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---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# ko-sbert-sts
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
๋ชจ๋ธ์ ์ฌ์ฉํ๊ธฐ ์ํด์๋ `ko-sentence-transformers` ๋ฅผ ์ค์นํด์ผ ํฉ๋๋ค.
```
pip install -U ko-sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["์๋
ํ์ธ์?", "ํ๊ตญ์ด ๋ฌธ์ฅ ์๋ฒ ๋ฉ์ ์ํ ๋ฒํธ ๋ชจ๋ธ์
๋๋ค."]
model = SentenceTransformer('ko-sbert-sts')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
KorSTS ํ์ต ๋ฐ์ดํฐ์
์ผ๋ก ํ์ตํ ํ KorSTS ํ๊ฐ ๋ฐ์ดํฐ์
์ผ๋ก ํ๊ฐํ ๊ฒฐ๊ณผ์
๋๋ค.
- Cosine Pearson: 80.79
- Cosine Spearman: 79.01
- Euclidean Pearson: 78.03
- Euclidean Spearman: 77.31
- Manhattan Pearson: 78.08
- Manhattan Spearman: 77.35
- Dot Pearson: 75.96
- Dot Spearman: 75.20
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 719 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 288,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
- Ham, J., Choe, Y. J., Park, K., Choi, I., & Soh, H. (2020). Kornli and korsts: New benchmark datasets for korean natural language understanding. arXiv
preprint arXiv:2004.03289
- Reimers, Nils and Iryna Gurevych. โSentence-BERT: Sentence Embeddings using Siamese BERT-Networks.โ ArXiv abs/1908.10084 (2019)
- [Ko-Sentence-BERT-SKTBERT](https://github.com/BM-K/KoSentenceBERT-SKT)
- [KoBERT](https://github.com/SKTBrain/KoBERT)
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