Sentence Similarity
Safetensors
Japanese
modernbert
feature-extraction
hpprc commited on
Commit
f82edc4
·
verified ·
1 Parent(s): 73b2b2e

Upload 17 files

Browse files
results-len512/Classification/scores_amazon_counterfactual_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.8212903958100242,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.9055793991416309,
9
+ "macro_f1": 0.6510076252723311
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.9184549356223176,
13
+ "macro_f1": 0.756611138600253
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.936830835117773,
19
+ "macro_f1": 0.8212903958100242
20
+ }
21
+ }
22
+ }
23
+ }
results-len512/Classification/scores_amazon_review_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.6134613719538808,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.456,
9
+ "macro_f1": 0.44947743361535253
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.6106,
13
+ "macro_f1": 0.6067084288597717
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.6164,
19
+ "macro_f1": 0.6134613719538808
20
+ }
21
+ }
22
+ }
23
+ }
results-len512/Classification/scores_massive_intent_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.821659588084153,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.7830791933103788,
9
+ "macro_f1": 0.7540349043356395
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.8558780127889818,
13
+ "macro_f1": 0.8412198611245395
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.8493611297915266,
19
+ "macro_f1": 0.821659588084153
20
+ }
21
+ }
22
+ }
23
+ }
results-len512/Classification/scores_massive_scenario_classification.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "macro_f1",
3
+ "metric_value": 0.8927605477819499,
4
+ "details": {
5
+ "optimal_classifier_name": "logreg",
6
+ "val_scores": {
7
+ "knn_cosine_k_2": {
8
+ "accuracy": 0.8612887358583374,
9
+ "macro_f1": 0.8475812013823129
10
+ },
11
+ "logreg": {
12
+ "accuracy": 0.8957206099360551,
13
+ "macro_f1": 0.889349188280443
14
+ }
15
+ },
16
+ "test_scores": {
17
+ "logreg": {
18
+ "accuracy": 0.8927370544720915,
19
+ "macro_f1": 0.8927605477819499
20
+ }
21
+ }
22
+ }
23
+ }
results-len512/Clustering/scores_livedoor_news.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "v_measure_score",
3
+ "metric_value": 0.5937524124850755,
4
+ "details": {
5
+ "optimal_clustering_model_name": "MiniBatchKMeans",
6
+ "val_scores": {
7
+ "MiniBatchKMeans": {
8
+ "v_measure_score": 0.5932473997172523,
9
+ "homogeneity_score": 0.5844833525834573,
10
+ "completeness_score": 0.6022782731375254
11
+ },
12
+ "AgglomerativeClustering": {
13
+ "v_measure_score": 0.5823205096909835,
14
+ "homogeneity_score": 0.5730383501428752,
15
+ "completeness_score": 0.5919083279167753
16
+ },
17
+ "BisectingKMeans": {
18
+ "v_measure_score": 0.5619252755091108,
19
+ "homogeneity_score": 0.5612257535302629,
20
+ "completeness_score": 0.5626265434579824
21
+ },
22
+ "Birch": {
23
+ "v_measure_score": 0.5824759855704932,
24
+ "homogeneity_score": 0.5734728844465294,
25
+ "completeness_score": 0.5917662794981928
26
+ }
27
+ },
28
+ "test_scores": {
29
+ "MiniBatchKMeans": {
30
+ "v_measure_score": 0.5937524124850755,
31
+ "homogeneity_score": 0.5867837845991711,
32
+ "completeness_score": 0.6008885481501399
33
+ }
34
+ }
35
+ }
36
+ }
results-len512/Clustering/scores_mewsc16.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "v_measure_score",
3
+ "metric_value": 0.4938330752654621,
4
+ "details": {
5
+ "optimal_clustering_model_name": "Birch",
6
+ "val_scores": {
7
+ "MiniBatchKMeans": {
8
+ "v_measure_score": 0.5020526188374297,
9
+ "homogeneity_score": 0.5508022841005213,
10
+ "completeness_score": 0.46123063979330864
11
+ },
12
+ "AgglomerativeClustering": {
13
+ "v_measure_score": 0.5567865030669769,
14
+ "homogeneity_score": 0.5991745068307929,
15
+ "completeness_score": 0.5199996459830565
16
+ },
17
+ "BisectingKMeans": {
18
+ "v_measure_score": 0.47357487467256326,
19
+ "homogeneity_score": 0.5170167479146354,
20
+ "completeness_score": 0.4368674730918793
21
+ },
22
+ "Birch": {
23
+ "v_measure_score": 0.5639601753239182,
24
+ "homogeneity_score": 0.6063086319538272,
25
+ "completeness_score": 0.527141271397583
26
+ }
27
+ },
28
+ "test_scores": {
29
+ "Birch": {
30
+ "v_measure_score": 0.4938330752654621,
31
+ "homogeneity_score": 0.5270581166850505,
32
+ "completeness_score": 0.4645485534255365
33
+ }
34
+ }
35
+ }
36
+ }
results-len512/PairClassification/scores_paws_x_ja.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "binary_f1",
3
+ "metric_value": 0.6259758694109298,
4
+ "details": {
5
+ "optimal_distance_metric": "dot_similarities",
6
+ "val_scores": {
7
+ "cosine_distances": {
8
+ "accuracy": 0.5725,
9
+ "accuracy_threshold": 0.7325241565704346,
10
+ "binary_f1": 0.5979670522257273,
11
+ "binary_f1_threshold": 1.0
12
+ },
13
+ "manhatten_distances": {
14
+ "accuracy": 0.6105,
15
+ "accuracy_threshold": 63.98142623901367,
16
+ "binary_f1": 0.6014825273561596,
17
+ "binary_f1_threshold": 395.8541259765625
18
+ },
19
+ "euclidean_distances": {
20
+ "accuracy": 0.6115,
21
+ "accuracy_threshold": 2.9641363620758057,
22
+ "binary_f1": 0.6016949152542372,
23
+ "binary_f1_threshold": 18.21161460876465
24
+ },
25
+ "dot_similarities": {
26
+ "accuracy": 0.583,
27
+ "accuracy_threshold": 872.2387084960938,
28
+ "binary_f1": 0.6023329798515377,
29
+ "binary_f1_threshold": 735.9739990234375
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "dot_similarities": {
34
+ "accuracy": 0.581,
35
+ "accuracy_threshold": 872.2387084960938,
36
+ "binary_f1": 0.6259758694109298,
37
+ "binary_f1_threshold": 735.9739990234375
38
+ }
39
+ }
40
+ }
41
+ }
results-len512/Reranking/scores_esci.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.934131495980383,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "ndcg@10": 0.947221947678768,
9
+ "ndcg@20": 0.9580109255686136,
10
+ "ndcg@40": 0.9655483305547552
11
+ },
12
+ "dot_score": {
13
+ "ndcg@10": 0.939423095128925,
14
+ "ndcg@20": 0.9518546213913051,
15
+ "ndcg@40": 0.9602741048502043
16
+ },
17
+ "euclidean_distance": {
18
+ "ndcg@10": 0.946722927636254,
19
+ "ndcg@20": 0.9575361305939064,
20
+ "ndcg@40": 0.9650712902932668
21
+ }
22
+ },
23
+ "test_scores": {
24
+ "cosine_similarity": {
25
+ "ndcg@10": 0.934131495980383,
26
+ "ndcg@20": 0.9502309541380383,
27
+ "ndcg@40": 0.9589594356406169
28
+ }
29
+ }
30
+ }
31
+ }
results-len512/Retrieval/scores_jagovfaqs_22k.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.7790411361538316,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.6557472945305645,
9
+ "accuracy@3": 0.8104708979233695,
10
+ "accuracy@5": 0.8531734425270547,
11
+ "accuracy@10": 0.896168470312957,
12
+ "ndcg@10": 0.7795641799225166,
13
+ "mrr@10": 0.7418103548331685
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.49166422930681486,
17
+ "accuracy@3": 0.6809008482012284,
18
+ "accuracy@5": 0.741152383737935,
19
+ "accuracy@10": 0.804913717461246,
20
+ "ndcg@10": 0.6488989893840936,
21
+ "mrr@10": 0.5988684847049854
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.6513600467973092,
25
+ "accuracy@3": 0.8110558642878034,
26
+ "accuracy@5": 0.8517110266159695,
27
+ "accuracy@10": 0.8964609534951741,
28
+ "ndcg@10": 0.7780790057668981,
29
+ "mrr@10": 0.7397305904910466
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "cosine_similarity": {
34
+ "accuracy@1": 0.6494152046783626,
35
+ "accuracy@3": 0.8093567251461988,
36
+ "accuracy@5": 0.8555555555555555,
37
+ "accuracy@10": 0.9008771929824562,
38
+ "ndcg@10": 0.7790411361538316,
39
+ "mrr@10": 0.7395963287849251
40
+ }
41
+ }
42
+ }
43
+ }
results-len512/Retrieval/scores_jaqket.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.7001158161632123,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.521608040201005,
9
+ "accuracy@3": 0.7266331658291457,
10
+ "accuracy@5": 0.7839195979899497,
11
+ "accuracy@10": 0.8361809045226131,
12
+ "ndcg@10": 0.6835031423555805,
13
+ "mrr@10": 0.634055196618011
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.46934673366834173,
17
+ "accuracy@3": 0.6532663316582915,
18
+ "accuracy@5": 0.714572864321608,
19
+ "accuracy@10": 0.7788944723618091,
20
+ "ndcg@10": 0.6249364714039314,
21
+ "mrr@10": 0.5755304299274148
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.5135678391959799,
25
+ "accuracy@3": 0.7206030150753768,
26
+ "accuracy@5": 0.7758793969849246,
27
+ "accuracy@10": 0.8311557788944723,
28
+ "ndcg@10": 0.6775263671411185,
29
+ "mrr@10": 0.6277366993698654
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "cosine_similarity": {
34
+ "accuracy@1": 0.526579739217653,
35
+ "accuracy@3": 0.7512537612838516,
36
+ "accuracy@5": 0.8164493480441324,
37
+ "accuracy@10": 0.8585757271815446,
38
+ "ndcg@10": 0.7001158161632123,
39
+ "mrr@10": 0.6483888172453874
40
+ }
41
+ }
42
+ }
43
+ }
results-len512/Retrieval/scores_mrtydi.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.48767396659564394,
4
+ "details": {
5
+ "optimal_distance_metric": "euclidean_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.38038793103448276,
9
+ "accuracy@3": 0.5668103448275862,
10
+ "accuracy@5": 0.6357758620689655,
11
+ "accuracy@10": 0.7079741379310345,
12
+ "ndcg@10": 0.5410435939581002,
13
+ "mrr@10": 0.48780617131910187
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.21443965517241378,
17
+ "accuracy@3": 0.36961206896551724,
18
+ "accuracy@5": 0.4375,
19
+ "accuracy@10": 0.5334051724137931,
20
+ "ndcg@10": 0.3646669540665438,
21
+ "mrr@10": 0.31187183565955123
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.3857758620689655,
25
+ "accuracy@3": 0.5657327586206896,
26
+ "accuracy@5": 0.6379310344827587,
27
+ "accuracy@10": 0.709051724137931,
28
+ "ndcg@10": 0.5445163825100324,
29
+ "mrr@10": 0.49207674808429097
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "euclidean_distance": {
34
+ "accuracy@1": 0.3680555555555556,
35
+ "accuracy@3": 0.5333333333333333,
36
+ "accuracy@5": 0.6069444444444444,
37
+ "accuracy@10": 0.6888888888888889,
38
+ "ndcg@10": 0.48767396659564394,
39
+ "mrr@10": 0.46919367283950547
40
+ }
41
+ }
42
+ }
43
+ }
results-len512/Retrieval/scores_nlp_journal_abs_intro.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.9550969239533367,
4
+ "details": {
5
+ "optimal_distance_metric": "euclidean_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.9098360655737705,
9
+ "accuracy@3": 0.9672131147540983,
10
+ "accuracy@5": 0.9918032786885246,
11
+ "accuracy@10": 0.9918032786885246,
12
+ "ndcg@10": 0.9519753703614401,
13
+ "mrr@10": 0.9387978142076502
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.8278688524590164,
17
+ "accuracy@3": 0.9344262295081968,
18
+ "accuracy@5": 0.9672131147540983,
19
+ "accuracy@10": 0.9836065573770492,
20
+ "ndcg@10": 0.9117419934645401,
21
+ "mrr@10": 0.8880692167577415
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.9098360655737705,
25
+ "accuracy@3": 0.9672131147540983,
26
+ "accuracy@5": 0.9918032786885246,
27
+ "accuracy@10": 0.9918032786885246,
28
+ "ndcg@10": 0.955194954465656,
29
+ "mrr@10": 0.942896174863388
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "euclidean_distance": {
34
+ "accuracy@1": 0.9105691056910569,
35
+ "accuracy@3": 0.9735772357723578,
36
+ "accuracy@5": 0.9817073170731707,
37
+ "accuracy@10": 0.991869918699187,
38
+ "ndcg@10": 0.9550969239533367,
39
+ "mrr@10": 0.9428547231900889
40
+ }
41
+ }
42
+ }
43
+ }
results-len512/Retrieval/scores_nlp_journal_title_abs.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.9800724651571329,
4
+ "details": {
5
+ "optimal_distance_metric": "cosine_similarity",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.9590163934426229,
9
+ "accuracy@3": 0.9754098360655737,
10
+ "accuracy@5": 0.9836065573770492,
11
+ "accuracy@10": 1.0,
12
+ "ndcg@10": 0.9787291019625602,
13
+ "mrr@10": 0.9719945355191256
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.8770491803278688,
17
+ "accuracy@3": 0.9754098360655737,
18
+ "accuracy@5": 0.9918032786885246,
19
+ "accuracy@10": 1.0,
20
+ "ndcg@10": 0.9465822466240205,
21
+ "mrr@10": 0.9285519125683058
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.9590163934426229,
25
+ "accuracy@3": 0.9754098360655737,
26
+ "accuracy@5": 0.9836065573770492,
27
+ "accuracy@10": 1.0,
28
+ "ndcg@10": 0.9782768299015435,
29
+ "mrr@10": 0.9715391621129326
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "cosine_similarity": {
34
+ "accuracy@1": 0.959349593495935,
35
+ "accuracy@3": 0.9898373983739838,
36
+ "accuracy@5": 0.991869918699187,
37
+ "accuracy@10": 0.9939024390243902,
38
+ "ndcg@10": 0.9800724651571329,
39
+ "mrr@10": 0.9753274616079494
40
+ }
41
+ }
42
+ }
43
+ }
results-len512/Retrieval/scores_nlp_journal_title_intro.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "ndcg@10",
3
+ "metric_value": 0.8726100026859497,
4
+ "details": {
5
+ "optimal_distance_metric": "euclidean_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "accuracy@1": 0.7868852459016393,
9
+ "accuracy@3": 0.8770491803278688,
10
+ "accuracy@5": 0.9180327868852459,
11
+ "accuracy@10": 0.9590163934426229,
12
+ "ndcg@10": 0.8714655319010017,
13
+ "mrr@10": 0.8436020036429872
14
+ },
15
+ "dot_score": {
16
+ "accuracy@1": 0.6967213114754098,
17
+ "accuracy@3": 0.860655737704918,
18
+ "accuracy@5": 0.8852459016393442,
19
+ "accuracy@10": 0.9180327868852459,
20
+ "ndcg@10": 0.8111445047740787,
21
+ "mrr@10": 0.7763173302107729
22
+ },
23
+ "euclidean_distance": {
24
+ "accuracy@1": 0.7950819672131147,
25
+ "accuracy@3": 0.9016393442622951,
26
+ "accuracy@5": 0.9180327868852459,
27
+ "accuracy@10": 0.9590163934426229,
28
+ "ndcg@10": 0.8753147176163506,
29
+ "mrr@10": 0.8488030184751496
30
+ }
31
+ },
32
+ "test_scores": {
33
+ "euclidean_distance": {
34
+ "accuracy@1": 0.774390243902439,
35
+ "accuracy@3": 0.9085365853658537,
36
+ "accuracy@5": 0.943089430894309,
37
+ "accuracy@10": 0.9613821138211383,
38
+ "ndcg@10": 0.8726100026859497,
39
+ "mrr@10": 0.8434160859465736
40
+ }
41
+ }
42
+ }
43
+ }
results-len512/STS/scores_jsick.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "spearman",
3
+ "metric_value": 0.7812165639898206,
4
+ "details": {
5
+ "optimal_similarity_metric": "manhatten_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "pearson": 0.8081424308280184,
9
+ "spearman": 0.7858960474676613
10
+ },
11
+ "manhatten_distance": {
12
+ "pearson": 0.8149453883286166,
13
+ "spearman": 0.7866688378992329
14
+ },
15
+ "euclidean_distance": {
16
+ "pearson": 0.8149453883286166,
17
+ "spearman": 0.7866688378992329
18
+ },
19
+ "dot_score": {
20
+ "pearson": 0.7289190000591521,
21
+ "spearman": 0.6947631991077574
22
+ }
23
+ },
24
+ "test_scores": {
25
+ "manhatten_distance": {
26
+ "pearson": 0.8101410164828766,
27
+ "spearman": 0.7812165639898206
28
+ }
29
+ }
30
+ }
31
+ }
results-len512/STS/scores_jsts.json ADDED
@@ -0,0 +1,31 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metric_name": "spearman",
3
+ "metric_value": 0.8432397778118456,
4
+ "details": {
5
+ "optimal_similarity_metric": "manhatten_distance",
6
+ "val_scores": {
7
+ "cosine_similarity": {
8
+ "pearson": 0.8535555729076842,
9
+ "spearman": 0.819115099484669
10
+ },
11
+ "manhatten_distance": {
12
+ "pearson": 0.8633943133209987,
13
+ "spearman": 0.8261879574678256
14
+ },
15
+ "euclidean_distance": {
16
+ "pearson": 0.8633943133209987,
17
+ "spearman": 0.8261879574678256
18
+ },
19
+ "dot_score": {
20
+ "pearson": 0.713304993887736,
21
+ "spearman": 0.6363102927036544
22
+ }
23
+ },
24
+ "test_scores": {
25
+ "manhatten_distance": {
26
+ "pearson": 0.8819766195104106,
27
+ "spearman": 0.8432397778118456
28
+ }
29
+ }
30
+ }
31
+ }
results-len512/summary.json ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "Classification": {
3
+ "amazon_counterfactual_classification": {
4
+ "macro_f1": 0.8212903958100242
5
+ },
6
+ "amazon_review_classification": {
7
+ "macro_f1": 0.6134613719538808
8
+ },
9
+ "massive_intent_classification": {
10
+ "macro_f1": 0.821659588084153
11
+ },
12
+ "massive_scenario_classification": {
13
+ "macro_f1": 0.8927605477819499
14
+ }
15
+ },
16
+ "Reranking": {
17
+ "esci": {
18
+ "ndcg@10": 0.934131495980383
19
+ }
20
+ },
21
+ "Retrieval": {
22
+ "jagovfaqs_22k": {
23
+ "ndcg@10": 0.7790411361538316
24
+ },
25
+ "jaqket": {
26
+ "ndcg@10": 0.7001158161632123
27
+ },
28
+ "mrtydi": {
29
+ "ndcg@10": 0.48767396659564394
30
+ },
31
+ "nlp_journal_abs_intro": {
32
+ "ndcg@10": 0.9550969239533367
33
+ },
34
+ "nlp_journal_title_abs": {
35
+ "ndcg@10": 0.9800724651571329
36
+ },
37
+ "nlp_journal_title_intro": {
38
+ "ndcg@10": 0.8726100026859497
39
+ }
40
+ },
41
+ "STS": {
42
+ "jsick": {
43
+ "spearman": 0.7812165639898206
44
+ },
45
+ "jsts": {
46
+ "spearman": 0.8432397778118456
47
+ }
48
+ },
49
+ "Clustering": {
50
+ "livedoor_news": {
51
+ "v_measure_score": 0.5937524124850755
52
+ },
53
+ "mewsc16": {
54
+ "v_measure_score": 0.4938330752654621
55
+ }
56
+ },
57
+ "PairClassification": {
58
+ "paws_x_ja": {
59
+ "binary_f1": 0.6259758694109298
60
+ }
61
+ }
62
+ }