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Model: ddobokki/klue-roberta-small-nli-sts
Source: Original Platform

README.md

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+---
+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 -->