcg@10": 0.922904980229142,
21
+ "mrr@10": 0.9036666666666666
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.89,
25
+ "accuracy@3": 0.98,
26
+ "accuracy@5": 0.99,
27
+ "accuracy@10": 1.0,
28
+ "ndcg@10": 0.9515956826934274,
29
+ "mrr@10": 0.9353333333333333
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "cosine_similarity": {
34
+ "accuracy@1": 0.9207920792079208,
35
+ "accuracy@3": 0.9826732673267327,
36
+ "accuracy@5": 0.995049504950495,
37
+ "accuracy@10": 1.0,
38
+ "ndcg@10": 0.9657898747088243,
39
+ "mrr@10": 0.9542491749174917
40
+ }
41
+ }
42
+ }
43
+ }
result/Retrieval/scores_nlp_journal_title_intro.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.779665053945222,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.69,
9
+ "accuracy@3": 0.85,
10
+ "accuracy@5": 0.88,
11
+ "accuracy@10": 0.92,
12
+ "ndcg@10": 0.8109556473323615,
13
+ "mrr@10": 0.7755
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.64,
17
+ "accuracy@3": 0.76,
18
+ "accuracy@5": 0.8,
19
+ "accuracy@10": 0.87,
20
+ "ndcg@10": 0.7514689640047522,
21
+ "mrr@10": 0.7141111111111111
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.7,
25
+ "accuracy@3": 0.86,
26
+ "accuracy@5": 0.89,
27
+ "accuracy@10": 0.9,
28
+ "ndcg@10": 0.8074203858231017,
29
+ "mrr@10": 0.7765833333333332
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "cosine_similarity": {
34
+ "accuracy@1": 0.6410891089108911,
35
+ "accuracy@3": 0.8044554455445545,
36
+ "accuracy@5": 0.8564356435643564,
37
+ "accuracy@10": 0.9207920792079208,
38
+ "ndcg@10": 0.779665053945222,
39
+ "mrr@10": 0.7346092644978786
40
+ }
41
+ }
42
+ }
43
+ }
result/STS/scores_jsick.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "spearman",
3
+ "metric_value": 0.8199959693684533,
4
+ "details": {
5
+ "optimal_similarity_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "pearson": 0.8396915234568699,
9
+ "spearman": 0.8270305329525446
10
+ },
11
+ "manhatten_distance": {
12
+ "pearson": 0.842469742131321,
13
+ "spearman": 0.8254681121166032
14
+ },
15
+ "euclidean_distance": {
16
+ "pearson": 0.842469742131321,
17
+ "spearman": 0.8254681121166032
18
+ },
19
+ "dot_score": {
20
+ "pearson": 0.8210183246366123,
21
+ "spearman": 0.7992374318058588
22
+ }
23
+ },
24
+ "test_scores": {
25
+ "cosine_similarity": {
26
+ "pearson": 0.8328957443401386,
27
+ "spearman": 0.8199959693684533
28
+ }
29
+ }
30
+ }
31
+ }
result/STS/scores_jsts.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "spearman",
3
+ "metric_value": 0.8426164139167538,
4
+ "details": {
5
+ "optimal_similarity_metric": "manhatten_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "pearson": 0.8561010991360143,
9
+ "spearman": 0.816640933451772
10
+ },
11
+ "manhatten_distance": {
12
+ "pearson": 0.8538233899023175,
13
+ "spearman": 0.8173500466699406
14
+ },
15
+ "euclidean_distance": {
16
+ "pearson": 0.8538233899023175,
17
+ "spearman": 0.8173500466699406
18
+ },
19
+ "dot_score": {
20
+ "pearson": 0.8304879187538848,
21
+ "spearman": 0.7769676321807759
22
+ }
23
+ },
24
+ "test_scores": {
25
+ "manhatten_distance": {
26
+ "pearson": 0.8763180052177622,
27
+ "spearman": 0.8426164139167538
28
+ }
29
+ }
30
+ }
31
+ }
result/summary.json ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "Classification": {
3
+ "amazon_counterfactual_classification": {
4
+ "macro_f1": 0.8080806321853091
5
+ },
6
+ "amazon_review_classification": {
7
+ "macro_f1": 0.5680171450057119
8
+ },
9
+ "massive_intent_classification": {
10
+ "macro_f1": 0.8255898596881264
11
+ },
12
+ "massive_scenario_classification": {
13
+ "macro_f1": 0.8956410349938264
14
+ }
15
+ },
16
+ "Reranking": {
17
+ "esci": {
18
+ "ndcg@10": 0.9298524733536755
19
+ }
20
+ },
21
+ "Retrieval": {
22
+ "jagovfaqs_22k": {
23
+ "ndcg@10": 0.7667506664925435
24
+ },
25
+ "jaqket": {
26
+ "ndcg@10": 0.6173871224245404
27
+ },
28
+ "mrtydi": {
29
+ "ndcg@10": 0.3803302462897418
30
+ },
31
+ "nlp_journal_abs_intro": {
32
+ "ndcg@10": 0.8712459719069233
33
+ },
34
+ "nlp_journal_title_abs": {
35
+ "ndcg@10": 0.9657898747088243
36
+ },
37
+ "nlp_journal_title_intro": {
38
+ "ndcg@10": 0.779665053945222
39
+ }
40
+ },
41
+ "STS": {
42
+ "jsick": {
43
+ "spearman": 0.8199959693684533
44
+ },
45
+ "jsts": {
46
+ "spearman": 0.8426164139167538
47
+ }
48
+ },
49
+ "Clustering": {
50
+ "livedoor_news": {
51
+ "v_measure_score": 0.5139491572866559
52
+ },
53
+ "mewsc16": {
54
+ "v_measure_score": 0.5225025331595674
55
+ }
56
+ },
57
+ "PairClassification": {
58
+ "paws_x_ja": {
59
+ "binary_f1": 0.6228813559322034
60
+ }
61
+ }
62
+ }