Sentence Similarity
sentence-transformers
PyTorch
TensorFlow
Rust
Safetensors
Transformers
English
bert
feature-extraction
Inference Endpoints
5 papers
Files changed (1) hide show
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README.md CHANGED
@@ -4,6 +4,7 @@ tags:
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
 
7
  language: en
8
  license: apache-2.0
9
  datasets:
@@ -28,10 +29,2846 @@ datasets:
28
  - embedding-data/SPECTER
29
  - embedding-data/PAQ_pairs
30
  - embedding-data/WikiAnswers
31
-
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
32
  ---
33
 
34
-
35
  # all-MiniLM-L6-v2
36
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
37
 
@@ -93,7 +2930,7 @@ print(sentence_embeddings)
93
 
94
  ## Evaluation Results
95
 
96
- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L6-v2)
97
 
98
  ------
99
 
 
4
  - sentence-transformers
5
  - feature-extraction
6
  - sentence-similarity
7
+ - mteb
8
  language: en
9
  license: apache-2.0
10
  datasets:
 
29
  - embedding-data/SPECTER
30
  - embedding-data/PAQ_pairs
31
  - embedding-data/WikiAnswers
32
+ model-index:
33
+ - name: all-MiniLM-L6-v2
34
+ results:
35
+ - task:
36
+ type: Classification
37
+ dataset:
38
+ type: mteb/amazon_counterfactual
39
+ name: MTEB AmazonCounterfactualClassification (en)
40
+ config: en
41
+ split: test
42
+ revision: 2d8a100785abf0ae21420d2a55b0c56e3e1ea996
43
+ metrics:
44
+ - type: accuracy
45
+ value: 64.14925373134331
46
+ - type: ap
47
+ value: 27.237875815186907
48
+ - type: f1
49
+ value: 58.03105716318715
50
+ - task:
51
+ type: Classification
52
+ dataset:
53
+ type: mteb/amazon_polarity
54
+ name: MTEB AmazonPolarityClassification
55
+ config: default
56
+ split: test
57
+ revision: 80714f8dcf8cefc218ef4f8c5a966dd83f75a0e1
58
+ metrics:
59
+ - type: accuracy
60
+ value: 62.582975
61
+ - type: ap
62
+ value: 58.26562634146188
63
+ - type: f1
64
+ value: 62.304673961004156
65
+ - task:
66
+ type: Classification
67
+ dataset:
68
+ type: mteb/amazon_reviews_multi
69
+ name: MTEB AmazonReviewsClassification (en)
70
+ config: en
71
+ split: test
72
+ revision: c379a6705fec24a2493fa68e011692605f44e119
73
+ metrics:
74
+ - type: accuracy
75
+ value: 31.785999999999998
76
+ - type: f1
77
+ value: 31.40726949960717
78
+ - task:
79
+ type: Retrieval
80
+ dataset:
81
+ type: arguana
82
+ name: MTEB ArguAna
83
+ config: default
84
+ split: test
85
+ revision: 5b3e3697907184a9b77a3c99ee9ea1a9cbb1e4e3
86
+ metrics:
87
+ - type: map_at_1
88
+ value: 25.605
89
+ - type: map_at_10
90
+ value: 41.165
91
+ - type: map_at_100
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+ value: 38.981
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+ value: 25.605
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+ - type: ndcg_at_10
102
+ value: 50.166999999999994
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+ - type: ndcg_at_100
104
+ value: 54.534000000000006
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+ - type: ndcg_at_1000
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+ value: 54.772
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+ value: 44.876
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+ - type: precision_at_1000
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+ value: 0.1
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+ value: 16.500999999999998
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+ - type: precision_at_5
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+ value: 12.546
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+ - type: recall_at_1
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+ value: 25.605
125
+ - type: recall_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
130
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+ - type: recall_at_3
132
+ value: 49.502
133
+ - type: recall_at_5
134
+ value: 62.731
135
+ - task:
136
+ type: Clustering
137
+ dataset:
138
+ type: mteb/arxiv-clustering-p2p
139
+ name: MTEB ArxivClusteringP2P
140
+ config: default
141
+ split: test
142
+ revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
143
+ metrics:
144
+ - type: v_measure
145
+ value: 46.54595079050156
146
+ - task:
147
+ type: Clustering
148
+ dataset:
149
+ type: mteb/arxiv-clustering-s2s
150
+ name: MTEB ArxivClusteringS2S
151
+ config: default
152
+ split: test
153
+ revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
154
+ metrics:
155
+ - type: v_measure
156
+ value: 37.85709823840442
157
+ - task:
158
+ type: Reranking
159
+ dataset:
160
+ type: mteb/askubuntudupquestions-reranking
161
+ name: MTEB AskUbuntuDupQuestions
162
+ config: default
163
+ split: test
164
+ revision: 4d853f94cd57d85ec13805aeeac3ae3e5eb4c49c
165
+ metrics:
166
+ - type: map
167
+ value: 63.47681681237331
168
+ - type: mrr
169
+ value: 77.08657608934617
170
+ - task:
171
+ type: STS
172
+ dataset:
173
+ type: mteb/biosses-sts
174
+ name: MTEB BIOSSES
175
+ config: default
176
+ split: test
177
+ revision: 9ee918f184421b6bd48b78f6c714d86546106103
178
+ metrics:
179
+ - type: cos_sim_pearson
180
+ value: 84.41897516342782
181
+ - type: cos_sim_spearman
182
+ value: 81.64041444909368
183
+ - type: euclidean_pearson
184
+ value: 82.67500318274435
185
+ - type: euclidean_spearman
186
+ value: 81.64041444909368
187
+ - type: manhattan_pearson
188
+ value: 82.35165974372227
189
+ - type: manhattan_spearman
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+ value: 81.50968857884978
191
+ - task:
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+ type: Classification
193
+ dataset:
194
+ type: mteb/banking77
195
+ name: MTEB Banking77Classification
196
+ config: default
197
+ split: test
198
+ revision: 44fa15921b4c889113cc5df03dd4901b49161ab7
199
+ metrics:
200
+ - type: accuracy
201
+ value: 79.75000000000001
202
+ - type: f1
203
+ value: 78.92604185699534
204
+ - task:
205
+ type: Clustering
206
+ dataset:
207
+ type: mteb/biorxiv-clustering-p2p
208
+ name: MTEB BiorxivClusteringP2P
209
+ config: default
210
+ split: test
211
+ revision: 11d0121201d1f1f280e8cc8f3d98fb9c4d9f9c55
212
+ metrics:
213
+ - type: v_measure
214
+ value: 38.48301914135123
215
+ - task:
216
+ type: Clustering
217
+ dataset:
218
+ type: mteb/biorxiv-clustering-s2s
219
+ name: MTEB BiorxivClusteringS2S
220
+ config: default
221
+ split: test
222
+ revision: c0fab014e1bcb8d3a5e31b2088972a1e01547dc1
223
+ metrics:
224
+ - type: v_measure
225
+ value: 33.170209943399804
226
+ - task:
227
+ type: Retrieval
228
+ dataset:
229
+ type: BeIR/cqadupstack
230
+ name: MTEB CQADupstackAndroidRetrieval
231
+ config: default
232
+ split: test
233
+ revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
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+ metrics:
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+ - type: map_at_1
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+ value: 34.660000000000004
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+ - type: map_at_10
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+ value: 46.938
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+ - type: map_at_100
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+ - type: map_at_1000
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+ - type: ndcg_at_100
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+ - type: recall_at_1
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+ - type: recall_at_10
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+ - type: recall_at_100
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+ - type: recall_at_1000
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+ - type: recall_at_3
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+ - type: recall_at_5
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+ - task:
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+ dataset:
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+ type: BeIR/cqadupstack
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+ name: MTEB CQADupstackEnglishRetrieval
288
+ config: default
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+ split: test
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+ revision: 2b9f5791698b5be7bc5e10535c8690f20043c3db
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+ metrics:
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+ - task:
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+ dataset:
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+ type: BeIR/cqadupstack
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+ name: MTEB CQADupstackGamingRetrieval
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+ config: default
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+ split: test
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+ - type: recall_at_3
2661
+ value: 0.44
2662
+ - type: recall_at_5
2663
+ value: 0.709
2664
+ - task:
2665
+ type: Retrieval
2666
+ dataset:
2667
+ type: webis-touche2020
2668
+ name: MTEB Touche2020
2669
+ config: default
2670
+ split: test
2671
+ revision: 527b7d77e16e343303e68cb6af11d6e18b9f7b3b
2672
+ metrics:
2673
+ - type: map_at_1
2674
+ value: 1.49
2675
+ - type: map_at_10
2676
+ value: 6.39
2677
+ - type: map_at_100
2678
+ value: 11.424
2679
+ - type: map_at_1000
2680
+ value: 12.847
2681
+ - type: map_at_3
2682
+ value: 3.055
2683
+ - type: map_at_5
2684
+ value: 3.966
2685
+ - type: ndcg_at_1
2686
+ value: 17.347
2687
+ - type: ndcg_at_10
2688
+ value: 16.904
2689
+ - type: ndcg_at_100
2690
+ value: 29.187
2691
+ - type: ndcg_at_1000
2692
+ value: 40.994
2693
+ - type: ndcg_at_3
2694
+ value: 15.669
2695
+ - type: ndcg_at_5
2696
+ value: 16.034000000000002
2697
+ - type: precision_at_1
2698
+ value: 18.367
2699
+ - type: precision_at_10
2700
+ value: 16.326999999999998
2701
+ - type: precision_at_100
2702
+ value: 6.673
2703
+ - type: precision_at_1000
2704
+ value: 1.439
2705
+ - type: precision_at_3
2706
+ value: 17.687
2707
+ - type: precision_at_5
2708
+ value: 17.143
2709
+ - type: recall_at_1
2710
+ value: 1.49
2711
+ - type: recall_at_10
2712
+ value: 12.499
2713
+ - type: recall_at_100
2714
+ value: 41.711
2715
+ - type: recall_at_1000
2716
+ value: 78.286
2717
+ - type: recall_at_3
2718
+ value: 4.055000000000001
2719
+ - type: recall_at_5
2720
+ value: 6.5040000000000004
2721
+ - task:
2722
+ type: Classification
2723
+ dataset:
2724
+ type: mteb/toxic_conversations_50k
2725
+ name: MTEB ToxicConversationsClassification
2726
+ config: default
2727
+ split: test
2728
+ revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
2729
+ metrics:
2730
+ - type: accuracy
2731
+ value: 66.9918
2732
+ - type: ap
2733
+ value: 12.24755801720171
2734
+ - type: f1
2735
+ value: 51.31653313211933
2736
+ - task:
2737
+ type: Classification
2738
+ dataset:
2739
+ type: mteb/tweet_sentiment_extraction
2740
+ name: MTEB TweetSentimentExtractionClassification
2741
+ config: default
2742
+ split: test
2743
+ revision: 62146448f05be9e52a36b8ee9936447ea787eede
2744
+ metrics:
2745
+ - type: accuracy
2746
+ value: 55.410299943406905
2747
+ - type: f1
2748
+ value: 55.71547395803944
2749
+ - task:
2750
+ type: Clustering
2751
+ dataset:
2752
+ type: mteb/twentynewsgroups-clustering
2753
+ name: MTEB TwentyNewsgroupsClustering
2754
+ config: default
2755
+ split: test
2756
+ revision: 091a54f9a36281ce7d6590ec8c75dd485e7e01d4
2757
+ metrics:
2758
+ - type: v_measure
2759
+ value: 46.860271427647774
2760
+ - task:
2761
+ type: PairClassification
2762
+ dataset:
2763
+ type: mteb/twittersemeval2015-pairclassification
2764
+ name: MTEB TwitterSemEval2015
2765
+ config: default
2766
+ split: test
2767
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2768
+ metrics:
2769
+ - type: cos_sim_accuracy
2770
+ value: 84.1151576563152
2771
+ - type: cos_sim_ap
2772
+ value: 67.85802440228593
2773
+ - type: cos_sim_f1
2774
+ value: 64.08006919560113
2775
+ - type: cos_sim_precision
2776
+ value: 60.260283523123405
2777
+ - type: cos_sim_recall
2778
+ value: 68.41688654353561
2779
+ - type: dot_accuracy
2780
+ value: 84.1151576563152
2781
+ - type: dot_ap
2782
+ value: 67.85802503410727
2783
+ - type: dot_f1
2784
+ value: 64.08006919560113
2785
+ - type: dot_precision
2786
+ value: 60.260283523123405
2787
+ - type: dot_recall
2788
+ value: 68.41688654353561
2789
+ - type: euclidean_accuracy
2790
+ value: 84.1151576563152
2791
+ - type: euclidean_ap
2792
+ value: 67.85802845168082
2793
+ - type: euclidean_f1
2794
+ value: 64.08006919560113
2795
+ - type: euclidean_precision
2796
+ value: 60.260283523123405
2797
+ - type: euclidean_recall
2798
+ value: 68.41688654353561
2799
+ - type: manhattan_accuracy
2800
+ value: 83.96614412588663
2801
+ - type: manhattan_ap
2802
+ value: 67.66935451307549
2803
+ - type: manhattan_f1
2804
+ value: 63.82363570654138
2805
+ - type: manhattan_precision
2806
+ value: 58.72312125914432
2807
+ - type: manhattan_recall
2808
+ value: 69.89445910290237
2809
+ - type: max_accuracy
2810
+ value: 84.1151576563152
2811
+ - type: max_ap
2812
+ value: 67.85802845168082
2813
+ - type: max_f1
2814
+ value: 64.08006919560113
2815
+ - task:
2816
+ type: PairClassification
2817
+ dataset:
2818
+ type: mteb/twitterurlcorpus-pairclassification
2819
+ name: MTEB TwitterURLCorpus
2820
+ config: default
2821
+ split: test
2822
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2823
+ metrics:
2824
+ - type: cos_sim_accuracy
2825
+ value: 88.2504754142896
2826
+ - type: cos_sim_ap
2827
+ value: 84.70165951451109
2828
+ - type: cos_sim_f1
2829
+ value: 76.57057281916886
2830
+ - type: cos_sim_precision
2831
+ value: 74.5226643346451
2832
+ - type: cos_sim_recall
2833
+ value: 78.73421619956883
2834
+ - type: dot_accuracy
2835
+ value: 88.2504754142896
2836
+ - type: dot_ap
2837
+ value: 84.7016596919848
2838
+ - type: dot_f1
2839
+ value: 76.57057281916886
2840
+ - type: dot_precision
2841
+ value: 74.5226643346451
2842
+ - type: dot_recall
2843
+ value: 78.73421619956883
2844
+ - type: euclidean_accuracy
2845
+ value: 88.2504754142896
2846
+ - type: euclidean_ap
2847
+ value: 84.70166029488888
2848
+ - type: euclidean_f1
2849
+ value: 76.57057281916886
2850
+ - type: euclidean_precision
2851
+ value: 74.5226643346451
2852
+ - type: euclidean_recall
2853
+ value: 78.73421619956883
2854
+ - type: manhattan_accuracy
2855
+ value: 88.27376101214732
2856
+ - type: manhattan_ap
2857
+ value: 84.63518812822186
2858
+ - type: manhattan_f1
2859
+ value: 76.55138674594514
2860
+ - type: manhattan_precision
2861
+ value: 74.86934118513065
2862
+ - type: manhattan_recall
2863
+ value: 78.31074838312288
2864
+ - type: max_accuracy
2865
+ value: 88.27376101214732
2866
+ - type: max_ap
2867
+ value: 84.70166029488888
2868
+ - type: max_f1
2869
+ value: 76.57057281916886
2870
  ---
2871
 
 
2872
  # all-MiniLM-L6-v2
2873
  This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
2874
 
 
2930
 
2931
  ## Evaluation Results
2932
 
2933
+ For an automated evaluation of this model, see *MTEB*: https://huggingface.co/spaces/mteb/leaderboard or the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sentence-transformers/all-MiniLM-L12-v2)
2934
 
2935
  ------
2936