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README.md
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@@ -1,6 +1,385 @@
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---
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tags:
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- sparse sparsity quantized onnx embeddings int8
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| 4 |
license: mit
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language:
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- en
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@@ -48,5 +427,4 @@ For further details regarding DeepSparse & Sentence Transformers integration, re
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For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
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-
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-
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| 1 |
---
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| 2 |
tags:
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| 3 |
- sparse sparsity quantized onnx embeddings int8
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| 4 |
+
- mteb
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+
model-index:
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- name: gte-large-sparse
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results:
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- task:
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type: STS
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dataset:
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type: mteb/biosses-sts
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name: MTEB BIOSSES
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config: default
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split: test
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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metrics:
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- type: cos_sim_pearson
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value: 88.64253410928214
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- type: cos_sim_spearman
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value: 85.83388349410652
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- type: euclidean_pearson
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value: 86.86126159318735
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- type: euclidean_spearman
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value: 85.61580623591163
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- type: manhattan_pearson
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value: 86.6901132883383
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- type: manhattan_spearman
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value: 85.60255292187769
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- task:
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type: STS
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dataset:
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type: mteb/sickr-sts
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name: MTEB SICK-R
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config: default
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split: test
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revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
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metrics:
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- type: cos_sim_pearson
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value: 85.23314640591607
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- type: cos_sim_spearman
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value: 79.00078545104338
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- type: euclidean_pearson
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value: 83.48009254500714
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- type: euclidean_spearman
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value: 78.95413001389939
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- type: manhattan_pearson
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| 47 |
+
value: 83.46945566025941
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- type: manhattan_spearman
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value: 78.9241707208135
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- task:
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type: STS
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dataset:
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type: mteb/sts12-sts
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name: MTEB STS12
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config: default
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split: test
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revision: a0d554a64d88156834ff5ae9920b964011b16384
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metrics:
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- type: cos_sim_pearson
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value: 81.77526666043804
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- type: cos_sim_spearman
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value: 73.4849063285867
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- type: euclidean_pearson
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value: 78.04477932740524
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- type: euclidean_spearman
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value: 73.01394205771743
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- type: manhattan_pearson
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value: 78.08836684503294
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- type: manhattan_spearman
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value: 73.05074711098149
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- task:
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type: STS
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dataset:
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type: mteb/sts13-sts
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name: MTEB STS13
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config: default
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split: test
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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metrics:
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- type: cos_sim_pearson
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| 81 |
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value: 84.57839215661352
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- type: cos_sim_spearman
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value: 86.13854767345153
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- type: euclidean_pearson
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| 85 |
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value: 85.12712609946449
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- type: euclidean_spearman
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value: 85.52497994789026
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- type: manhattan_pearson
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| 89 |
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value: 85.06833141611173
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- type: manhattan_spearman
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value: 85.45003068636466
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- task:
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type: STS
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dataset:
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type: mteb/sts14-sts
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name: MTEB STS14
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config: default
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| 98 |
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split: test
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| 99 |
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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+
metrics:
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- type: cos_sim_pearson
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| 102 |
+
value: 83.30485126978374
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- type: cos_sim_spearman
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value: 80.36497172462357
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- type: euclidean_pearson
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| 106 |
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value: 82.91977909424605
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| 107 |
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- type: euclidean_spearman
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| 108 |
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value: 80.16995106297438
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- type: manhattan_pearson
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| 110 |
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value: 82.88200991402184
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| 111 |
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- type: manhattan_spearman
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value: 80.14259757215227
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- task:
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type: STS
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dataset:
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type: mteb/sts15-sts
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| 117 |
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name: MTEB STS15
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| 118 |
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config: default
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| 119 |
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split: test
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| 120 |
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
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+
metrics:
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| 122 |
+
- type: cos_sim_pearson
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| 123 |
+
value: 86.99883111314007
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+
- type: cos_sim_spearman
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+
value: 88.531352572377
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+
- type: euclidean_pearson
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| 127 |
+
value: 87.96834578059067
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+
- type: euclidean_spearman
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| 129 |
+
value: 88.44800718542935
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| 130 |
+
- type: manhattan_pearson
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| 131 |
+
value: 87.94889391725033
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+
- type: manhattan_spearman
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| 133 |
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value: 88.45467695837115
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- task:
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type: STS
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dataset:
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type: mteb/sts16-sts
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| 138 |
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name: MTEB STS16
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| 139 |
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config: default
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| 140 |
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split: test
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| 141 |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
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| 142 |
+
metrics:
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| 143 |
+
- type: cos_sim_pearson
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| 144 |
+
value: 82.4636984892402
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| 145 |
+
- type: cos_sim_spearman
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| 146 |
+
value: 84.0808920789148
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| 147 |
+
- type: euclidean_pearson
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| 148 |
+
value: 83.70613486028309
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| 149 |
+
- type: euclidean_spearman
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| 150 |
+
value: 84.35941626905009
|
| 151 |
+
- type: manhattan_pearson
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| 152 |
+
value: 83.70259457073782
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| 153 |
+
- type: manhattan_spearman
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| 154 |
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value: 84.35496521501604
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| 155 |
+
- task:
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| 156 |
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type: STS
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| 157 |
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dataset:
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| 158 |
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type: mteb/sts17-crosslingual-sts
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| 159 |
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name: MTEB STS17 (en-en)
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| 160 |
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config: en-en
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| 161 |
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split: test
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| 162 |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
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| 163 |
+
metrics:
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| 164 |
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- type: cos_sim_pearson
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| 165 |
+
value: 88.76172944971023
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| 166 |
+
- type: cos_sim_spearman
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| 167 |
+
value: 89.4190945039165
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| 168 |
+
- type: euclidean_pearson
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| 169 |
+
value: 89.47263005347381
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| 170 |
+
- type: euclidean_spearman
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| 171 |
+
value: 89.49228360724095
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| 172 |
+
- type: manhattan_pearson
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| 173 |
+
value: 89.49959868816694
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| 174 |
+
- type: manhattan_spearman
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| 175 |
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value: 89.5314536157954
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| 176 |
+
- task:
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| 177 |
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type: STS
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| 178 |
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dataset:
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| 179 |
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type: mteb/sts22-crosslingual-sts
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| 180 |
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name: MTEB STS22 (en)
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| 181 |
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config: en
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| 182 |
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split: test
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| 183 |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
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| 184 |
+
metrics:
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| 185 |
+
- type: cos_sim_pearson
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| 186 |
+
value: 64.57158223787549
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| 187 |
+
- type: cos_sim_spearman
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| 188 |
+
value: 66.75053533168037
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| 189 |
+
- type: euclidean_pearson
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| 190 |
+
value: 66.45526604831747
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| 191 |
+
- type: euclidean_spearman
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| 192 |
+
value: 66.14567667353113
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| 193 |
+
- type: manhattan_pearson
|
| 194 |
+
value: 66.47352000151176
|
| 195 |
+
- type: manhattan_spearman
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| 196 |
+
value: 66.21099856852885
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| 197 |
+
- task:
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type: STS
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dataset:
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type: mteb/stsbenchmark-sts
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name: MTEB STSBenchmark
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| 202 |
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config: default
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| 203 |
+
split: test
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| 204 |
+
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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| 205 |
+
metrics:
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| 206 |
+
- type: cos_sim_pearson
|
| 207 |
+
value: 85.055653571006
|
| 208 |
+
- type: cos_sim_spearman
|
| 209 |
+
value: 85.45387832634702
|
| 210 |
+
- type: euclidean_pearson
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| 211 |
+
value: 86.31667154906651
|
| 212 |
+
- type: euclidean_spearman
|
| 213 |
+
value: 85.66079590537946
|
| 214 |
+
- type: manhattan_pearson
|
| 215 |
+
value: 86.2806853257308
|
| 216 |
+
- type: manhattan_spearman
|
| 217 |
+
value: 85.63700636713952
|
| 218 |
+
- task:
|
| 219 |
+
type: PairClassification
|
| 220 |
+
dataset:
|
| 221 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
| 222 |
+
name: MTEB SprintDuplicateQuestions
|
| 223 |
+
config: default
|
| 224 |
+
split: test
|
| 225 |
+
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
|
| 226 |
+
metrics:
|
| 227 |
+
- type: cos_sim_accuracy
|
| 228 |
+
value: 99.78811881188119
|
| 229 |
+
- type: cos_sim_ap
|
| 230 |
+
value: 94.67027715905307
|
| 231 |
+
- type: cos_sim_f1
|
| 232 |
+
value: 89.33074684772066
|
| 233 |
+
- type: cos_sim_precision
|
| 234 |
+
value: 86.7231638418079
|
| 235 |
+
- type: cos_sim_recall
|
| 236 |
+
value: 92.10000000000001
|
| 237 |
+
- type: dot_accuracy
|
| 238 |
+
value: 99.47128712871287
|
| 239 |
+
- type: dot_ap
|
| 240 |
+
value: 78.41478815918727
|
| 241 |
+
- type: dot_f1
|
| 242 |
+
value: 73.30049261083744
|
| 243 |
+
- type: dot_precision
|
| 244 |
+
value: 72.23300970873787
|
| 245 |
+
- type: dot_recall
|
| 246 |
+
value: 74.4
|
| 247 |
+
- type: euclidean_accuracy
|
| 248 |
+
value: 99.78415841584159
|
| 249 |
+
- type: euclidean_ap
|
| 250 |
+
value: 94.60075930867181
|
| 251 |
+
- type: euclidean_f1
|
| 252 |
+
value: 89.12175648702593
|
| 253 |
+
- type: euclidean_precision
|
| 254 |
+
value: 88.94422310756973
|
| 255 |
+
- type: euclidean_recall
|
| 256 |
+
value: 89.3
|
| 257 |
+
- type: manhattan_accuracy
|
| 258 |
+
value: 99.78415841584159
|
| 259 |
+
- type: manhattan_ap
|
| 260 |
+
value: 94.62867439278095
|
| 261 |
+
- type: manhattan_f1
|
| 262 |
+
value: 89.2337536372454
|
| 263 |
+
- type: manhattan_precision
|
| 264 |
+
value: 86.62900188323917
|
| 265 |
+
- type: manhattan_recall
|
| 266 |
+
value: 92.0
|
| 267 |
+
- type: max_accuracy
|
| 268 |
+
value: 99.78811881188119
|
| 269 |
+
- type: max_ap
|
| 270 |
+
value: 94.67027715905307
|
| 271 |
+
- type: max_f1
|
| 272 |
+
value: 89.33074684772066
|
| 273 |
+
- task:
|
| 274 |
+
type: PairClassification
|
| 275 |
+
dataset:
|
| 276 |
+
type: mteb/twittersemeval2015-pairclassification
|
| 277 |
+
name: MTEB TwitterSemEval2015
|
| 278 |
+
config: default
|
| 279 |
+
split: test
|
| 280 |
+
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
|
| 281 |
+
metrics:
|
| 282 |
+
- type: cos_sim_accuracy
|
| 283 |
+
value: 85.09864695714371
|
| 284 |
+
- type: cos_sim_ap
|
| 285 |
+
value: 70.33704198164713
|
| 286 |
+
- type: cos_sim_f1
|
| 287 |
+
value: 66.22893954410307
|
| 288 |
+
- type: cos_sim_precision
|
| 289 |
+
value: 62.42410088743577
|
| 290 |
+
- type: cos_sim_recall
|
| 291 |
+
value: 70.52770448548813
|
| 292 |
+
- type: dot_accuracy
|
| 293 |
+
value: 79.11426357513263
|
| 294 |
+
- type: dot_ap
|
| 295 |
+
value: 49.15484584572233
|
| 296 |
+
- type: dot_f1
|
| 297 |
+
value: 51.12580243364951
|
| 298 |
+
- type: dot_precision
|
| 299 |
+
value: 40.13840830449827
|
| 300 |
+
- type: dot_recall
|
| 301 |
+
value: 70.3957783641161
|
| 302 |
+
- type: euclidean_accuracy
|
| 303 |
+
value: 85.15825236931514
|
| 304 |
+
- type: euclidean_ap
|
| 305 |
+
value: 70.51017350854076
|
| 306 |
+
- type: euclidean_f1
|
| 307 |
+
value: 66.45416294785159
|
| 308 |
+
- type: euclidean_precision
|
| 309 |
+
value: 64.29805082654823
|
| 310 |
+
- type: euclidean_recall
|
| 311 |
+
value: 68.7598944591029
|
| 312 |
+
- type: manhattan_accuracy
|
| 313 |
+
value: 85.1403707456637
|
| 314 |
+
- type: manhattan_ap
|
| 315 |
+
value: 70.47587863399994
|
| 316 |
+
- type: manhattan_f1
|
| 317 |
+
value: 66.4576802507837
|
| 318 |
+
- type: manhattan_precision
|
| 319 |
+
value: 63.32138590203107
|
| 320 |
+
- type: manhattan_recall
|
| 321 |
+
value: 69.92084432717678
|
| 322 |
+
- type: max_accuracy
|
| 323 |
+
value: 85.15825236931514
|
| 324 |
+
- type: max_ap
|
| 325 |
+
value: 70.51017350854076
|
| 326 |
+
- type: max_f1
|
| 327 |
+
value: 66.4576802507837
|
| 328 |
+
- task:
|
| 329 |
+
type: PairClassification
|
| 330 |
+
dataset:
|
| 331 |
+
type: mteb/twitterurlcorpus-pairclassification
|
| 332 |
+
name: MTEB TwitterURLCorpus
|
| 333 |
+
config: default
|
| 334 |
+
split: test
|
| 335 |
+
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
|
| 336 |
+
metrics:
|
| 337 |
+
- type: cos_sim_accuracy
|
| 338 |
+
value: 88.8539604921023
|
| 339 |
+
- type: cos_sim_ap
|
| 340 |
+
value: 85.71869912577101
|
| 341 |
+
- type: cos_sim_f1
|
| 342 |
+
value: 78.00535626720983
|
| 343 |
+
- type: cos_sim_precision
|
| 344 |
+
value: 76.46232344893885
|
| 345 |
+
- type: cos_sim_recall
|
| 346 |
+
value: 79.61194949183862
|
| 347 |
+
- type: dot_accuracy
|
| 348 |
+
value: 84.57717235223348
|
| 349 |
+
- type: dot_ap
|
| 350 |
+
value: 74.89496650237145
|
| 351 |
+
- type: dot_f1
|
| 352 |
+
value: 69.05327823892932
|
| 353 |
+
- type: dot_precision
|
| 354 |
+
value: 65.75666829166377
|
| 355 |
+
- type: dot_recall
|
| 356 |
+
value: 72.69787496150293
|
| 357 |
+
- type: euclidean_accuracy
|
| 358 |
+
value: 88.89471028835332
|
| 359 |
+
- type: euclidean_ap
|
| 360 |
+
value: 85.75169460500409
|
| 361 |
+
- type: euclidean_f1
|
| 362 |
+
value: 78.17055393586006
|
| 363 |
+
- type: euclidean_precision
|
| 364 |
+
value: 74.21118184334348
|
| 365 |
+
- type: euclidean_recall
|
| 366 |
+
value: 82.57622420696026
|
| 367 |
+
- type: manhattan_accuracy
|
| 368 |
+
value: 88.92187681918733
|
| 369 |
+
- type: manhattan_ap
|
| 370 |
+
value: 85.7496679471825
|
| 371 |
+
- type: manhattan_f1
|
| 372 |
+
value: 78.11088295687884
|
| 373 |
+
- type: manhattan_precision
|
| 374 |
+
value: 75.82083061535117
|
| 375 |
+
- type: manhattan_recall
|
| 376 |
+
value: 80.5435786880197
|
| 377 |
+
- type: max_accuracy
|
| 378 |
+
value: 88.92187681918733
|
| 379 |
+
- type: max_ap
|
| 380 |
+
value: 85.75169460500409
|
| 381 |
+
- type: max_f1
|
| 382 |
+
value: 78.17055393586006
|
| 383 |
license: mit
|
| 384 |
language:
|
| 385 |
- en
|
|
|
|
| 427 |
|
| 428 |
For general questions on these models and sparsification methods, reach out to the engineering team on our [community Slack](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ).
|
| 429 |
|
| 430 |
+
|
|
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