---
license: cc-by-sa-4.0
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: genre
    dtype: string
  - name: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: score
    dtype: float64
  splits:
  - name: train
    num_bytes: 1034815
    num_examples: 5691
  - name: valid
    num_bytes: 297254
    num_examples: 1465
  - name: test
    num_bytes: 247409
    num_examples: 1376
  download_size: 837346
  dataset_size: 1579478
---

# Korean Semantic Textual Similarity (KorSTS) Dataset (with converted scores)

For a better dataset description, please visit this GitHub repository prepared by the authors of the article: [LINK](https://github.com/kakaobrain/kor-nlu-datasets) <br>
<br>
**This dataset was prepared by converting [this dataset](https://huggingface.co/datasets/dkoterwa/kor-sts)** Specifically, I've changed the values of scores. If the score was equal or above 2.5, then it was assigned with label "1" (similar). In other cases, label "-1" was assigned (not similar).

I've made these changes in order to apply CosineEmbeddingLoss from PyTorch library [LINK](https://pytorch.org/docs/stable/generated/torch.nn.CosineEmbeddingLoss.html).

**How to download**

```
from datasets import load_dataset
data = load_dataset("dkoterwa/kor-sts-cosine-embedding-loss")
```


**If you use this dataset for research, please cite this paper:**

```
@article{ham2020kornli,
  title={KorNLI and KorSTS: New Benchmark Datasets for Korean Natural Language Understanding},
  author={Ham, Jiyeon and Choe, Yo Joong and Park, Kyubyong and Choi, Ilji and Soh, Hyungjoon},
  journal={arXiv preprint arXiv:2004.03289},
  year={2020}
}
```
region:us
{"dataset_info": {"features": [{"name": "id", "dtype": "int64"}, {"name": "genre", "dtype": "string"}, {"name": "sentence1", "dtype": "string"}, {"name": "sentence2", "dtype": "string"}, {"name": "score", "dtype": "int64"}], "splits": [{"name": "train", "num_bytes": 1034815, "num_examples": 5691}, {"name": "test", "num_bytes": 247409, "num_examples": 1376}, {"name": "valid", "num_bytes": 297254, "num_examples": 1465}], "download_size": 828725, "dataset_size": 1579478}}
TAGS
#region-us 

---
license: cc-by-sa-4.0
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: genre
    dtype: string
  - name: sentence1
    dtype: string
  - name: sentence2
    dtype: string
  - name: score
    dtype: float64
  splits:
  - name: train
    num_bytes: 1034815
    num_examples: 5691
  - name: valid
    num_bytes: 297254
    num_examples: 1465
  - name: test
    num_bytes: 247409
    num_examples: 1376
  download_size: 837346
  dataset_size: 1579478
---

# Korean Semantic Textual Similarity (KorSTS) Dataset (with converted scores)

For a better dataset description, please visit this GitHub repository prepared by the authors of the article: LINK <br>
<br>
This dataset was prepared by converting this dataset Specifically, I've changed the values of scores. If the score was equal or above 2.5, then it was assigned with label "1" (similar). In other cases, label "-1" was assigned (not similar).

I've made these changes in order to apply CosineEmbeddingLoss from PyTorch library LINK.

How to download




If you use this dataset for research, please cite this paper:


# Korean Semantic Textual Similarity (KorSTS) Dataset (with converted scores)

For a better dataset description, please visit this GitHub repository prepared by the authors of the article: LINK <br>
<br>
This dataset was prepared by converting this dataset Specifically, I've changed the values of scores. If the score was equal or above 2.5, then it was assigned with label "1" (similar). In other cases, label "-1" was assigned (not similar).

I've made these changes in order to apply CosineEmbeddingLoss from PyTorch library LINK.

How to download




If you use this dataset for research, please cite this paper:
TAGS
#region-us 
 # Korean Semantic Textual Similarity (KorSTS) Dataset (with converted scores)

For a better dataset description, please visit this GitHub repository prepared by the authors of the article: LINK <br>
<br>
This dataset was prepared by converting this dataset Specifically, I've changed the values of scores. If the score was equal or above 2.5, then it was assigned with label "1" (similar). In other cases, label "-1" was assigned (not similar).

I've made these changes in order to apply CosineEmbeddingLoss from PyTorch library LINK.

How to download




If you use this dataset for research, please cite this paper:
passage: TAGS
#region-us 
# Korean Semantic Textual Similarity (KorSTS) Dataset (with converted scores)

For a better dataset description, please visit this GitHub repository prepared by the authors of the article: LINK <br>
<br>
This dataset was prepared by converting this dataset Specifically, I've changed the values of scores. If the score was equal or above 2.5, then it was assigned with label "1" (similar). In other cases, label "-1" was assigned (not similar).

I've made these changes in order to apply CosineEmbeddingLoss from PyTorch library LINK.

How to download




If you use this dataset for research, please cite this paper: