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This is a sentence-transformers model: It maps sentences & paragraphs to a 768 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 = ["์•ˆ๋…•ํ•˜์„ธ์š”?", "ํ•œ๊ตญ์–ด ๋ฌธ์žฅ ์ž„๋ฒ ๋”ฉ์„ ์œ„ํ•œ ๋ฒ„ํŠธ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค."]

model = SentenceTransformer('jhgan/ko-sbert-sts')
embeddings = model.encode(sentences)

Usage (HuggingFace Transformers)

Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.

from transformers import AutoTokenizer, AutoModel
import torch

#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']

# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('jhgan/ko-sbert-sts')
model = AutoModel.from_pretrained('jhgan/ko-sbert-sts')

# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

# Compute token embeddings
with torch.no_grad():
    model_output = model(**encoded_input)

# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

print("Sentence embeddings:")

Evaluation Results

KorSTS ํ•™์Šต ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ํ•™์Šตํ•œ ํ›„ KorSTS ํ‰๊ฐ€ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ํ‰๊ฐ€ํ•œ ๊ฒฐ๊ณผ์ž…๋‹ˆ๋‹ค.

  • Cosine Pearson: 81.55
  • Cosine Spearman: 81.23
  • Euclidean Pearson: 79.94
  • Euclidean Spearman: 79.79
  • Manhattan Pearson: 79.90
  • Manhattan Spearman: 79.75
  • Dot Pearson: 76.02
  • Dot Spearman: 75.31


The model was trained with the parameters:


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'}



Parameters of the fit()-Method:

    "epochs": 5,
    "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": 360,
    "weight_decay": 0.01

Full Model Architecture

  (0): Transformer({'max_seq_length': 128, '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. โ€œSentence-BERT: Sentence Embeddings using Siamese BERT-Networks.โ€ ArXiv abs/1908.10084 (2019)
  • Reimers, Nils and Iryna Gurevych. โ€œMaking Monolingual Sentence Embeddings Multilingual Using Knowledge Distillation.โ€ EMNLP (2020)
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