metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: korean_embedding_model
results:
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 62.462024005162874
- type: cos_sim_spearman
value: 59.04592371468026
- type: euclidean_pearson
value: 60.118409297960774
- type: euclidean_spearman
value: 59.04592371468026
- type: manhattan_pearson
value: 59.6758261833799
- type: manhattan_spearman
value: 59.10255151100711
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 69.54306440280438
- type: cos_sim_spearman
value: 62.859142390813574
- type: euclidean_pearson
value: 65.6949193466544
- type: euclidean_spearman
value: 62.859152754778854
- type: manhattan_pearson
value: 65.65986839533139
- type: manhattan_spearman
value: 62.82868162534342
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 66.06384755873458
- type: cos_sim_spearman
value: 62.589736136651894
- type: euclidean_pearson
value: 62.78577890775041
- type: euclidean_spearman
value: 62.588858379781634
- type: manhattan_pearson
value: 62.827478623777985
- type: manhattan_spearman
value: 62.617997229102706
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 71.86398880834443
- type: cos_sim_spearman
value: 72.1348002553312
- type: euclidean_pearson
value: 71.6796109730168
- type: euclidean_spearman
value: 72.1349022685911
- type: manhattan_pearson
value: 71.66477952415218
- type: manhattan_spearman
value: 72.09093373400123
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 70.22680219584427
- type: cos_sim_spearman
value: 67.0818395499375
- type: euclidean_pearson
value: 68.24498247750782
- type: euclidean_spearman
value: 67.0818306104199
- type: manhattan_pearson
value: 68.23186143435814
- type: manhattan_spearman
value: 67.06973319437314
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 75.54853695205654
- type: cos_sim_spearman
value: 75.93775396598934
- type: euclidean_pearson
value: 75.10618334577337
- type: euclidean_spearman
value: 75.93775372510834
- type: manhattan_pearson
value: 75.123200749426
- type: manhattan_spearman
value: 75.95755907955946
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 70.22928051288379
- type: cos_sim_spearman
value: 70.13385961598065
- type: euclidean_pearson
value: 69.66948135244029
- type: euclidean_spearman
value: 70.13385923761084
- type: manhattan_pearson
value: 69.66975130970742
- type: manhattan_spearman
value: 70.16415157887303
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.12344529924287
- type: cos_sim_spearman
value: 77.13355009366349
- type: euclidean_pearson
value: 77.73092283054677
- type: euclidean_spearman
value: 77.13355009366349
- type: manhattan_pearson
value: 77.59037018668798
- type: manhattan_spearman
value: 77.00181739561044
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 60.402875441797896
- type: cos_sim_spearman
value: 62.21971197434699
- type: euclidean_pearson
value: 63.08540172189354
- type: euclidean_spearman
value: 62.21971197434699
- type: manhattan_pearson
value: 62.971870200624714
- type: manhattan_spearman
value: 62.17079870601948
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 69.14110875934769
- type: cos_sim_spearman
value: 67.83869999603111
- type: euclidean_pearson
value: 68.32930987602938
- type: euclidean_spearman
value: 67.8387112205369
- type: manhattan_pearson
value: 68.385068161592
- type: manhattan_spearman
value: 67.86635507968924
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 29.185534982566132
- type: cos_sim_spearman
value: 28.71714958933386
- type: dot_pearson
value: 29.185527195235316
- type: dot_spearman
value: 28.71714958933386
{MODEL_NAME}
This is a sentence-transformers model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
Usage (Sentence-Transformers)
Using this model becomes easy when you have sentence-transformers installed:
pip install -U sentence-transformers
Then you can use the model like this:
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
Evaluation Results
For an automated evaluation of this model, see the Sentence Embeddings Benchmark: https://seb.sbert.net
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
(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})
(2): Normalize()
)