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README.md
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| 2 |
license: mit
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| 3 |
---
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This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model for embeddings created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export and Neural Magic's [Sparsify](https://account.neuralmagic.com/signin?client_id=d04a5f0c-983d-11ed-88a6-971073f187d3&return_to=https%3A//accounts.neuralmagic.com/v1/connect/authorize%3Fscope%3Dsparsify%3Aread%2Bsparsify%3Awrite%2Buser%3Aapi-key%3Aread%2Buser%3Aprofile%3Awrite%2Buser%3Aprofile%3Aread%26response_type%3Dcode%26code_challenge_method%3DS256%26redirect_uri%3Dhttps%3A//apps.neuralmagic.com/sparsify/oidc/callback.html%26state%3Da9b466a6193c4a7b92cba469408d2495%26client_id%3Dd04a5f0c-983d-11ed-88a6-971073f187d3%26code_challenge%3DP0EkmKBpplTb7crJOGS8YLSwT8UH-BeuD0wuE4JTORQ%26response_mode%3Dquery) for One-Shot quantization and unstructured pruning (50%).
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| 1 |
---
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| 2 |
+
tags:
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| 3 |
+
- mteb
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+
model-index:
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- name: bge-small-en-v1.5-sparse
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+
results:
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| 7 |
+
- task:
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| 8 |
+
type: Classification
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+
dataset:
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| 10 |
+
type: mteb/amazon_counterfactual
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| 11 |
+
name: MTEB AmazonCounterfactualClassification (en)
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| 12 |
+
config: en
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| 13 |
+
split: test
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| 14 |
+
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
|
| 15 |
+
metrics:
|
| 16 |
+
- type: accuracy
|
| 17 |
+
value: 70.71641791044776
|
| 18 |
+
- type: ap
|
| 19 |
+
value: 32.850850647310004
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| 20 |
+
- type: f1
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| 21 |
+
value: 64.48101916414805
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| 22 |
+
- task:
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| 23 |
+
type: Classification
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| 24 |
+
dataset:
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| 25 |
+
type: mteb/amazon_polarity
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| 26 |
+
name: MTEB AmazonPolarityClassification
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| 27 |
+
config: default
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| 28 |
+
split: test
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| 29 |
+
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
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| 30 |
+
metrics:
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| 31 |
+
- type: accuracy
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| 32 |
+
value: 83.33962500000001
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| 33 |
+
- type: ap
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| 34 |
+
value: 78.28706349240106
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+
- type: f1
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| 36 |
+
value: 83.27426715603062
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| 37 |
+
- task:
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| 38 |
+
type: Classification
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| 39 |
+
dataset:
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| 40 |
+
type: mteb/amazon_reviews_multi
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| 41 |
+
name: MTEB AmazonReviewsClassification (en)
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| 42 |
+
config: en
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| 43 |
+
split: test
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| 44 |
+
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
|
| 45 |
+
metrics:
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| 46 |
+
- type: accuracy
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| 47 |
+
value: 40.988
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| 48 |
+
- type: f1
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| 49 |
+
value: 40.776679545648506
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| 50 |
+
- task:
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| 51 |
+
type: STS
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| 52 |
+
dataset:
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| 53 |
+
type: mteb/biosses-sts
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| 54 |
+
name: MTEB BIOSSES
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| 55 |
+
config: default
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| 56 |
+
split: test
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| 57 |
+
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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| 58 |
+
metrics:
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| 59 |
+
- type: cos_sim_pearson
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| 60 |
+
value: 79.64892774481326
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| 61 |
+
- type: cos_sim_spearman
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| 62 |
+
value: 78.953003817029
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| 63 |
+
- type: euclidean_pearson
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| 64 |
+
value: 78.92456838230683
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| 65 |
+
- type: euclidean_spearman
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| 66 |
+
value: 78.56504316985354
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| 67 |
+
- type: manhattan_pearson
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| 68 |
+
value: 79.21436359014227
|
| 69 |
+
- type: manhattan_spearman
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| 70 |
+
value: 78.66263575501259
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| 71 |
+
- task:
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| 72 |
+
type: Classification
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| 73 |
+
dataset:
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| 74 |
+
type: mteb/banking77
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| 75 |
+
name: MTEB Banking77Classification
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| 76 |
+
config: default
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| 77 |
+
split: test
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| 78 |
+
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
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| 79 |
+
metrics:
|
| 80 |
+
- type: accuracy
|
| 81 |
+
value: 81.25
|
| 82 |
+
- type: f1
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| 83 |
+
value: 81.20841448916138
|
| 84 |
+
- task:
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| 85 |
+
type: Classification
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| 86 |
+
dataset:
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| 87 |
+
type: mteb/emotion
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| 88 |
+
name: MTEB EmotionClassification
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| 89 |
+
config: default
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| 90 |
+
split: test
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| 91 |
+
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
|
| 92 |
+
metrics:
|
| 93 |
+
- type: accuracy
|
| 94 |
+
value: 41.665
|
| 95 |
+
- type: f1
|
| 96 |
+
value: 37.601137843331244
|
| 97 |
+
- task:
|
| 98 |
+
type: Classification
|
| 99 |
+
dataset:
|
| 100 |
+
type: mteb/imdb
|
| 101 |
+
name: MTEB ImdbClassification
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| 102 |
+
config: default
|
| 103 |
+
split: test
|
| 104 |
+
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
|
| 105 |
+
metrics:
|
| 106 |
+
- type: accuracy
|
| 107 |
+
value: 74.8052
|
| 108 |
+
- type: ap
|
| 109 |
+
value: 68.92588517572685
|
| 110 |
+
- type: f1
|
| 111 |
+
value: 74.66801685854456
|
| 112 |
+
- task:
|
| 113 |
+
type: Classification
|
| 114 |
+
dataset:
|
| 115 |
+
type: mteb/mtop_domain
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| 116 |
+
name: MTEB MTOPDomainClassification (en)
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| 117 |
+
config: en
|
| 118 |
+
split: test
|
| 119 |
+
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
|
| 120 |
+
metrics:
|
| 121 |
+
- type: accuracy
|
| 122 |
+
value: 91.2220702234382
|
| 123 |
+
- type: f1
|
| 124 |
+
value: 90.81687856852439
|
| 125 |
+
- task:
|
| 126 |
+
type: Classification
|
| 127 |
+
dataset:
|
| 128 |
+
type: mteb/mtop_intent
|
| 129 |
+
name: MTEB MTOPIntentClassification (en)
|
| 130 |
+
config: en
|
| 131 |
+
split: test
|
| 132 |
+
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
|
| 133 |
+
metrics:
|
| 134 |
+
- type: accuracy
|
| 135 |
+
value: 69.39124487004105
|
| 136 |
+
- type: f1
|
| 137 |
+
value: 51.8350043424968
|
| 138 |
+
- task:
|
| 139 |
+
type: Classification
|
| 140 |
+
dataset:
|
| 141 |
+
type: mteb/amazon_massive_intent
|
| 142 |
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name: MTEB MassiveIntentClassification (en)
|
| 143 |
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config: en
|
| 144 |
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split: test
|
| 145 |
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revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
|
| 146 |
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metrics:
|
| 147 |
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- type: accuracy
|
| 148 |
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value: 69.80497646267652
|
| 149 |
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- type: f1
|
| 150 |
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value: 67.34213899244814
|
| 151 |
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- task:
|
| 152 |
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type: Classification
|
| 153 |
+
dataset:
|
| 154 |
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type: mteb/amazon_massive_scenario
|
| 155 |
+
name: MTEB MassiveScenarioClassification (en)
|
| 156 |
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config: en
|
| 157 |
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split: test
|
| 158 |
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revision: 7d571f92784cd94a019292a1f45445077d0ef634
|
| 159 |
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metrics:
|
| 160 |
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- type: accuracy
|
| 161 |
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value: 74.54270342972428
|
| 162 |
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- type: f1
|
| 163 |
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value: 74.02802500235784
|
| 164 |
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- task:
|
| 165 |
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type: STS
|
| 166 |
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dataset:
|
| 167 |
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type: mteb/sickr-sts
|
| 168 |
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name: MTEB SICK-R
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| 169 |
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config: default
|
| 170 |
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split: test
|
| 171 |
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revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
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| 172 |
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metrics:
|
| 173 |
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- type: cos_sim_pearson
|
| 174 |
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value: 80.89215288990194
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| 175 |
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- type: cos_sim_spearman
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| 176 |
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value: 74.386413188675
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| 177 |
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- type: euclidean_pearson
|
| 178 |
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value: 78.83679563989534
|
| 179 |
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- type: euclidean_spearman
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| 180 |
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value: 74.29328198771996
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| 181 |
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- type: manhattan_pearson
|
| 182 |
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value: 78.77968796707641
|
| 183 |
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- type: manhattan_spearman
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| 184 |
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value: 74.20887429784696
|
| 185 |
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- task:
|
| 186 |
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type: STS
|
| 187 |
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dataset:
|
| 188 |
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type: mteb/sts12-sts
|
| 189 |
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name: MTEB STS12
|
| 190 |
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config: default
|
| 191 |
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split: test
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| 192 |
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revision: a0d554a64d88156834ff5ae9920b964011b16384
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| 193 |
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metrics:
|
| 194 |
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- type: cos_sim_pearson
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| 195 |
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value: 78.31858821914498
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- type: cos_sim_spearman
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value: 72.2217008523832
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- type: euclidean_pearson
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value: 75.38901061978429
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| 200 |
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- type: euclidean_spearman
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| 201 |
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value: 71.81255767675184
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- type: manhattan_pearson
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value: 75.49472202181288
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| 204 |
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- type: manhattan_spearman
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value: 71.96322588726144
|
| 206 |
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- task:
|
| 207 |
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type: STS
|
| 208 |
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dataset:
|
| 209 |
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type: mteb/sts13-sts
|
| 210 |
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name: MTEB STS13
|
| 211 |
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config: default
|
| 212 |
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split: test
|
| 213 |
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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| 214 |
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metrics:
|
| 215 |
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- type: cos_sim_pearson
|
| 216 |
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value: 79.48334648997455
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- type: cos_sim_spearman
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value: 80.99654029572798
|
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- type: euclidean_pearson
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value: 80.46546523970035
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- type: euclidean_spearman
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value: 80.90646216980744
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- type: manhattan_pearson
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value: 80.35474057857608
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| 225 |
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- type: manhattan_spearman
|
| 226 |
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value: 80.8141299909659
|
| 227 |
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- task:
|
| 228 |
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type: STS
|
| 229 |
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dataset:
|
| 230 |
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type: mteb/sts14-sts
|
| 231 |
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name: MTEB STS14
|
| 232 |
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config: default
|
| 233 |
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split: test
|
| 234 |
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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| 235 |
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metrics:
|
| 236 |
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- type: cos_sim_pearson
|
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value: 79.73826970784727
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| 238 |
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- type: cos_sim_spearman
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value: 76.9926870133034
|
| 240 |
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- type: euclidean_pearson
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| 241 |
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value: 79.6386542120984
|
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- type: euclidean_spearman
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value: 77.05041986942253
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- type: manhattan_pearson
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value: 79.61799508502459
|
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value: 77.07169617647067
|
| 248 |
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|
| 249 |
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type: STS
|
| 250 |
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dataset:
|
| 251 |
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type: mteb/sts15-sts
|
| 252 |
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name: MTEB STS15
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| 253 |
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config: default
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| 254 |
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split: test
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
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| 256 |
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metrics:
|
| 257 |
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- type: cos_sim_pearson
|
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value: 83.93999019426069
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| 259 |
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- type: cos_sim_spearman
|
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value: 85.21166521594695
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| 261 |
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- type: euclidean_pearson
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value: 84.97207676326357
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| 263 |
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| 264 |
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value: 85.40726578482739
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| 265 |
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- type: manhattan_pearson
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value: 85.0386693192183
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| 267 |
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value: 85.49230945586409
|
| 269 |
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- task:
|
| 270 |
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type: STS
|
| 271 |
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dataset:
|
| 272 |
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type: mteb/sts16-sts
|
| 273 |
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name: MTEB STS16
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| 274 |
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config: default
|
| 275 |
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split: test
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| 276 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
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| 277 |
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metrics:
|
| 278 |
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- type: cos_sim_pearson
|
| 279 |
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value: 80.8133974034008
|
| 280 |
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- type: cos_sim_spearman
|
| 281 |
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value: 82.82919022688844
|
| 282 |
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- type: euclidean_pearson
|
| 283 |
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value: 81.92587923760179
|
| 284 |
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- type: euclidean_spearman
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| 285 |
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value: 82.86629450518863
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| 286 |
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- type: manhattan_pearson
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value: 81.98232365999253
|
| 288 |
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|
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value: 82.94313939920296
|
| 290 |
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- task:
|
| 291 |
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type: STS
|
| 292 |
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dataset:
|
| 293 |
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type: mteb/sts17-crosslingual-sts
|
| 294 |
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name: MTEB STS17 (en-en)
|
| 295 |
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config: en-en
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| 296 |
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split: test
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| 297 |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
|
| 298 |
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metrics:
|
| 299 |
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- type: cos_sim_pearson
|
| 300 |
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value: 86.12872422642363
|
| 301 |
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- type: cos_sim_spearman
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| 302 |
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value: 87.77672179979807
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| 303 |
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- type: euclidean_pearson
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value: 87.76172961705947
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| 305 |
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value: 87.9891393339215
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- type: manhattan_pearson
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value: 87.78863663568221
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| 309 |
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value: 88.08297053203866
|
| 311 |
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|
| 312 |
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type: STS
|
| 313 |
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dataset:
|
| 314 |
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type: mteb/sts22-crosslingual-sts
|
| 315 |
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name: MTEB STS22 (en)
|
| 316 |
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config: en
|
| 317 |
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split: test
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| 318 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
|
| 319 |
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metrics:
|
| 320 |
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- type: cos_sim_pearson
|
| 321 |
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value: 58.82824030232733
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| 322 |
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- type: cos_sim_spearman
|
| 323 |
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value: 64.17079382633538
|
| 324 |
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- type: euclidean_pearson
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| 325 |
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value: 61.31505225602925
|
| 326 |
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| 327 |
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value: 64.05080034530694
|
| 328 |
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value: 61.77095758943306
|
| 330 |
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| 331 |
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value: 64.14475973774933
|
| 332 |
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|
| 333 |
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type: STS
|
| 334 |
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dataset:
|
| 335 |
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type: mteb/stsbenchmark-sts
|
| 336 |
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name: MTEB STSBenchmark
|
| 337 |
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config: default
|
| 338 |
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split: test
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| 339 |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
|
| 340 |
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metrics:
|
| 341 |
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- type: cos_sim_pearson
|
| 342 |
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value: 81.39239803497064
|
| 343 |
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|
| 344 |
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value: 81.76637354520439
|
| 345 |
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- type: euclidean_pearson
|
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value: 82.98008209033587
|
| 347 |
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value: 82.18662536188657
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| 349 |
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| 350 |
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value: 82.9630328314908
|
| 351 |
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- type: manhattan_spearman
|
| 352 |
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value: 82.13726553603003
|
| 353 |
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- task:
|
| 354 |
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type: PairClassification
|
| 355 |
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dataset:
|
| 356 |
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type: mteb/sprintduplicatequestions-pairclassification
|
| 357 |
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name: MTEB SprintDuplicateQuestions
|
| 358 |
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config: default
|
| 359 |
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split: test
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| 360 |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 361 |
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metrics:
|
| 362 |
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- type: cos_sim_accuracy
|
| 363 |
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value: 99.8019801980198
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| 364 |
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- type: cos_sim_ap
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| 365 |
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value: 94.58629018512772
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| 366 |
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- type: cos_sim_f1
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| 367 |
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value: 89.84771573604061
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| 368 |
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- type: cos_sim_precision
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| 369 |
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value: 91.23711340206185
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| 370 |
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- type: cos_sim_recall
|
| 371 |
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value: 88.5
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| 372 |
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- type: dot_accuracy
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value: 99.74950495049505
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- type: dot_precision
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value: 88.86576482830385
|
| 380 |
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- type: dot_recall
|
| 381 |
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value: 85.39999999999999
|
| 382 |
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- type: euclidean_accuracy
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| 383 |
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value: 99.80495049504951
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| 384 |
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- type: euclidean_ap
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| 385 |
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value: 94.56231673602272
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| 386 |
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- type: euclidean_f1
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value: 90.02531645569621
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- type: euclidean_precision
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value: 91.17948717948718
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| 390 |
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- type: euclidean_recall
|
| 391 |
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value: 88.9
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| 392 |
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value: 99.8009900990099
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value: 94.5775591647447
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value: 89.86384266263238
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value: 90.64089521871821
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| 400 |
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- type: manhattan_recall
|
| 401 |
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value: 89.1
|
| 402 |
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- type: max_accuracy
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value: 99.80495049504951
|
| 404 |
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- type: max_ap
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value: 94.58629018512772
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| 406 |
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- type: max_f1
|
| 407 |
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value: 90.02531645569621
|
| 408 |
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- task:
|
| 409 |
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type: Classification
|
| 410 |
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dataset:
|
| 411 |
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type: mteb/toxic_conversations_50k
|
| 412 |
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name: MTEB ToxicConversationsClassification
|
| 413 |
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config: default
|
| 414 |
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split: test
|
| 415 |
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revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
|
| 416 |
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metrics:
|
| 417 |
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- type: accuracy
|
| 418 |
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value: 67.43820000000001
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| 419 |
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- type: ap
|
| 420 |
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value: 12.899489312331003
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| 421 |
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- type: f1
|
| 422 |
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value: 52.03468121072981
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| 423 |
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- task:
|
| 424 |
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type: Classification
|
| 425 |
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dataset:
|
| 426 |
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type: mteb/tweet_sentiment_extraction
|
| 427 |
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name: MTEB TweetSentimentExtractionClassification
|
| 428 |
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config: default
|
| 429 |
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split: test
|
| 430 |
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revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
|
| 431 |
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metrics:
|
| 432 |
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- type: accuracy
|
| 433 |
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value: 57.475947934352
|
| 434 |
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- type: f1
|
| 435 |
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value: 57.77676730676238
|
| 436 |
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- task:
|
| 437 |
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type: PairClassification
|
| 438 |
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dataset:
|
| 439 |
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type: mteb/twittersemeval2015-pairclassification
|
| 440 |
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name: MTEB TwitterSemEval2015
|
| 441 |
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config: default
|
| 442 |
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split: test
|
| 443 |
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revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
| 444 |
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metrics:
|
| 445 |
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- type: cos_sim_accuracy
|
| 446 |
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value: 83.94230196101806
|
| 447 |
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- type: cos_sim_ap
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value: 67.00916556336148
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|
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value: 63.046014257939085
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- type: cos_sim_precision
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value: 61.961783439490446
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- type: cos_sim_recall
|
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value: 64.16886543535621
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- type: dot_accuracy
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value: 83.18531322644095
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| 461 |
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value: 56.63438256658596
|
| 463 |
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- type: dot_recall
|
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value: 61.715039577836414
|
| 465 |
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- type: euclidean_accuracy
|
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value: 83.94230196101806
|
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|
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- type: euclidean_precision
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value: 58.9543682641596
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| 473 |
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- type: euclidean_recall
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value: 67.83641160949868
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value: 62.693323274236135
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value: 59.884698534710544
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| 483 |
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value: 65.77836411609499
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|
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value: 63.08428413691571
|
| 491 |
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- task:
|
| 492 |
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type: PairClassification
|
| 493 |
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dataset:
|
| 494 |
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type: mteb/twitterurlcorpus-pairclassification
|
| 495 |
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name: MTEB TwitterURLCorpus
|
| 496 |
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config: default
|
| 497 |
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split: test
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| 498 |
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revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
| 499 |
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metrics:
|
| 500 |
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- type: cos_sim_accuracy
|
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value: 88.0777738968448
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| 502 |
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value: 75.91830995817077
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- type: cos_sim_precision
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value: 69.84671107949033
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value: 80.18170619032954
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- type: euclidean_accuracy
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value: 88.08359529630924
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- type: euclidean_ap
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value: 84.22633217661986
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| 524 |
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| 525 |
+
value: 76.09190339866403
|
| 526 |
+
- type: euclidean_precision
|
| 527 |
+
value: 72.70304390517605
|
| 528 |
+
- type: euclidean_recall
|
| 529 |
+
value: 79.81213427779488
|
| 530 |
+
- type: manhattan_accuracy
|
| 531 |
+
value: 88.08359529630924
|
| 532 |
+
- type: manhattan_ap
|
| 533 |
+
value: 84.18362004611083
|
| 534 |
+
- type: manhattan_f1
|
| 535 |
+
value: 76.08789625360231
|
| 536 |
+
- type: manhattan_precision
|
| 537 |
+
value: 71.49336582724072
|
| 538 |
+
- type: manhattan_recall
|
| 539 |
+
value: 81.3135201724669
|
| 540 |
+
- type: max_accuracy
|
| 541 |
+
value: 88.08359529630924
|
| 542 |
+
- type: max_ap
|
| 543 |
+
value: 84.22633217661986
|
| 544 |
+
- type: max_f1
|
| 545 |
+
value: 76.09190339866403
|
| 546 |
license: mit
|
| 547 |
---
|
| 548 |
This is the sparse ONNX variant of the [bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model for embeddings created with [DeepSparse Optimum](https://github.com/neuralmagic/optimum-deepsparse) for ONNX export and Neural Magic's [Sparsify](https://account.neuralmagic.com/signin?client_id=d04a5f0c-983d-11ed-88a6-971073f187d3&return_to=https%3A//accounts.neuralmagic.com/v1/connect/authorize%3Fscope%3Dsparsify%3Aread%2Bsparsify%3Awrite%2Buser%3Aapi-key%3Aread%2Buser%3Aprofile%3Awrite%2Buser%3Aprofile%3Aread%26response_type%3Dcode%26code_challenge_method%3DS256%26redirect_uri%3Dhttps%3A//apps.neuralmagic.com/sparsify/oidc/callback.html%26state%3Da9b466a6193c4a7b92cba469408d2495%26client_id%3Dd04a5f0c-983d-11ed-88a6-971073f187d3%26code_challenge%3DP0EkmKBpplTb7crJOGS8YLSwT8UH-BeuD0wuE4JTORQ%26response_mode%3Dquery) for One-Shot quantization and unstructured pruning (50%).
|