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tomaarsen
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splade-distilbert-base-uncased-nq-updated-sparsity

Feature Extraction
sentence-transformers
Safetensors
English
distilbert
sparse-encoder
sparse
splade
Generated from Trainer
dataset_size:99000
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community
tomaarsen HF Staff commited on
Commit
b8650f1
·
verified ·
1 Parent(s): 4698fab

Add new SparseEncoder model

Browse files
Files changed (11) hide show
  1. 1_SpladePooling/config.json +5 -0
  2. README.md +1762 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +11 -0
  5. model.safetensors +3 -0
  6. modules.json +14 -0
  7. sentence_bert_config.json +4 -0
  8. special_tokens_map.json +7 -0
  9. tokenizer.json +0 -0
  10. tokenizer_config.json +56 -0
  11. vocab.txt +0 -0
1_SpladePooling/config.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "pooling_strategy": "max",
3
+ "activation_function": "relu",
4
+ "word_embedding_dimension": 30522
5
+ }
README.md ADDED
@@ -0,0 +1,1762 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ language:
3
+ - en
4
+ license: apache-2.0
5
+ tags:
6
+ - sentence-transformers
7
+ - sparse-encoder
8
+ - sparse
9
+ - splade
10
+ - generated_from_trainer
11
+ - dataset_size:99000
12
+ - loss:SpladeLoss
13
+ - loss:SparseMultipleNegativesRankingLoss
14
+ - loss:FlopsLoss
15
+ base_model: distilbert/distilbert-base-uncased
16
+ widget:
17
+ - text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower
18
+ of the former World Trade Center in New York City. The introduction features Ben
19
+ Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the
20
+ keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles.
21
+ The rest of the video has several cuts to Durst and his bandmates hanging out
22
+ of the Bentley as they drive about Manhattan. The song Ben Stiller is playing
23
+ at the beginning is "My Generation" from the same album. The video also features
24
+ scenes of Fred Durst with five girls dancing in a room. The video was filmed around
25
+ the same time as the film Zoolander, which explains Stiller and Dorff's appearance.
26
+ Fred Durst has a small cameo in that film.
27
+ - text: 'Maze Runner: The Death Cure On April 22, 2017, the studio delayed the release
28
+ date once again, to February 9, 2018, in order to allow more time for post-production;
29
+ months later, on August 25, the studio moved the release forward two weeks.[17]
30
+ The film will premiere on January 26, 2018 in 3D, IMAX and IMAX 3D.[18][19]'
31
+ - text: who played the dj in the movie the warriors
32
+ - text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with
33
+ a growth hormone deficiency as a child. At age 13, he relocated to Spain to join
34
+ Barcelona, who agreed to pay for his medical treatment. After a fast progression
35
+ through Barcelona's youth academy, Messi made his competitive debut aged 17 in
36
+ October 2004. Despite being injury-prone during his early career, he established
37
+ himself as an integral player for the club within the next three years, finishing
38
+ 2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year
39
+ award, a feat he repeated the following year. His first uninterrupted campaign
40
+ came in the 2008–09 season, during which he helped Barcelona achieve the first
41
+ treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA
42
+ World Player of the Year award by record voting margins.
43
+ - text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim
44
+ for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s
45
+ film Smiles of a Summer Night. It is a ballad from Act Two, in which the character
46
+ Desirée reflects on the ironies and disappointments of her life. Among other things,
47
+ she looks back on an affair years earlier with the lawyer Fredrik, who was deeply
48
+ in love with her but whose marriage proposals she had rejected. Meeting him after
49
+ so long, she realizes she is in love with him and finally ready to marry him,
50
+ but now it is he who rejects her: he is in an unconsummated marriage with a much
51
+ younger woman. Desirée proposes marriage to rescue him from this situation, but
52
+ he declines, citing his dedication to his bride. Reacting to his rejection, Desirée
53
+ sings this song. The song is later reprised as a coda after Fredrik''s young wife
54
+ runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]'
55
+ datasets:
56
+ - sentence-transformers/natural-questions
57
+ pipeline_tag: feature-extraction
58
+ library_name: sentence-transformers
59
+ metrics:
60
+ - dot_accuracy@1
61
+ - dot_accuracy@3
62
+ - dot_accuracy@5
63
+ - dot_accuracy@10
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+ - dot_precision@1
65
+ - dot_precision@3
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+ - dot_precision@5
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+ - dot_precision@10
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+ - dot_recall@1
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+ - dot_recall@3
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+ - dot_recall@5
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+ - dot_recall@10
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+ - dot_ndcg@10
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+ - dot_mrr@10
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+ - dot_map@100
75
+ - query_active_dims
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+ - query_sparsity_ratio
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+ - corpus_active_dims
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+ - corpus_sparsity_ratio
79
+ co2_eq_emissions:
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+ emissions: 32.40901449048007
81
+ energy_consumed: 0.08337753469362151
82
+ source: codecarbon
83
+ training_type: fine-tuning
84
+ on_cloud: false
85
+ cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
86
+ ram_total_size: 31.777088165283203
87
+ hours_used: 0.285
88
+ hardware_used: 1 x NVIDIA GeForce RTX 3090
89
+ model-index:
90
+ - name: splade-distilbert-base-uncased trained on Natural Questions
91
+ results:
92
+ - task:
93
+ type: sparse-information-retrieval
94
+ name: Sparse Information Retrieval
95
+ dataset:
96
+ name: NanoMSMARCO
97
+ type: NanoMSMARCO
98
+ metrics:
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+ name: Sparse Information Retrieval
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217
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+ name: Sparse Information Retrieval
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+ value: 0.17566666666666667
996
+ name: Dot Recall@3
997
+ - type: dot_recall@5
998
+ value: 0.24466666666666664
999
+ name: Dot Recall@5
1000
+ - type: dot_recall@10
1001
+ value: 0.3446666666666666
1002
+ name: Dot Recall@10
1003
+ - type: dot_ndcg@10
1004
+ value: 0.3317925768694159
1005
+ name: Dot Ndcg@10
1006
+ - type: dot_mrr@10
1007
+ value: 0.533222222222222
1008
+ name: Dot Mrr@10
1009
+ - type: dot_map@100
1010
+ value: 0.25209583120462153
1011
+ name: Dot Map@100
1012
+ - type: query_active_dims
1013
+ value: 134.1999969482422
1014
+ name: Query Active Dims
1015
+ - type: query_sparsity_ratio
1016
+ value: 0.9956031715828503
1017
+ name: Query Sparsity Ratio
1018
+ - type: corpus_active_dims
1019
+ value: 164.88478088378906
1020
+ name: Corpus Active Dims
1021
+ - type: corpus_sparsity_ratio
1022
+ value: 0.9945978382516287
1023
+ name: Corpus Sparsity Ratio
1024
+ - task:
1025
+ type: sparse-information-retrieval
1026
+ name: Sparse Information Retrieval
1027
+ dataset:
1028
+ name: NanoArguAna
1029
+ type: NanoArguAna
1030
+ metrics:
1031
+ - type: dot_accuracy@1
1032
+ value: 0.1
1033
+ name: Dot Accuracy@1
1034
+ - type: dot_accuracy@3
1035
+ value: 0.52
1036
+ name: Dot Accuracy@3
1037
+ - type: dot_accuracy@5
1038
+ value: 0.62
1039
+ name: Dot Accuracy@5
1040
+ - type: dot_accuracy@10
1041
+ value: 0.8
1042
+ name: Dot Accuracy@10
1043
+ - type: dot_precision@1
1044
+ value: 0.1
1045
+ name: Dot Precision@1
1046
+ - type: dot_precision@3
1047
+ value: 0.1733333333333333
1048
+ name: Dot Precision@3
1049
+ - type: dot_precision@5
1050
+ value: 0.12400000000000003
1051
+ name: Dot Precision@5
1052
+ - type: dot_precision@10
1053
+ value: 0.08
1054
+ name: Dot Precision@10
1055
+ - type: dot_recall@1
1056
+ value: 0.1
1057
+ name: Dot Recall@1
1058
+ - type: dot_recall@3
1059
+ value: 0.52
1060
+ name: Dot Recall@3
1061
+ - type: dot_recall@5
1062
+ value: 0.62
1063
+ name: Dot Recall@5
1064
+ - type: dot_recall@10
1065
+ value: 0.8
1066
+ name: Dot Recall@10
1067
+ - type: dot_ndcg@10
1068
+ value: 0.44172833183312293
1069
+ name: Dot Ndcg@10
1070
+ - type: dot_mrr@10
1071
+ value: 0.32852380952380955
1072
+ name: Dot Mrr@10
1073
+ - type: dot_map@100
1074
+ value: 0.3339302930314127
1075
+ name: Dot Map@100
1076
+ - type: query_active_dims
1077
+ value: 152.0399932861328
1078
+ name: Query Active Dims
1079
+ - type: query_sparsity_ratio
1080
+ value: 0.9950186752740275
1081
+ name: Query Sparsity Ratio
1082
+ - type: corpus_active_dims
1083
+ value: 149.56478881835938
1084
+ name: Corpus Active Dims
1085
+ - type: corpus_sparsity_ratio
1086
+ value: 0.9950997710235777
1087
+ name: Corpus Sparsity Ratio
1088
+ - task:
1089
+ type: sparse-information-retrieval
1090
+ name: Sparse Information Retrieval
1091
+ dataset:
1092
+ name: NanoSciFact
1093
+ type: NanoSciFact
1094
+ metrics:
1095
+ - type: dot_accuracy@1
1096
+ value: 0.46
1097
+ name: Dot Accuracy@1
1098
+ - type: dot_accuracy@3
1099
+ value: 0.56
1100
+ name: Dot Accuracy@3
1101
+ - type: dot_accuracy@5
1102
+ value: 0.6
1103
+ name: Dot Accuracy@5
1104
+ - type: dot_accuracy@10
1105
+ value: 0.68
1106
+ name: Dot Accuracy@10
1107
+ - type: dot_precision@1
1108
+ value: 0.46
1109
+ name: Dot Precision@1
1110
+ - type: dot_precision@3
1111
+ value: 0.20666666666666667
1112
+ name: Dot Precision@3
1113
+ - type: dot_precision@5
1114
+ value: 0.14
1115
+ name: Dot Precision@5
1116
+ - type: dot_precision@10
1117
+ value: 0.07800000000000001
1118
+ name: Dot Precision@10
1119
+ - type: dot_recall@1
1120
+ value: 0.425
1121
+ name: Dot Recall@1
1122
+ - type: dot_recall@3
1123
+ value: 0.545
1124
+ name: Dot Recall@3
1125
+ - type: dot_recall@5
1126
+ value: 0.59
1127
+ name: Dot Recall@5
1128
+ - type: dot_recall@10
1129
+ value: 0.67
1130
+ name: Dot Recall@10
1131
+ - type: dot_ndcg@10
1132
+ value: 0.5519450641329704
1133
+ name: Dot Ndcg@10
1134
+ - type: dot_mrr@10
1135
+ value: 0.5232698412698412
1136
+ name: Dot Mrr@10
1137
+ - type: dot_map@100
1138
+ value: 0.5187507919958133
1139
+ name: Dot Map@100
1140
+ - type: query_active_dims
1141
+ value: 138.32000732421875
1142
+ name: Query Active Dims
1143
+ - type: query_sparsity_ratio
1144
+ value: 0.9954681866416284
1145
+ name: Query Sparsity Ratio
1146
+ - type: corpus_active_dims
1147
+ value: 166.03871154785156
1148
+ name: Corpus Active Dims
1149
+ - type: corpus_sparsity_ratio
1150
+ value: 0.9945600317296425
1151
+ name: Corpus Sparsity Ratio
1152
+ - task:
1153
+ type: sparse-information-retrieval
1154
+ name: Sparse Information Retrieval
1155
+ dataset:
1156
+ name: NanoTouche2020
1157
+ type: NanoTouche2020
1158
+ metrics:
1159
+ - type: dot_accuracy@1
1160
+ value: 0.6326530612244898
1161
+ name: Dot Accuracy@1
1162
+ - type: dot_accuracy@3
1163
+ value: 0.8775510204081632
1164
+ name: Dot Accuracy@3
1165
+ - type: dot_accuracy@5
1166
+ value: 0.9591836734693877
1167
+ name: Dot Accuracy@5
1168
+ - type: dot_accuracy@10
1169
+ value: 1.0
1170
+ name: Dot Accuracy@10
1171
+ - type: dot_precision@1
1172
+ value: 0.6326530612244898
1173
+ name: Dot Precision@1
1174
+ - type: dot_precision@3
1175
+ value: 0.6054421768707483
1176
+ name: Dot Precision@3
1177
+ - type: dot_precision@5
1178
+ value: 0.5387755102040817
1179
+ name: Dot Precision@5
1180
+ - type: dot_precision@10
1181
+ value: 0.43673469387755104
1182
+ name: Dot Precision@10
1183
+ - type: dot_recall@1
1184
+ value: 0.04531781391284345
1185
+ name: Dot Recall@1
1186
+ - type: dot_recall@3
1187
+ value: 0.12723496235073023
1188
+ name: Dot Recall@3
1189
+ - type: dot_recall@5
1190
+ value: 0.18244054845345592
1191
+ name: Dot Recall@5
1192
+ - type: dot_recall@10
1193
+ value: 0.2837929445033988
1194
+ name: Dot Recall@10
1195
+ - type: dot_ndcg@10
1196
+ value: 0.5015858619400403
1197
+ name: Dot Ndcg@10
1198
+ - type: dot_mrr@10
1199
+ value: 0.767201166180758
1200
+ name: Dot Mrr@10
1201
+ - type: dot_map@100
1202
+ value: 0.3675943031666616
1203
+ name: Dot Map@100
1204
+ - type: query_active_dims
1205
+ value: 52.67346954345703
1206
+ name: Query Active Dims
1207
+ - type: query_sparsity_ratio
1208
+ value: 0.9982742458048799
1209
+ name: Query Sparsity Ratio
1210
+ - type: corpus_active_dims
1211
+ value: 147.12759399414062
1212
+ name: Corpus Active Dims
1213
+ - type: corpus_sparsity_ratio
1214
+ value: 0.9951796214535699
1215
+ name: Corpus Sparsity Ratio
1216
+ ---
1217
+
1218
+ # splade-distilbert-base-uncased trained on Natural Questions
1219
+
1220
+ This is a [SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
1221
+
1222
+ ## Model Details
1223
+
1224
+ ### Model Description
1225
+ - **Model Type:** SPLADE Sparse Encoder
1226
+ - **Base model:** [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) <!-- at revision 12040accade4e8a0f71eabdb258fecc2e7e948be -->
1227
+ - **Maximum Sequence Length:** 256 tokens
1228
+ - **Output Dimensionality:** 30522 dimensions
1229
+ - **Similarity Function:** Dot Product
1230
+ - **Training Dataset:**
1231
+ - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
1232
+ - **Language:** en
1233
+ - **License:** apache-2.0
1234
+
1235
+ ### Model Sources
1236
+
1237
+ - **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
1238
+ - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
1239
+ - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
1240
+ - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
1241
+
1242
+ ### Full Model Architecture
1243
+
1244
+ ```
1245
+ SparseEncoder(
1246
+ (0): MLMTransformer({'max_seq_length': 256, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
1247
+ (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
1248
+ )
1249
+ ```
1250
+
1251
+ ## Usage
1252
+
1253
+ ### Direct Usage (Sentence Transformers)
1254
+
1255
+ First install the Sentence Transformers library:
1256
+
1257
+ ```bash
1258
+ pip install -U sentence-transformers
1259
+ ```
1260
+
1261
+ Then you can load this model and run inference.
1262
+ ```python
1263
+ from sentence_transformers import SparseEncoder
1264
+
1265
+ # Download from the 🤗 Hub
1266
+ model = SparseEncoder("tomaarsen/splade-distilbert-base-uncased-nq-updated-sparsity")
1267
+ # Run inference
1268
+ sentences = [
1269
+ 'is send in the clowns from a musical',
1270
+ 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
1271
+ 'The Suite Life on Deck The Suite Life on Deck is an American sitcom that aired on Disney Channel from September 26, 2008 to May 6, 2011. It is a sequel/spin-off of the Disney Channel Original Series The Suite Life of Zack & Cody. The series follows twin brothers Zack and Cody Martin and hotel heiress London Tipton in a new setting, the SS Tipton, where they attend classes at "Seven Seas High School" and meet Bailey Pickett while Mr. Moseby manages the ship. The ship travels around the world to nations such as Italy, France, Greece, India, Sweden and the United Kingdom where the characters experience different cultures, adventures, and situations.[1]',
1272
+ ]
1273
+ embeddings = model.encode(sentences)
1274
+ print(embeddings.shape)
1275
+ # (3, 30522)
1276
+
1277
+ # Get the similarity scores for the embeddings
1278
+ similarities = model.similarity(embeddings, embeddings)
1279
+ print(similarities.shape)
1280
+ # [3, 3]
1281
+ ```
1282
+
1283
+ <!--
1284
+ ### Direct Usage (Transformers)
1285
+
1286
+ <details><summary>Click to see the direct usage in Transformers</summary>
1287
+
1288
+ </details>
1289
+ -->
1290
+
1291
+ <!--
1292
+ ### Downstream Usage (Sentence Transformers)
1293
+
1294
+ You can finetune this model on your own dataset.
1295
+
1296
+ <details><summary>Click to expand</summary>
1297
+
1298
+ </details>
1299
+ -->
1300
+
1301
+ <!--
1302
+ ### Out-of-Scope Use
1303
+
1304
+ *List how the model may foreseeably be misused and address what users ought not to do with the model.*
1305
+ -->
1306
+
1307
+ ## Evaluation
1308
+
1309
+ ### Metrics
1310
+
1311
+ #### Sparse Information Retrieval
1312
+
1313
+ * Datasets: `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
1314
+ * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
1315
+
1316
+ | Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
1317
+ |:----------------------|:------------|:-------------|:-----------|:-----------------|:------------|:-----------|:-------------|:-------------|:-------------------|:------------|:------------|:------------|:---------------|
1318
+ | dot_accuracy@1 | 0.26 | 0.36 | 0.38 | 0.26 | 0.62 | 0.64 | 0.24 | 0.74 | 0.86 | 0.42 | 0.1 | 0.46 | 0.6327 |
1319
+ | dot_accuracy@3 | 0.5 | 0.46 | 0.58 | 0.36 | 0.84 | 0.84 | 0.44 | 0.9 | 0.94 | 0.6 | 0.52 | 0.56 | 0.8776 |
1320
+ | dot_accuracy@5 | 0.64 | 0.48 | 0.68 | 0.48 | 0.9 | 0.9 | 0.52 | 0.94 | 0.98 | 0.72 | 0.62 | 0.6 | 0.9592 |
1321
+ | dot_accuracy@10 | 0.74 | 0.58 | 0.7 | 0.58 | 0.92 | 0.98 | 0.66 | 0.96 | 1.0 | 0.78 | 0.8 | 0.68 | 1.0 |
1322
+ | dot_precision@1 | 0.26 | 0.36 | 0.38 | 0.26 | 0.62 | 0.64 | 0.24 | 0.74 | 0.86 | 0.42 | 0.1 | 0.46 | 0.6327 |
1323
+ | dot_precision@3 | 0.1667 | 0.32 | 0.1933 | 0.1333 | 0.54 | 0.2867 | 0.1667 | 0.42 | 0.3667 | 0.28 | 0.1733 | 0.2067 | 0.6054 |
1324
+ | dot_precision@5 | 0.128 | 0.264 | 0.136 | 0.108 | 0.492 | 0.188 | 0.128 | 0.276 | 0.248 | 0.236 | 0.124 | 0.14 | 0.5388 |
1325
+ | dot_precision@10 | 0.074 | 0.232 | 0.07 | 0.074 | 0.434 | 0.104 | 0.092 | 0.156 | 0.132 | 0.168 | 0.08 | 0.078 | 0.4367 |
1326
+ | dot_recall@1 | 0.26 | 0.0197 | 0.35 | 0.1283 | 0.0826 | 0.6167 | 0.1359 | 0.37 | 0.7707 | 0.086 | 0.1 | 0.425 | 0.0453 |
1327
+ | dot_recall@3 | 0.5 | 0.0496 | 0.55 | 0.1883 | 0.1457 | 0.8167 | 0.2892 | 0.63 | 0.8847 | 0.1757 | 0.52 | 0.545 | 0.1272 |
1328
+ | dot_recall@5 | 0.64 | 0.0659 | 0.65 | 0.2467 | 0.1854 | 0.8767 | 0.3327 | 0.69 | 0.936 | 0.2447 | 0.62 | 0.59 | 0.1824 |
1329
+ | dot_recall@10 | 0.74 | 0.0889 | 0.66 | 0.3033 | 0.2804 | 0.9433 | 0.4233 | 0.78 | 0.9733 | 0.3447 | 0.8 | 0.67 | 0.2838 |
1330
+ | **dot_ndcg@10** | **0.4945** | **0.2727** | **0.5212** | **0.2565** | **0.5342** | **0.7891** | **0.3255** | **0.7118** | **0.9096** | **0.3318** | **0.4417** | **0.5519** | **0.5016** |
1331
+ | dot_mrr@10 | 0.4166 | 0.4239 | 0.4965 | 0.3453 | 0.7352 | 0.7517 | 0.3704 | 0.8269 | 0.9042 | 0.5332 | 0.3285 | 0.5233 | 0.7672 |
1332
+ | dot_map@100 | 0.4269 | 0.1106 | 0.4802 | 0.2063 | 0.4148 | 0.7337 | 0.2649 | 0.6308 | 0.8825 | 0.2521 | 0.3339 | 0.5188 | 0.3676 |
1333
+ | query_active_dims | 70.22 | 85.58 | 55.08 | 86.26 | 55.94 | 84.86 | 65.44 | 81.9 | 58.36 | 134.2 | 152.04 | 138.32 | 52.6735 |
1334
+ | query_sparsity_ratio | 0.9977 | 0.9972 | 0.9982 | 0.9972 | 0.9982 | 0.9972 | 0.9979 | 0.9973 | 0.9981 | 0.9956 | 0.995 | 0.9955 | 0.9983 |
1335
+ | corpus_active_dims | 125.4981 | 182.9797 | 114.7911 | 128.049 | 125.4017 | 142.3433 | 132.0273 | 142.9919 | 64.7033 | 164.8848 | 149.5648 | 166.0387 | 147.1276 |
1336
+ | corpus_sparsity_ratio | 0.9959 | 0.994 | 0.9962 | 0.9958 | 0.9959 | 0.9953 | 0.9957 | 0.9953 | 0.9979 | 0.9946 | 0.9951 | 0.9946 | 0.9952 |
1337
+
1338
+ #### Sparse Nano BEIR
1339
+
1340
+ * Dataset: `NanoBEIR_mean`
1341
+ * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
1342
+ ```json
1343
+ {
1344
+ "dataset_names": [
1345
+ "msmarco",
1346
+ "nfcorpus",
1347
+ "nq"
1348
+ ]
1349
+ }
1350
+ ```
1351
+
1352
+ | Metric | Value |
1353
+ |:----------------------|:----------|
1354
+ | dot_accuracy@1 | 0.32 |
1355
+ | dot_accuracy@3 | 0.52 |
1356
+ | dot_accuracy@5 | 0.58 |
1357
+ | dot_accuracy@10 | 0.6533 |
1358
+ | dot_precision@1 | 0.32 |
1359
+ | dot_precision@3 | 0.2289 |
1360
+ | dot_precision@5 | 0.176 |
1361
+ | dot_precision@10 | 0.1227 |
1362
+ | dot_recall@1 | 0.213 |
1363
+ | dot_recall@3 | 0.3763 |
1364
+ | dot_recall@5 | 0.4358 |
1365
+ | dot_recall@10 | 0.4963 |
1366
+ | **dot_ndcg@10** | **0.425** |
1367
+ | dot_mrr@10 | 0.4317 |
1368
+ | dot_map@100 | 0.3387 |
1369
+ | query_active_dims | 61.2733 |
1370
+ | query_sparsity_ratio | 0.998 |
1371
+ | corpus_active_dims | 119.8034 |
1372
+ | corpus_sparsity_ratio | 0.9961 |
1373
+
1374
+ #### Sparse Nano BEIR
1375
+
1376
+ * Dataset: `NanoBEIR_mean`
1377
+ * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
1378
+ ```json
1379
+ {
1380
+ "dataset_names": [
1381
+ "climatefever",
1382
+ "dbpedia",
1383
+ "fever",
1384
+ "fiqa2018",
1385
+ "hotpotqa",
1386
+ "msmarco",
1387
+ "nfcorpus",
1388
+ "nq",
1389
+ "quoraretrieval",
1390
+ "scidocs",
1391
+ "arguana",
1392
+ "scifact",
1393
+ "touche2020"
1394
+ ]
1395
+ }
1396
+ ```
1397
+
1398
+ | Metric | Value |
1399
+ |:----------------------|:-----------|
1400
+ | dot_accuracy@1 | 0.4594 |
1401
+ | dot_accuracy@3 | 0.6475 |
1402
+ | dot_accuracy@5 | 0.7246 |
1403
+ | dot_accuracy@10 | 0.7985 |
1404
+ | dot_precision@1 | 0.4594 |
1405
+ | dot_precision@3 | 0.2968 |
1406
+ | dot_precision@5 | 0.2313 |
1407
+ | dot_precision@10 | 0.1639 |
1408
+ | dot_recall@1 | 0.2608 |
1409
+ | dot_recall@3 | 0.4171 |
1410
+ | dot_recall@5 | 0.4816 |
1411
+ | dot_recall@10 | 0.5609 |
1412
+ | **dot_ndcg@10** | **0.5109** |
1413
+ | dot_mrr@10 | 0.571 |
1414
+ | dot_map@100 | 0.4326 |
1415
+ | query_active_dims | 86.221 |
1416
+ | query_sparsity_ratio | 0.9972 |
1417
+ | corpus_active_dims | 137.4155 |
1418
+ | corpus_sparsity_ratio | 0.9955 |
1419
+
1420
+ <!--
1421
+ ## Bias, Risks and Limitations
1422
+
1423
+ *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
1424
+ -->
1425
+
1426
+ <!--
1427
+ ### Recommendations
1428
+
1429
+ *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
1430
+ -->
1431
+
1432
+ ## Training Details
1433
+
1434
+ ### Training Dataset
1435
+
1436
+ #### natural-questions
1437
+
1438
+ * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
1439
+ * Size: 99,000 training samples
1440
+ * Columns: <code>query</code> and <code>answer</code>
1441
+ * Approximate statistics based on the first 1000 samples:
1442
+ | | query | answer |
1443
+ |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
1444
+ | type | string | string |
1445
+ | details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
1446
+ * Samples:
1447
+ | query | answer |
1448
+ |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
1449
+ | <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
1450
+ | <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
1451
+ | <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
1452
+ * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
1453
+ ```json
1454
+ {
1455
+ "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
1456
+ "lambda_corpus": 3e-05,
1457
+ "lambda_query": 5e-05
1458
+ }
1459
+ ```
1460
+
1461
+ ### Evaluation Dataset
1462
+
1463
+ #### natural-questions
1464
+
1465
+ * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
1466
+ * Size: 1,000 evaluation samples
1467
+ * Columns: <code>query</code> and <code>answer</code>
1468
+ * Approximate statistics based on the first 1000 samples:
1469
+ | | query | answer |
1470
+ |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
1471
+ | type | string | string |
1472
+ | details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
1473
+ * Samples:
1474
+ | query | answer |
1475
+ |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
1476
+ | <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
1477
+ | <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
1478
+ | <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
1479
+ * Loss: [<code>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
1480
+ ```json
1481
+ {
1482
+ "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
1483
+ "lambda_corpus": 3e-05,
1484
+ "lambda_query": 5e-05
1485
+ }
1486
+ ```
1487
+
1488
+ ### Training Hyperparameters
1489
+ #### Non-Default Hyperparameters
1490
+
1491
+ - `eval_strategy`: steps
1492
+ - `per_device_train_batch_size`: 12
1493
+ - `per_device_eval_batch_size`: 12
1494
+ - `learning_rate`: 2e-05
1495
+ - `num_train_epochs`: 1
1496
+ - `bf16`: True
1497
+ - `load_best_model_at_end`: True
1498
+ - `batch_sampler`: no_duplicates
1499
+
1500
+ #### All Hyperparameters
1501
+ <details><summary>Click to expand</summary>
1502
+
1503
+ - `overwrite_output_dir`: False
1504
+ - `do_predict`: False
1505
+ - `eval_strategy`: steps
1506
+ - `prediction_loss_only`: True
1507
+ - `per_device_train_batch_size`: 12
1508
+ - `per_device_eval_batch_size`: 12
1509
+ - `per_gpu_train_batch_size`: None
1510
+ - `per_gpu_eval_batch_size`: None
1511
+ - `gradient_accumulation_steps`: 1
1512
+ - `eval_accumulation_steps`: None
1513
+ - `torch_empty_cache_steps`: None
1514
+ - `learning_rate`: 2e-05
1515
+ - `weight_decay`: 0.0
1516
+ - `adam_beta1`: 0.9
1517
+ - `adam_beta2`: 0.999
1518
+ - `adam_epsilon`: 1e-08
1519
+ - `max_grad_norm`: 1.0
1520
+ - `num_train_epochs`: 1
1521
+ - `max_steps`: -1
1522
+ - `lr_scheduler_type`: linear
1523
+ - `lr_scheduler_kwargs`: {}
1524
+ - `warmup_ratio`: 0.0
1525
+ - `warmup_steps`: 0
1526
+ - `log_level`: passive
1527
+ - `log_level_replica`: warning
1528
+ - `log_on_each_node`: True
1529
+ - `logging_nan_inf_filter`: True
1530
+ - `save_safetensors`: True
1531
+ - `save_on_each_node`: False
1532
+ - `save_only_model`: False
1533
+ - `restore_callback_states_from_checkpoint`: False
1534
+ - `no_cuda`: False
1535
+ - `use_cpu`: False
1536
+ - `use_mps_device`: False
1537
+ - `seed`: 42
1538
+ - `data_seed`: None
1539
+ - `jit_mode_eval`: False
1540
+ - `use_ipex`: False
1541
+ - `bf16`: True
1542
+ - `fp16`: False
1543
+ - `fp16_opt_level`: O1
1544
+ - `half_precision_backend`: auto
1545
+ - `bf16_full_eval`: False
1546
+ - `fp16_full_eval`: False
1547
+ - `tf32`: None
1548
+ - `local_rank`: 0
1549
+ - `ddp_backend`: None
1550
+ - `tpu_num_cores`: None
1551
+ - `tpu_metrics_debug`: False
1552
+ - `debug`: []
1553
+ - `dataloader_drop_last`: False
1554
+ - `dataloader_num_workers`: 0
1555
+ - `dataloader_prefetch_factor`: None
1556
+ - `past_index`: -1
1557
+ - `disable_tqdm`: False
1558
+ - `remove_unused_columns`: True
1559
+ - `label_names`: None
1560
+ - `load_best_model_at_end`: True
1561
+ - `ignore_data_skip`: False
1562
+ - `fsdp`: []
1563
+ - `fsdp_min_num_params`: 0
1564
+ - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
1565
+ - `fsdp_transformer_layer_cls_to_wrap`: None
1566
+ - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
1567
+ - `deepspeed`: None
1568
+ - `label_smoothing_factor`: 0.0
1569
+ - `optim`: adamw_torch
1570
+ - `optim_args`: None
1571
+ - `adafactor`: False
1572
+ - `group_by_length`: False
1573
+ - `length_column_name`: length
1574
+ - `ddp_find_unused_parameters`: None
1575
+ - `ddp_bucket_cap_mb`: None
1576
+ - `ddp_broadcast_buffers`: False
1577
+ - `dataloader_pin_memory`: True
1578
+ - `dataloader_persistent_workers`: False
1579
+ - `skip_memory_metrics`: True
1580
+ - `use_legacy_prediction_loop`: False
1581
+ - `push_to_hub`: False
1582
+ - `resume_from_checkpoint`: None
1583
+ - `hub_model_id`: None
1584
+ - `hub_strategy`: every_save
1585
+ - `hub_private_repo`: None
1586
+ - `hub_always_push`: False
1587
+ - `gradient_checkpointing`: False
1588
+ - `gradient_checkpointing_kwargs`: None
1589
+ - `include_inputs_for_metrics`: False
1590
+ - `include_for_metrics`: []
1591
+ - `eval_do_concat_batches`: True
1592
+ - `fp16_backend`: auto
1593
+ - `push_to_hub_model_id`: None
1594
+ - `push_to_hub_organization`: None
1595
+ - `mp_parameters`:
1596
+ - `auto_find_batch_size`: False
1597
+ - `full_determinism`: False
1598
+ - `torchdynamo`: None
1599
+ - `ray_scope`: last
1600
+ - `ddp_timeout`: 1800
1601
+ - `torch_compile`: False
1602
+ - `torch_compile_backend`: None
1603
+ - `torch_compile_mode`: None
1604
+ - `dispatch_batches`: None
1605
+ - `split_batches`: None
1606
+ - `include_tokens_per_second`: False
1607
+ - `include_num_input_tokens_seen`: False
1608
+ - `neftune_noise_alpha`: None
1609
+ - `optim_target_modules`: None
1610
+ - `batch_eval_metrics`: False
1611
+ - `eval_on_start`: False
1612
+ - `use_liger_kernel`: False
1613
+ - `eval_use_gather_object`: False
1614
+ - `average_tokens_across_devices`: False
1615
+ - `prompts`: None
1616
+ - `batch_sampler`: no_duplicates
1617
+ - `multi_dataset_batch_sampler`: proportional
1618
+
1619
+ </details>
1620
+
1621
+ ### Training Logs
1622
+ | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 |
1623
+ |:-------:|:--------:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|
1624
+ | 0.0242 | 200 | 4.7655 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1625
+ | 0.0485 | 400 | 0.168 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1626
+ | 0.0727 | 600 | 0.0672 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1627
+ | 0.0970 | 800 | 0.0533 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1628
+ | 0.1212 | 1000 | 0.0605 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1629
+ | 0.1455 | 1200 | 0.051 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1630
+ | 0.1697 | 1400 | 0.0244 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1631
+ | 0.1939 | 1600 | 0.0306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1632
+ | 0.2 | 1650 | - | 0.0220 | 0.4946 | 0.2654 | 0.4801 | 0.4134 | - | - | - | - | - | - | - | - | - | - |
1633
+ | 0.2182 | 1800 | 0.0246 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1634
+ | 0.2424 | 2000 | 0.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1635
+ | 0.2667 | 2200 | 0.0322 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1636
+ | 0.2909 | 2400 | 0.0316 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1637
+ | 0.3152 | 2600 | 0.029 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1638
+ | 0.3394 | 2800 | 0.0145 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1639
+ | 0.3636 | 3000 | 0.0312 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1640
+ | 0.3879 | 3200 | 0.0232 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1641
+ | 0.4 | 3300 | - | 0.0155 | 0.4420 | 0.2753 | 0.5112 | 0.4095 | - | - | - | - | - | - | - | - | - | - |
1642
+ | 0.4121 | 3400 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1643
+ | 0.4364 | 3600 | 0.0233 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1644
+ | 0.4606 | 3800 | 0.0224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1645
+ | 0.4848 | 4000 | 0.0126 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1646
+ | 0.5091 | 4200 | 0.0269 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1647
+ | 0.5333 | 4400 | 0.0245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1648
+ | 0.5576 | 4600 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1649
+ | 0.5818 | 4800 | 0.0276 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1650
+ | 0.6 | 4950 | - | 0.0098 | 0.4901 | 0.2460 | 0.5124 | 0.4162 | - | - | - | - | - | - | - | - | - | - |
1651
+ | 0.6061 | 5000 | 0.0193 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1652
+ | 0.6303 | 5200 | 0.0223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1653
+ | 0.6545 | 5400 | 0.0117 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1654
+ | 0.6788 | 5600 | 0.0254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1655
+ | 0.7030 | 5800 | 0.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1656
+ | 0.7273 | 6000 | 0.0271 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1657
+ | 0.7515 | 6200 | 0.02 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1658
+ | 0.7758 | 6400 | 0.0088 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1659
+ | **0.8** | **6600** | **0.0125** | **0.0233** | **0.4945** | **0.2727** | **0.5212** | **0.4294** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** |
1660
+ | 0.8242 | 6800 | 0.0214 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1661
+ | 0.8485 | 7000 | 0.0147 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1662
+ | 0.8727 | 7200 | 0.0192 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1663
+ | 0.8970 | 7400 | 0.0135 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1664
+ | 0.9212 | 7600 | 0.0086 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1665
+ | 0.9455 | 7800 | 0.0205 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1666
+ | 0.9697 | 8000 | 0.0267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1667
+ | 0.9939 | 8200 | 0.0149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
1668
+ | 1.0 | 8250 | - | 0.0174 | 0.4954 | 0.2631 | 0.5163 | 0.4250 | - | - | - | - | - | - | - | - | - | - |
1669
+ | -1 | -1 | - | - | 0.4945 | 0.2727 | 0.5212 | 0.5109 | 0.2565 | 0.5342 | 0.7891 | 0.3255 | 0.7118 | 0.9096 | 0.3318 | 0.4417 | 0.5519 | 0.5016 |
1670
+
1671
+ * The bold row denotes the saved checkpoint.
1672
+
1673
+ ### Environmental Impact
1674
+ Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
1675
+ - **Energy Consumed**: 0.083 kWh
1676
+ - **Carbon Emitted**: 0.032 kg of CO2
1677
+ - **Hours Used**: 0.285 hours
1678
+
1679
+ ### Training Hardware
1680
+ - **On Cloud**: No
1681
+ - **GPU Model**: 1 x NVIDIA GeForce RTX 3090
1682
+ - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
1683
+ - **RAM Size**: 31.78 GB
1684
+
1685
+ ### Framework Versions
1686
+ - Python: 3.11.6
1687
+ - Sentence Transformers: 4.2.0.dev0
1688
+ - Transformers: 4.49.0
1689
+ - PyTorch: 2.6.0+cu124
1690
+ - Accelerate: 1.5.1
1691
+ - Datasets: 2.21.0
1692
+ - Tokenizers: 0.21.1
1693
+
1694
+ ## Citation
1695
+
1696
+ ### BibTeX
1697
+
1698
+ #### Sentence Transformers
1699
+ ```bibtex
1700
+ @inproceedings{reimers-2019-sentence-bert,
1701
+ title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
1702
+ author = "Reimers, Nils and Gurevych, Iryna",
1703
+ booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
1704
+ month = "11",
1705
+ year = "2019",
1706
+ publisher = "Association for Computational Linguistics",
1707
+ url = "https://arxiv.org/abs/1908.10084",
1708
+ }
1709
+ ```
1710
+
1711
+ #### SpladeLoss
1712
+ ```bibtex
1713
+ @misc{formal2022distillationhardnegativesampling,
1714
+ title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
1715
+ author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
1716
+ year={2022},
1717
+ eprint={2205.04733},
1718
+ archivePrefix={arXiv},
1719
+ primaryClass={cs.IR},
1720
+ url={https://arxiv.org/abs/2205.04733},
1721
+ }
1722
+ ```
1723
+
1724
+ #### SparseMultipleNegativesRankingLoss
1725
+ ```bibtex
1726
+ @misc{henderson2017efficient,
1727
+ title={Efficient Natural Language Response Suggestion for Smart Reply},
1728
+ author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
1729
+ year={2017},
1730
+ eprint={1705.00652},
1731
+ archivePrefix={arXiv},
1732
+ primaryClass={cs.CL}
1733
+ }
1734
+ ```
1735
+
1736
+ #### FlopsLoss
1737
+ ```bibtex
1738
+ @article{paria2020minimizing,
1739
+ title={Minimizing flops to learn efficient sparse representations},
1740
+ author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
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+ journal={arXiv preprint arXiv:2004.05665},
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+ year={2020}
1743
+ }
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+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
1759
+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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