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MTEB results

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@@ -1,16 +1,2621 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  # ember-v1
2
 
3
  <p align="center">
4
  <img src="https://console.llmrails.com/assets/img/logo-black.svg" width="150px">
5
  </p>
6
 
7
- This model is trained on a large-scale corpus of relevance text pairs, covering a wide range of domains like financial, scientific, medical, legal and others. While training we used some technics from Retromae and SetFit papers.
8
 
9
- We are also providing it on our own platform as API as a service, feel free to signup: [LLMRails](https://llmrails.com/?ref=ember-v1).
10
 
11
  ### Plans
12
- - Paper will be published soon
13
- - v2 is on it's way with 4k maximum sequence length
14
 
15
  ## Usage
16
  Use with API request:
@@ -23,7 +2628,7 @@ curl --location 'https://api.llmrails.com/v1/embeddings' \
23
  "model":"embedding-english-v1" # equals to ember-v1
24
  }'
25
  ```
26
- API docs: https://docs.llmrails.com/embedding/embed-text
27
  Langchain plugin: https://python.langchain.com/docs/integrations/text_embedding/llm_rails
28
 
29
  Use with transformers:
@@ -63,7 +2668,7 @@ from sentence_transformers import SentenceTransformer
63
  from sentence_transformers.util import cos_sim
64
 
65
  sentences = [
66
- "This is an example sentence",
67
  "Each sentence is converted"
68
  ]
69
 
 
1
+ ---
2
+ tags:
3
+ - mteb
4
+ - sentence-transformers
5
+ - feature-extraction
6
+ - sentence-similarity
7
+ language: en
8
+ license: mit
9
+ model-index:
10
+ - name: ember_v1
11
+ results:
12
+ - task:
13
+ type: Classification
14
+ dataset:
15
+ type: mteb/amazon_counterfactual
16
+ name: MTEB AmazonCounterfactualClassification (en)
17
+ config: en
18
+ split: test
19
+ revision: e8379541af4e31359cca9fbcf4b00f2671dba205
20
+ metrics:
21
+ - type: accuracy
22
+ value: 76.05970149253731
23
+ - type: ap
24
+ value: 38.76045348512767
25
+ - type: f1
26
+ value: 69.8824007294685
27
+ - task:
28
+ type: Classification
29
+ dataset:
30
+ type: mteb/amazon_polarity
31
+ name: MTEB AmazonPolarityClassification
32
+ config: default
33
+ split: test
34
+ revision: e2d317d38cd51312af73b3d32a06d1a08b442046
35
+ metrics:
36
+ - type: accuracy
37
+ value: 91.977
38
+ - type: ap
39
+ value: 88.63507587170176
40
+ - type: f1
41
+ value: 91.9524133311038
42
+ - task:
43
+ type: Classification
44
+ dataset:
45
+ type: mteb/amazon_reviews_multi
46
+ name: MTEB AmazonReviewsClassification (en)
47
+ config: en
48
+ split: test
49
+ revision: 1399c76144fd37290681b995c656ef9b2e06e26d
50
+ metrics:
51
+ - type: accuracy
52
+ value: 47.938
53
+ - type: f1
54
+ value: 47.58273047536129
55
+ - task:
56
+ type: Retrieval
57
+ dataset:
58
+ type: arguana
59
+ name: MTEB ArguAna
60
+ config: default
61
+ split: test
62
+ revision: None
63
+ metrics:
64
+ - type: map_at_1
65
+ value: 41.252
66
+ - type: map_at_10
67
+ value: 56.567
68
+ - type: map_at_100
69
+ value: 57.07600000000001
70
+ - type: map_at_1000
71
+ value: 57.08
72
+ - type: map_at_3
73
+ value: 52.394
74
+ - type: map_at_5
75
+ value: 55.055
76
+ - type: mrr_at_1
77
+ value: 42.39
78
+ - type: mrr_at_10
79
+ value: 57.001999999999995
80
+ - type: mrr_at_100
81
+ value: 57.531
82
+ - type: mrr_at_1000
83
+ value: 57.535000000000004
84
+ - type: mrr_at_3
85
+ value: 52.845
86
+ - type: mrr_at_5
87
+ value: 55.47299999999999
88
+ - type: ndcg_at_1
89
+ value: 41.252
90
+ - type: ndcg_at_10
91
+ value: 64.563
92
+ - type: ndcg_at_100
93
+ value: 66.667
94
+ - type: ndcg_at_1000
95
+ value: 66.77
96
+ - type: ndcg_at_3
97
+ value: 56.120000000000005
98
+ - type: ndcg_at_5
99
+ value: 60.889
100
+ - type: precision_at_1
101
+ value: 41.252
102
+ - type: precision_at_10
103
+ value: 8.982999999999999
104
+ - type: precision_at_100
105
+ value: 0.989
106
+ - type: precision_at_1000
107
+ value: 0.1
108
+ - type: precision_at_3
109
+ value: 22.309
110
+ - type: precision_at_5
111
+ value: 15.690000000000001
112
+ - type: recall_at_1
113
+ value: 41.252
114
+ - type: recall_at_10
115
+ value: 89.82900000000001
116
+ - type: recall_at_100
117
+ value: 98.86200000000001
118
+ - type: recall_at_1000
119
+ value: 99.644
120
+ - type: recall_at_3
121
+ value: 66.927
122
+ - type: recall_at_5
123
+ value: 78.45
124
+ - task:
125
+ type: Clustering
126
+ dataset:
127
+ type: mteb/arxiv-clustering-p2p
128
+ name: MTEB ArxivClusteringP2P
129
+ config: default
130
+ split: test
131
+ revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
132
+ metrics:
133
+ - type: v_measure
134
+ value: 48.5799968717232
135
+ - task:
136
+ type: Clustering
137
+ dataset:
138
+ type: mteb/arxiv-clustering-s2s
139
+ name: MTEB ArxivClusteringS2S
140
+ config: default
141
+ split: test
142
+ revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
143
+ metrics:
144
+ - type: v_measure
145
+ value: 43.142844164856136
146
+ - task:
147
+ type: Reranking
148
+ dataset:
149
+ type: mteb/askubuntudupquestions-reranking
150
+ name: MTEB AskUbuntuDupQuestions
151
+ config: default
152
+ split: test
153
+ revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
154
+ metrics:
155
+ - type: map
156
+ value: 64.45997990276463
157
+ - type: mrr
158
+ value: 77.85560392208592
159
+ - task:
160
+ type: STS
161
+ dataset:
162
+ type: mteb/biosses-sts
163
+ name: MTEB BIOSSES
164
+ config: default
165
+ split: test
166
+ revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
167
+ metrics:
168
+ - type: cos_sim_pearson
169
+ value: 86.38299310075898
170
+ - type: cos_sim_spearman
171
+ value: 85.81038898286454
172
+ - type: euclidean_pearson
173
+ value: 84.28002556389774
174
+ - type: euclidean_spearman
175
+ value: 85.80315990248238
176
+ - type: manhattan_pearson
177
+ value: 83.9755390675032
178
+ - type: manhattan_spearman
179
+ value: 85.30435335611396
180
+ - task:
181
+ type: Classification
182
+ dataset:
183
+ type: mteb/banking77
184
+ name: MTEB Banking77Classification
185
+ config: default
186
+ split: test
187
+ revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
188
+ metrics:
189
+ - type: accuracy
190
+ value: 87.89935064935065
191
+ - type: f1
192
+ value: 87.87886687103833
193
+ - task:
194
+ type: Clustering
195
+ dataset:
196
+ type: mteb/biorxiv-clustering-p2p
197
+ name: MTEB BiorxivClusteringP2P
198
+ config: default
199
+ split: test
200
+ revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
201
+ metrics:
202
+ - type: v_measure
203
+ value: 38.84335510371379
204
+ - task:
205
+ type: Clustering
206
+ dataset:
207
+ type: mteb/biorxiv-clustering-s2s
208
+ name: MTEB BiorxivClusteringS2S
209
+ config: default
210
+ split: test
211
+ revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
212
+ metrics:
213
+ - type: v_measure
214
+ value: 36.377963093857005
215
+ - task:
216
+ type: Retrieval
217
+ dataset:
218
+ type: BeIR/cqadupstack
219
+ name: MTEB CQADupstackAndroidRetrieval
220
+ config: default
221
+ split: test
222
+ revision: None
223
+ metrics:
224
+ - type: map_at_1
225
+ value: 32.557
226
+ - type: map_at_10
227
+ value: 44.501000000000005
228
+ - type: map_at_100
229
+ value: 46.11
230
+ - type: map_at_1000
231
+ value: 46.232
232
+ - type: map_at_3
233
+ value: 40.711000000000006
234
+ - type: map_at_5
235
+ value: 42.937
236
+ - type: mrr_at_1
237
+ value: 40.916000000000004
238
+ - type: mrr_at_10
239
+ value: 51.317
240
+ - type: mrr_at_100
241
+ value: 52.003
242
+ - type: mrr_at_1000
243
+ value: 52.044999999999995
244
+ - type: mrr_at_3
245
+ value: 48.569
246
+ - type: mrr_at_5
247
+ value: 50.322
248
+ - type: ndcg_at_1
249
+ value: 40.916000000000004
250
+ - type: ndcg_at_10
251
+ value: 51.353
252
+ - type: ndcg_at_100
253
+ value: 56.762
254
+ - type: ndcg_at_1000
255
+ value: 58.555
256
+ - type: ndcg_at_3
257
+ value: 46.064
258
+ - type: ndcg_at_5
259
+ value: 48.677
260
+ - type: precision_at_1
261
+ value: 40.916000000000004
262
+ - type: precision_at_10
263
+ value: 9.927999999999999
264
+ - type: precision_at_100
265
+ value: 1.592
266
+ - type: precision_at_1000
267
+ value: 0.20600000000000002
268
+ - type: precision_at_3
269
+ value: 22.078999999999997
270
+ - type: precision_at_5
271
+ value: 16.08
272
+ - type: recall_at_1
273
+ value: 32.557
274
+ - type: recall_at_10
275
+ value: 63.942
276
+ - type: recall_at_100
277
+ value: 86.436
278
+ - type: recall_at_1000
279
+ value: 97.547
280
+ - type: recall_at_3
281
+ value: 48.367
282
+ - type: recall_at_5
283
+ value: 55.818
284
+ - task:
285
+ type: Retrieval
286
+ dataset:
287
+ type: BeIR/cqadupstack
288
+ name: MTEB CQADupstackEnglishRetrieval
289
+ config: default
290
+ split: test
291
+ revision: None
292
+ metrics:
293
+ - type: map_at_1
294
+ value: 32.106
295
+ - type: map_at_10
296
+ value: 42.55
297
+ - type: map_at_100
298
+ value: 43.818
299
+ - type: map_at_1000
300
+ value: 43.952999999999996
301
+ - type: map_at_3
302
+ value: 39.421
303
+ - type: map_at_5
304
+ value: 41.276
305
+ - type: mrr_at_1
306
+ value: 39.936
307
+ - type: mrr_at_10
308
+ value: 48.484
309
+ - type: mrr_at_100
310
+ value: 49.123
311
+ - type: mrr_at_1000
312
+ value: 49.163000000000004
313
+ - type: mrr_at_3
314
+ value: 46.221000000000004
315
+ - type: mrr_at_5
316
+ value: 47.603
317
+ - type: ndcg_at_1
318
+ value: 39.936
319
+ - type: ndcg_at_10
320
+ value: 48.25
321
+ - type: ndcg_at_100
322
+ value: 52.674
323
+ - type: ndcg_at_1000
324
+ value: 54.638
325
+ - type: ndcg_at_3
326
+ value: 44.05
327
+ - type: ndcg_at_5
328
+ value: 46.125
329
+ - type: precision_at_1
330
+ value: 39.936
331
+ - type: precision_at_10
332
+ value: 9.096
333
+ - type: precision_at_100
334
+ value: 1.473
335
+ - type: precision_at_1000
336
+ value: 0.19499999999999998
337
+ - type: precision_at_3
338
+ value: 21.295
339
+ - type: precision_at_5
340
+ value: 15.121
341
+ - type: recall_at_1
342
+ value: 32.106
343
+ - type: recall_at_10
344
+ value: 58.107
345
+ - type: recall_at_100
346
+ value: 76.873
347
+ - type: recall_at_1000
348
+ value: 89.079
349
+ - type: recall_at_3
350
+ value: 45.505
351
+ - type: recall_at_5
352
+ value: 51.479
353
+ - task:
354
+ type: Retrieval
355
+ dataset:
356
+ type: BeIR/cqadupstack
357
+ name: MTEB CQADupstackGamingRetrieval
358
+ config: default
359
+ split: test
360
+ revision: None
361
+ metrics:
362
+ - type: map_at_1
363
+ value: 41.513
364
+ - type: map_at_10
365
+ value: 54.571999999999996
366
+ - type: map_at_100
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368
+ - type: map_at_1000
369
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370
+ - type: map_at_3
371
+ value: 51.127
372
+ - type: map_at_5
373
+ value: 53.151
374
+ - type: mrr_at_1
375
+ value: 47.398
376
+ - type: mrr_at_10
377
+ value: 57.82000000000001
378
+ - type: mrr_at_100
379
+ value: 58.457
380
+ - type: mrr_at_1000
381
+ value: 58.479000000000006
382
+ - type: mrr_at_3
383
+ value: 55.32899999999999
384
+ - type: mrr_at_5
385
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+ value: 62.666999999999994
2176
+ - type: ndcg_at_10
2177
+ value: 73.425
2178
+ - type: ndcg_at_100
2179
+ value: 75.955
2180
+ - type: ndcg_at_1000
2181
+ value: 76.459
2182
+ - type: ndcg_at_3
2183
+ value: 68.345
2184
+ - type: ndcg_at_5
2185
+ value: 71.319
2186
+ - type: precision_at_1
2187
+ value: 62.666999999999994
2188
+ - type: precision_at_10
2189
+ value: 9.667
2190
+ - type: precision_at_100
2191
+ value: 1.09
2192
+ - type: precision_at_1000
2193
+ value: 0.11299999999999999
2194
+ - type: precision_at_3
2195
+ value: 26.333000000000002
2196
+ - type: precision_at_5
2197
+ value: 17.732999999999997
2198
+ - type: recall_at_1
2199
+ value: 59.660999999999994
2200
+ - type: recall_at_10
2201
+ value: 85.422
2202
+ - type: recall_at_100
2203
+ value: 96.167
2204
+ - type: recall_at_1000
2205
+ value: 100.0
2206
+ - type: recall_at_3
2207
+ value: 72.044
2208
+ - type: recall_at_5
2209
+ value: 79.428
2210
+ - task:
2211
+ type: PairClassification
2212
+ dataset:
2213
+ type: mteb/sprintduplicatequestions-pairclassification
2214
+ name: MTEB SprintDuplicateQuestions
2215
+ config: default
2216
+ split: test
2217
+ revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
2218
+ metrics:
2219
+ - type: cos_sim_accuracy
2220
+ value: 99.86435643564356
2221
+ - type: cos_sim_ap
2222
+ value: 96.83057412333741
2223
+ - type: cos_sim_f1
2224
+ value: 93.04215337734891
2225
+ - type: cos_sim_precision
2226
+ value: 94.53044375644994
2227
+ - type: cos_sim_recall
2228
+ value: 91.60000000000001
2229
+ - type: dot_accuracy
2230
+ value: 99.7910891089109
2231
+ - type: dot_ap
2232
+ value: 94.10681982106397
2233
+ - type: dot_f1
2234
+ value: 89.34881373043918
2235
+ - type: dot_precision
2236
+ value: 90.21406727828746
2237
+ - type: dot_recall
2238
+ value: 88.5
2239
+ - type: euclidean_accuracy
2240
+ value: 99.85544554455446
2241
+ - type: euclidean_ap
2242
+ value: 96.78545104478602
2243
+ - type: euclidean_f1
2244
+ value: 92.65143992055613
2245
+ - type: euclidean_precision
2246
+ value: 92.01183431952663
2247
+ - type: euclidean_recall
2248
+ value: 93.30000000000001
2249
+ - type: manhattan_accuracy
2250
+ value: 99.85841584158416
2251
+ - type: manhattan_ap
2252
+ value: 96.80748903307823
2253
+ - type: manhattan_f1
2254
+ value: 92.78247884519662
2255
+ - type: manhattan_precision
2256
+ value: 92.36868186323092
2257
+ - type: manhattan_recall
2258
+ value: 93.2
2259
+ - type: max_accuracy
2260
+ value: 99.86435643564356
2261
+ - type: max_ap
2262
+ value: 96.83057412333741
2263
+ - type: max_f1
2264
+ value: 93.04215337734891
2265
+ - task:
2266
+ type: Clustering
2267
+ dataset:
2268
+ type: mteb/stackexchange-clustering
2269
+ name: MTEB StackExchangeClustering
2270
+ config: default
2271
+ split: test
2272
+ revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
2273
+ metrics:
2274
+ - type: v_measure
2275
+ value: 65.53971025855282
2276
+ - task:
2277
+ type: Clustering
2278
+ dataset:
2279
+ type: mteb/stackexchange-clustering-p2p
2280
+ name: MTEB StackExchangeClusteringP2P
2281
+ config: default
2282
+ split: test
2283
+ revision: 815ca46b2622cec33ccafc3735d572c266efdb44
2284
+ metrics:
2285
+ - type: v_measure
2286
+ value: 33.97791591490788
2287
+ - task:
2288
+ type: Reranking
2289
+ dataset:
2290
+ type: mteb/stackoverflowdupquestions-reranking
2291
+ name: MTEB StackOverflowDupQuestions
2292
+ config: default
2293
+ split: test
2294
+ revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
2295
+ metrics:
2296
+ - type: map
2297
+ value: 55.852215301355066
2298
+ - type: mrr
2299
+ value: 56.85527809608691
2300
+ - task:
2301
+ type: Summarization
2302
+ dataset:
2303
+ type: mteb/summeval
2304
+ name: MTEB SummEval
2305
+ config: default
2306
+ split: test
2307
+ revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
2308
+ metrics:
2309
+ - type: cos_sim_pearson
2310
+ value: 31.21442519856758
2311
+ - type: cos_sim_spearman
2312
+ value: 30.822536216936825
2313
+ - type: dot_pearson
2314
+ value: 28.661325528121807
2315
+ - type: dot_spearman
2316
+ value: 28.1435226478879
2317
+ - task:
2318
+ type: Retrieval
2319
+ dataset:
2320
+ type: trec-covid
2321
+ name: MTEB TRECCOVID
2322
+ config: default
2323
+ split: test
2324
+ revision: None
2325
+ metrics:
2326
+ - type: map_at_1
2327
+ value: 0.183
2328
+ - type: map_at_10
2329
+ value: 1.526
2330
+ - type: map_at_100
2331
+ value: 7.915
2332
+ - type: map_at_1000
2333
+ value: 19.009
2334
+ - type: map_at_3
2335
+ value: 0.541
2336
+ - type: map_at_5
2337
+ value: 0.8659999999999999
2338
+ - type: mrr_at_1
2339
+ value: 68.0
2340
+ - type: mrr_at_10
2341
+ value: 81.186
2342
+ - type: mrr_at_100
2343
+ value: 81.186
2344
+ - type: mrr_at_1000
2345
+ value: 81.186
2346
+ - type: mrr_at_3
2347
+ value: 80.0
2348
+ - type: mrr_at_5
2349
+ value: 80.9
2350
+ - type: ndcg_at_1
2351
+ value: 64.0
2352
+ - type: ndcg_at_10
2353
+ value: 64.13799999999999
2354
+ - type: ndcg_at_100
2355
+ value: 47.632000000000005
2356
+ - type: ndcg_at_1000
2357
+ value: 43.037
2358
+ - type: ndcg_at_3
2359
+ value: 67.542
2360
+ - type: ndcg_at_5
2361
+ value: 67.496
2362
+ - type: precision_at_1
2363
+ value: 68.0
2364
+ - type: precision_at_10
2365
+ value: 67.80000000000001
2366
+ - type: precision_at_100
2367
+ value: 48.980000000000004
2368
+ - type: precision_at_1000
2369
+ value: 19.036
2370
+ - type: precision_at_3
2371
+ value: 72.0
2372
+ - type: precision_at_5
2373
+ value: 71.2
2374
+ - type: recall_at_1
2375
+ value: 0.183
2376
+ - type: recall_at_10
2377
+ value: 1.799
2378
+ - type: recall_at_100
2379
+ value: 11.652999999999999
2380
+ - type: recall_at_1000
2381
+ value: 40.086
2382
+ - type: recall_at_3
2383
+ value: 0.5930000000000001
2384
+ - type: recall_at_5
2385
+ value: 0.983
2386
+ - task:
2387
+ type: Retrieval
2388
+ dataset:
2389
+ type: webis-touche2020
2390
+ name: MTEB Touche2020
2391
+ config: default
2392
+ split: test
2393
+ revision: None
2394
+ metrics:
2395
+ - type: map_at_1
2396
+ value: 2.29
2397
+ - type: map_at_10
2398
+ value: 9.489
2399
+ - type: map_at_100
2400
+ value: 15.051
2401
+ - type: map_at_1000
2402
+ value: 16.561999999999998
2403
+ - type: map_at_3
2404
+ value: 5.137
2405
+ - type: map_at_5
2406
+ value: 6.7989999999999995
2407
+ - type: mrr_at_1
2408
+ value: 28.571
2409
+ - type: mrr_at_10
2410
+ value: 45.699
2411
+ - type: mrr_at_100
2412
+ value: 46.461000000000006
2413
+ - type: mrr_at_1000
2414
+ value: 46.461000000000006
2415
+ - type: mrr_at_3
2416
+ value: 41.837
2417
+ - type: mrr_at_5
2418
+ value: 43.163000000000004
2419
+ - type: ndcg_at_1
2420
+ value: 23.469
2421
+ - type: ndcg_at_10
2422
+ value: 23.544999999999998
2423
+ - type: ndcg_at_100
2424
+ value: 34.572
2425
+ - type: ndcg_at_1000
2426
+ value: 46.035
2427
+ - type: ndcg_at_3
2428
+ value: 27.200000000000003
2429
+ - type: ndcg_at_5
2430
+ value: 25.266
2431
+ - type: precision_at_1
2432
+ value: 28.571
2433
+ - type: precision_at_10
2434
+ value: 22.041
2435
+ - type: precision_at_100
2436
+ value: 7.3469999999999995
2437
+ - type: precision_at_1000
2438
+ value: 1.484
2439
+ - type: precision_at_3
2440
+ value: 29.932
2441
+ - type: precision_at_5
2442
+ value: 26.531
2443
+ - type: recall_at_1
2444
+ value: 2.29
2445
+ - type: recall_at_10
2446
+ value: 15.895999999999999
2447
+ - type: recall_at_100
2448
+ value: 45.518
2449
+ - type: recall_at_1000
2450
+ value: 80.731
2451
+ - type: recall_at_3
2452
+ value: 6.433
2453
+ - type: recall_at_5
2454
+ value: 9.484
2455
+ - task:
2456
+ type: Classification
2457
+ dataset:
2458
+ type: mteb/toxic_conversations_50k
2459
+ name: MTEB ToxicConversationsClassification
2460
+ config: default
2461
+ split: test
2462
+ revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
2463
+ metrics:
2464
+ - type: accuracy
2465
+ value: 71.4178
2466
+ - type: ap
2467
+ value: 14.575240629602373
2468
+ - type: f1
2469
+ value: 55.02449563229096
2470
+ - task:
2471
+ type: Classification
2472
+ dataset:
2473
+ type: mteb/tweet_sentiment_extraction
2474
+ name: MTEB TweetSentimentExtractionClassification
2475
+ config: default
2476
+ split: test
2477
+ revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
2478
+ metrics:
2479
+ - type: accuracy
2480
+ value: 60.00282965478212
2481
+ - type: f1
2482
+ value: 60.34413028768773
2483
+ - task:
2484
+ type: Clustering
2485
+ dataset:
2486
+ type: mteb/twentynewsgroups-clustering
2487
+ name: MTEB TwentyNewsgroupsClustering
2488
+ config: default
2489
+ split: test
2490
+ revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
2491
+ metrics:
2492
+ - type: v_measure
2493
+ value: 50.409448342549936
2494
+ - task:
2495
+ type: PairClassification
2496
+ dataset:
2497
+ type: mteb/twittersemeval2015-pairclassification
2498
+ name: MTEB TwitterSemEval2015
2499
+ config: default
2500
+ split: test
2501
+ revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
2502
+ metrics:
2503
+ - type: cos_sim_accuracy
2504
+ value: 87.62591643321214
2505
+ - type: cos_sim_ap
2506
+ value: 79.28766491329633
2507
+ - type: cos_sim_f1
2508
+ value: 71.98772064466617
2509
+ - type: cos_sim_precision
2510
+ value: 69.8609731876862
2511
+ - type: cos_sim_recall
2512
+ value: 74.24802110817942
2513
+ - type: dot_accuracy
2514
+ value: 84.75293556654945
2515
+ - type: dot_ap
2516
+ value: 69.72705761174353
2517
+ - type: dot_f1
2518
+ value: 65.08692852543464
2519
+ - type: dot_precision
2520
+ value: 63.57232704402516
2521
+ - type: dot_recall
2522
+ value: 66.6754617414248
2523
+ - type: euclidean_accuracy
2524
+ value: 87.44710019669786
2525
+ - type: euclidean_ap
2526
+ value: 79.11021477292638
2527
+ - type: euclidean_f1
2528
+ value: 71.5052389470994
2529
+ - type: euclidean_precision
2530
+ value: 69.32606541129832
2531
+ - type: euclidean_recall
2532
+ value: 73.82585751978891
2533
+ - type: manhattan_accuracy
2534
+ value: 87.42325803182929
2535
+ - type: manhattan_ap
2536
+ value: 79.05094494327616
2537
+ - type: manhattan_f1
2538
+ value: 71.36333985649055
2539
+ - type: manhattan_precision
2540
+ value: 70.58064516129032
2541
+ - type: manhattan_recall
2542
+ value: 72.16358839050132
2543
+ - type: max_accuracy
2544
+ value: 87.62591643321214
2545
+ - type: max_ap
2546
+ value: 79.28766491329633
2547
+ - type: max_f1
2548
+ value: 71.98772064466617
2549
+ - task:
2550
+ type: PairClassification
2551
+ dataset:
2552
+ type: mteb/twitterurlcorpus-pairclassification
2553
+ name: MTEB TwitterURLCorpus
2554
+ config: default
2555
+ split: test
2556
+ revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
2557
+ metrics:
2558
+ - type: cos_sim_accuracy
2559
+ value: 88.85202002561415
2560
+ - type: cos_sim_ap
2561
+ value: 85.9835303311168
2562
+ - type: cos_sim_f1
2563
+ value: 78.25741142443962
2564
+ - type: cos_sim_precision
2565
+ value: 73.76635768811342
2566
+ - type: cos_sim_recall
2567
+ value: 83.3307668617185
2568
+ - type: dot_accuracy
2569
+ value: 88.20584468506229
2570
+ - type: dot_ap
2571
+ value: 83.591632302697
2572
+ - type: dot_f1
2573
+ value: 76.81739705396173
2574
+ - type: dot_precision
2575
+ value: 73.45275728837373
2576
+ - type: dot_recall
2577
+ value: 80.50508161379734
2578
+ - type: euclidean_accuracy
2579
+ value: 88.64633057787093
2580
+ - type: euclidean_ap
2581
+ value: 85.25705123182283
2582
+ - type: euclidean_f1
2583
+ value: 77.18535726329199
2584
+ - type: euclidean_precision
2585
+ value: 75.17699437997226
2586
+ - type: euclidean_recall
2587
+ value: 79.30397289805975
2588
+ - type: manhattan_accuracy
2589
+ value: 88.63274731245392
2590
+ - type: manhattan_ap
2591
+ value: 85.2376825633018
2592
+ - type: manhattan_f1
2593
+ value: 77.15810785937788
2594
+ - type: manhattan_precision
2595
+ value: 73.92255061014319
2596
+ - type: manhattan_recall
2597
+ value: 80.68986757006468
2598
+ - type: max_accuracy
2599
+ value: 88.85202002561415
2600
+ - type: max_ap
2601
+ value: 85.9835303311168
2602
+ - type: max_f1
2603
+ value: 78.25741142443962
2604
+ ---
2605
+
2606
  # ember-v1
2607
 
2608
  <p align="center">
2609
  <img src="https://console.llmrails.com/assets/img/logo-black.svg" width="150px">
2610
  </p>
2611
 
2612
+ This model has been trained on an extensive corpus of text pairs that encompass a broad spectrum of domains, including finance, science, medicine, law, and various others. During the training process, we incorporated techniques derived from the Retromae and SetFit research papers.
2613
 
2614
+ We are pleased to offer this model as an API service through our platform, [LLMRails](https://llmrails.com/?ref=ember-v1). If you are interested, please don't hesitate to sign up.
2615
 
2616
  ### Plans
2617
+ - The research paper will be published soon.
2618
+ - The v2 of the model is currently in development and will feature an extended maximum sequence length of 4,000 tokens.
2619
 
2620
  ## Usage
2621
  Use with API request:
 
2628
  "model":"embedding-english-v1" # equals to ember-v1
2629
  }'
2630
  ```
2631
+ API docs: https://docs.llmrails.com/embedding/embed-text<br>
2632
  Langchain plugin: https://python.langchain.com/docs/integrations/text_embedding/llm_rails
2633
 
2634
  Use with transformers:
 
2668
  from sentence_transformers.util import cos_sim
2669
 
2670
  sentences = [
2671
+ "This is an example sentence",
2672
  "Each sentence is converted"
2673
  ]
2674