sentence-transformers safetensors roberta feature-extraction sentence-similarity mteb model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: korean_embedding_model
  results:
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      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:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      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:
      name: MTEB STS12
      type: mteb/sts12-sts
      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:
      name: MTEB STS13
      type: mteb/sts13-sts
      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:
      name: MTEB STS14
      type: mteb/sts14-sts
      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:
      name: MTEB STS15
      type: mteb/sts15-sts
      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:
      name: MTEB STS16
      type: mteb/sts16-sts
      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:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      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:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      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:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      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:
      name: MTEB SummEval
      type: 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](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.

<!--- Describe your model here -->

## Usage (Sentence-Transformers)

Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:

```
pip install -U sentence-transformers
```

Then you can use the model like this:

```python
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

<!--- Describe how your model was evaluated -->

For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})



## 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()
)
```

## Citing & Authors

<!--- Describe where people can find more information -->