readme: ---
  pipeline_tag: sentence-similarity
  tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - ko
  ---
  
  # ddobokki/klue-roberta-small-nli-sts
  
  한국어 Sentence Transformer 모델입니다.
  
  <!--- Describe your model here -->
  
  ## Usage (Sentence-Transformers)
  
  [sentence-transformers](https://www.SBERT.net) 라이브러리를 이용해 사용할 수 있습니다.
  
  ```
  pip install -U sentence-transformers
  ```
  
  사용법
  
  ```python
  from sentence_transformers import SentenceTransformer
  sentences = ["흐르는 강물을 거꾸로 거슬러 오르는", "세월이 가면 가슴이 터질 듯한"]
  
  model = SentenceTransformer('ddobokki/klue-roberta-small-nli-sts')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  
  ## Usage (HuggingFace Transformers)
  transformers 라이브러리만 사용할 경우
  
  ```python
  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 = ["흐르는 강물을 거꾸로 거슬러 오르는", "세월이 가면 가슴이 터질 듯한"]
  
  # Load model from HuggingFace Hub
  tokenizer = AutoTokenizer.from_pretrained('ddobokki/klue-roberta-small-nli-sts')
  model = AutoModel.from_pretrained('ddobokki/klue-roberta-small-nli-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:")
  print(sentence_embeddings)
  ```
  
  ## Performance
  - Semantic Textual Similarity test set results <br>
  
  | Model                  | Cosine Pearson | Cosine Spearman | Euclidean Pearson | Euclidean Spearman | Manhattan Pearson | Manhattan Spearman | Dot Pearson | Dot Spearman |
  |------------------------|:----:|:----:|:----:|:----:|:----:|:----:|:----:|:----:|
  | KoSRoBERTa<sup>small</sup>    | 84.27 | 84.17 | 83.33 | 83.65 | 83.34 | 83.65 | 82.10 | 81.38 |
  
  
  ## Full Model Architecture
  ```
  SentenceTransformer(
    (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
    (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
  
  <!--- Describe where people can find more information -->