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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- sparse |
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- sparsity |
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- quantized |
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- onnx |
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- embeddings |
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- int8 |
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- mteb |
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- deepsparse |
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model-index: |
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- name: bge-large-en-v1.5-quant |
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results: |
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- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_counterfactual |
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name: MTEB AmazonCounterfactualClassification (en) |
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config: en |
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split: test |
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revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
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metrics: |
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- type: accuracy |
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value: 75.53731343283583 |
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- type: ap |
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value: 38.30609312253564 |
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- type: f1 |
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value: 69.42802757893695 |
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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: 89.27346145216443 |
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- type: cos_sim_spearman |
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value: 88.36526647458979 |
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- type: euclidean_pearson |
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value: 86.83053354694746 |
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- type: euclidean_spearman |
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value: 87.56223612880584 |
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- type: manhattan_pearson |
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value: 86.59250609226758 |
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- type: manhattan_spearman |
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value: 87.70681773644885 |
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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 |
|
value: 86.18998669716373 |
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- type: cos_sim_spearman |
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value: 82.06129973984048 |
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- type: euclidean_pearson |
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value: 83.65969509485801 |
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- type: euclidean_spearman |
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value: 81.91666612708826 |
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- type: manhattan_pearson |
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value: 83.6906794731384 |
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- type: manhattan_spearman |
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value: 81.91752705367436 |
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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: 86.93407086985752 |
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- type: cos_sim_spearman |
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value: 78.82992283957066 |
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- type: euclidean_pearson |
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value: 83.39733473832982 |
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- type: euclidean_spearman |
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value: 78.86999229850214 |
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- type: manhattan_pearson |
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value: 83.39397058098533 |
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- type: manhattan_spearman |
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value: 78.85397971200753 |
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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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value: 87.2586009863056 |
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- type: cos_sim_spearman |
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value: 87.99415514558852 |
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- type: euclidean_pearson |
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value: 86.98993652364359 |
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- type: euclidean_spearman |
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value: 87.72725335668807 |
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- type: manhattan_pearson |
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value: 86.897205761048 |
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- type: manhattan_spearman |
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value: 87.65231103509018 |
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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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split: test |
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
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metrics: |
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- type: cos_sim_pearson |
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value: 85.41417660460755 |
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- type: cos_sim_spearman |
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value: 83.50291886604928 |
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- type: euclidean_pearson |
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value: 84.67758839660924 |
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- type: euclidean_spearman |
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value: 83.4368059512681 |
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- type: manhattan_pearson |
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value: 84.66027228213025 |
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- type: manhattan_spearman |
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value: 83.43472054456252 |
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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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name: MTEB STS15 |
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config: default |
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split: test |
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revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
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metrics: |
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- type: cos_sim_pearson |
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value: 88.02513262365703 |
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- type: cos_sim_spearman |
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value: 89.00430907638267 |
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- type: euclidean_pearson |
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value: 88.16290361497319 |
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- type: euclidean_spearman |
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value: 88.6645154822661 |
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- type: manhattan_pearson |
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value: 88.15337528825458 |
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- type: manhattan_spearman |
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value: 88.66202950081507 |
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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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name: MTEB STS16 |
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config: default |
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split: test |
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revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
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metrics: |
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- type: cos_sim_pearson |
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value: 85.10194022827035 |
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- type: cos_sim_spearman |
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value: 86.45367112223394 |
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- type: euclidean_pearson |
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value: 85.45292931769094 |
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- type: euclidean_spearman |
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value: 86.06607589083283 |
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- type: manhattan_pearson |
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value: 85.4111233047049 |
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- type: manhattan_spearman |
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value: 86.04379654118996 |
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- task: |
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type: STS |
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dataset: |
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type: mteb/sts17-crosslingual-sts |
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name: MTEB STS17 (en-en) |
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config: en-en |
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split: test |
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revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d |
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metrics: |
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- type: cos_sim_pearson |
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value: 89.86966589113663 |
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- type: cos_sim_spearman |
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value: 89.5617056243649 |
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- type: euclidean_pearson |
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value: 89.018495917952 |
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- type: euclidean_spearman |
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value: 88.387335721179 |
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- type: manhattan_pearson |
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value: 89.07568042943448 |
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- type: manhattan_spearman |
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value: 88.51733863475219 |
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- task: |
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type: STS |
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dataset: |
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type: mteb/sts22-crosslingual-sts |
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name: MTEB STS22 (en) |
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config: en |
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split: test |
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revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
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metrics: |
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- type: cos_sim_pearson |
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value: 68.38465344518238 |
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- type: cos_sim_spearman |
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value: 68.15219488291783 |
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- type: euclidean_pearson |
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value: 68.99169681132668 |
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- type: euclidean_spearman |
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value: 68.01334641045888 |
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- type: manhattan_pearson |
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value: 68.84952679202642 |
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- type: manhattan_spearman |
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value: 67.85430179655137 |
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- 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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config: default |
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split: test |
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revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
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metrics: |
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- type: cos_sim_pearson |
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value: 86.60574360222778 |
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- type: cos_sim_spearman |
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value: 87.8878986593873 |
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- type: euclidean_pearson |
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value: 87.11557232168404 |
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- type: euclidean_spearman |
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value: 87.40944677043365 |
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- type: manhattan_pearson |
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value: 87.10395398212532 |
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- type: manhattan_spearman |
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value: 87.35977283466168 |
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- task: |
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type: PairClassification |
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dataset: |
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type: mteb/sprintduplicatequestions-pairclassification |
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name: MTEB SprintDuplicateQuestions |
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config: default |
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split: test |
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revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
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metrics: |
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- type: cos_sim_accuracy |
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value: 99.84752475247525 |
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- type: cos_sim_ap |
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value: 96.49316696572335 |
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- type: cos_sim_f1 |
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value: 92.35352532274081 |
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- type: cos_sim_precision |
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value: 91.71597633136095 |
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- type: cos_sim_recall |
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value: 93.0 |
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- type: dot_accuracy |
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value: 99.77326732673268 |
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- type: dot_ap |
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value: 93.5497681978726 |
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- type: dot_f1 |
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value: 88.35582208895552 |
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- type: dot_precision |
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value: 88.31168831168831 |
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- type: dot_recall |
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value: 88.4 |
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- type: euclidean_accuracy |
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value: 99.84653465346534 |
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- type: euclidean_ap |
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value: 96.36378999360083 |
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- type: euclidean_f1 |
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value: 92.33052944087086 |
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- type: euclidean_precision |
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value: 91.38099902056807 |
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- type: euclidean_recall |
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value: 93.30000000000001 |
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- type: manhattan_accuracy |
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value: 99.84455445544555 |
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- type: manhattan_ap |
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value: 96.36035171233175 |
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- type: manhattan_f1 |
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value: 92.13260761999011 |
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- type: manhattan_precision |
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value: 91.1851126346719 |
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- type: manhattan_recall |
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value: 93.10000000000001 |
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- type: max_accuracy |
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value: 99.84752475247525 |
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- type: max_ap |
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value: 96.49316696572335 |
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- type: max_f1 |
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value: 92.35352532274081 |
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- task: |
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type: PairClassification |
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dataset: |
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type: mteb/twittersemeval2015-pairclassification |
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name: MTEB TwitterSemEval2015 |
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config: default |
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split: test |
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revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
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metrics: |
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- type: cos_sim_accuracy |
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value: 87.26828396018358 |
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- type: cos_sim_ap |
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value: 77.79878217023162 |
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- type: cos_sim_f1 |
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value: 71.0425694621463 |
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- type: cos_sim_precision |
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value: 68.71301775147928 |
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- type: cos_sim_recall |
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value: 73.53562005277044 |
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- type: dot_accuracy |
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value: 84.01978899684092 |
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- type: dot_ap |
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value: 66.12134149171163 |
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- type: dot_f1 |
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value: 63.283507097098365 |
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- type: dot_precision |
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value: 60.393191081275475 |
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- type: dot_recall |
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value: 66.46437994722955 |
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- type: euclidean_accuracy |
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value: 87.24444179531503 |
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- type: euclidean_ap |
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value: 77.84821131946212 |
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- type: euclidean_f1 |
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value: 71.30456661215247 |
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- type: euclidean_precision |
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value: 68.1413801394566 |
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- type: euclidean_recall |
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value: 74.77572559366754 |
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- type: manhattan_accuracy |
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value: 87.19079692436074 |
|
- type: manhattan_ap |
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value: 77.78054941055291 |
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- type: manhattan_f1 |
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value: 71.13002127393318 |
|
- type: manhattan_precision |
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value: 67.65055939062128 |
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- type: manhattan_recall |
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value: 74.9868073878628 |
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- type: max_accuracy |
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value: 87.26828396018358 |
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- type: max_ap |
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value: 77.84821131946212 |
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- type: max_f1 |
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value: 71.30456661215247 |
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- task: |
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type: PairClassification |
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dataset: |
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type: mteb/twitterurlcorpus-pairclassification |
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name: MTEB TwitterURLCorpus |
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config: default |
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split: test |
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revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
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metrics: |
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- type: cos_sim_accuracy |
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value: 88.91023402025847 |
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- type: cos_sim_ap |
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value: 85.94088151184411 |
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- type: cos_sim_f1 |
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value: 78.25673997223645 |
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- type: cos_sim_precision |
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value: 74.45433059919367 |
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- type: cos_sim_recall |
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value: 82.46843239913767 |
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- type: dot_accuracy |
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value: 87.91865564481701 |
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- type: dot_ap |
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value: 82.75373957440969 |
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- type: dot_f1 |
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value: 75.97383507276201 |
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- type: dot_precision |
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value: 72.67294713160854 |
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- type: dot_recall |
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value: 79.5888512473052 |
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- type: euclidean_accuracy |
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value: 88.8539604921023 |
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- type: euclidean_ap |
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value: 85.71590936389937 |
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- type: euclidean_f1 |
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value: 77.82902261742242 |
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- type: euclidean_precision |
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value: 74.7219270279844 |
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- type: euclidean_recall |
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value: 81.20572836464429 |
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- type: manhattan_accuracy |
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value: 88.78992509799356 |
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- type: manhattan_ap |
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value: 85.70200619366904 |
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- type: manhattan_f1 |
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value: 77.85875848203065 |
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- type: manhattan_precision |
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value: 72.94315506222671 |
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- type: manhattan_recall |
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value: 83.48475515860795 |
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- type: max_accuracy |
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value: 88.91023402025847 |
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- type: max_ap |
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value: 85.94088151184411 |
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- type: max_f1 |
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value: 78.25673997223645 |
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--- |
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|
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# bge-large-en-v1.5-quant |
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|
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<div> |
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<img src="https://huggingface.co/zeroshot/bge-large-en-v1.5-quant/resolve/main/bge-large-latency.png" alt="latency" width="500" style="display:inline-block; margin-right:10px;"/> |
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</div> |
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|
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[DeepSparse](https://github.com/neuralmagic/deepsparse) is able to improve latency performance on a 10 core laptop by 4.8X and up to 3.5X on a 16 core AWS instance. |
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## Usage |
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|
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This is the quantized (INT8) ONNX variant of the [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) embeddings model accelerated with [Sparsify](https://github.com/neuralmagic/sparsify) for quantization and [DeepSparseSentenceTransformers](https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/sentence_transformers) for inference. |
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|
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```bash |
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pip install -U deepsparse-nightly[sentence_transformers] |
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``` |
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|
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```python |
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from deepsparse.sentence_transformers import DeepSparseSentenceTransformer |
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model = DeepSparseSentenceTransformer('neuralmagic/bge-large-en-v1.5-quant', export=False) |
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|
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# Our sentences we like to encode |
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sentences = ['This framework generates embeddings for each input sentence', |
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'Sentences are passed as a list of string.', |
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'The quick brown fox jumps over the lazy dog.'] |
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|
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# Sentences are encoded by calling model.encode() |
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embeddings = model.encode(sentences) |
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|
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# Print the embeddings |
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for sentence, embedding in zip(sentences, embeddings): |
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print("Sentence:", sentence) |
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print("Embedding:", embedding.shape) |
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print("") |
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``` |
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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). |
