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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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- mteb |
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model-index: |
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- name: korean_embedding_model |
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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: 62.462024005162874 |
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- type: cos_sim_spearman |
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value: 59.04592371468026 |
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- type: euclidean_pearson |
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value: 60.118409297960774 |
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- type: euclidean_spearman |
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value: 59.04592371468026 |
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- type: manhattan_pearson |
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value: 59.6758261833799 |
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- type: manhattan_spearman |
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value: 59.10255151100711 |
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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: 69.54306440280438 |
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- type: cos_sim_spearman |
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value: 62.859142390813574 |
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- type: euclidean_pearson |
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value: 65.6949193466544 |
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- type: euclidean_spearman |
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value: 62.859152754778854 |
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- type: manhattan_pearson |
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value: 65.65986839533139 |
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- type: manhattan_spearman |
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value: 62.82868162534342 |
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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: 66.06384755873458 |
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- type: cos_sim_spearman |
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value: 62.589736136651894 |
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- type: euclidean_pearson |
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value: 62.78577890775041 |
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- type: euclidean_spearman |
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value: 62.588858379781634 |
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- type: manhattan_pearson |
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value: 62.827478623777985 |
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- type: manhattan_spearman |
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value: 62.617997229102706 |
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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: 71.86398880834443 |
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- type: cos_sim_spearman |
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value: 72.1348002553312 |
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- type: euclidean_pearson |
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value: 71.6796109730168 |
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- type: euclidean_spearman |
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value: 72.1349022685911 |
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- type: manhattan_pearson |
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value: 71.66477952415218 |
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- type: manhattan_spearman |
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value: 72.09093373400123 |
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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: 70.22680219584427 |
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- type: cos_sim_spearman |
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value: 67.0818395499375 |
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- type: euclidean_pearson |
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value: 68.24498247750782 |
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- type: euclidean_spearman |
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value: 67.0818306104199 |
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- type: manhattan_pearson |
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value: 68.23186143435814 |
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- type: manhattan_spearman |
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value: 67.06973319437314 |
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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: 75.54853695205654 |
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- type: cos_sim_spearman |
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value: 75.93775396598934 |
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- type: euclidean_pearson |
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value: 75.10618334577337 |
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- type: euclidean_spearman |
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value: 75.93775372510834 |
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- type: manhattan_pearson |
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value: 75.123200749426 |
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- type: manhattan_spearman |
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value: 75.95755907955946 |
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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: 70.22928051288379 |
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- type: cos_sim_spearman |
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value: 70.13385961598065 |
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- type: euclidean_pearson |
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value: 69.66948135244029 |
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- type: euclidean_spearman |
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value: 70.13385923761084 |
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- type: manhattan_pearson |
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value: 69.66975130970742 |
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- type: manhattan_spearman |
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value: 70.16415157887303 |
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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: 77.12344529924287 |
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- type: cos_sim_spearman |
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value: 77.13355009366349 |
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- type: euclidean_pearson |
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value: 77.73092283054677 |
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- type: euclidean_spearman |
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value: 77.13355009366349 |
|
- type: manhattan_pearson |
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value: 77.59037018668798 |
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- type: manhattan_spearman |
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value: 77.00181739561044 |
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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: eea2b4fe26a775864c896887d910b76a8098ad3f |
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metrics: |
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- type: cos_sim_pearson |
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value: 60.402875441797896 |
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- type: cos_sim_spearman |
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value: 62.21971197434699 |
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- type: euclidean_pearson |
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value: 63.08540172189354 |
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- type: euclidean_spearman |
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value: 62.21971197434699 |
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- type: manhattan_pearson |
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value: 62.971870200624714 |
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- type: manhattan_spearman |
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value: 62.17079870601948 |
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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: 69.14110875934769 |
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- type: cos_sim_spearman |
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value: 67.83869999603111 |
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- type: euclidean_pearson |
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value: 68.32930987602938 |
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- type: euclidean_spearman |
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value: 67.8387112205369 |
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- type: manhattan_pearson |
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value: 68.385068161592 |
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- type: manhattan_spearman |
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value: 67.86635507968924 |
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- task: |
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type: Summarization |
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dataset: |
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type: mteb/summeval |
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name: MTEB SummEval |
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config: default |
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split: test |
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revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
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metrics: |
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- type: cos_sim_pearson |
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value: 29.185534982566132 |
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- type: cos_sim_spearman |
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value: 28.71714958933386 |
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- type: dot_pearson |
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value: 29.185527195235316 |
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- type: dot_spearman |
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value: 28.71714958933386 |
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--- |
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# {MODEL_NAME} |
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This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search. |
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<!--- Describe your model here --> |
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## Usage (Sentence-Transformers) |
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Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: |
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``` |
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pip install -U sentence-transformers |
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``` |
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Then you can use the model like this: |
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```python |
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from sentence_transformers import SentenceTransformer |
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sentences = ["This is an example sentence", "Each sentence is converted"] |
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model = SentenceTransformer('{MODEL_NAME}') |
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embeddings = model.encode(sentences) |
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print(embeddings) |
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``` |
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## Evaluation Results |
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<!--- Describe how your model was evaluated --> |
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For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) |
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## Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel |
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(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) |
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(2): Normalize() |
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) |
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``` |
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## Citing & Authors |
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<!--- Describe where people can find more information --> |
