Update README.md

README.md

@@ -5,27 +5,28 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
+- ko
 ---
 
 # ddobokki/klue-roberta-small-nli-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.
+한국어 Sentence Transformer 모델입니다.
 
 <!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
-Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+[sentence-transformers](https://www.SBERT.net) 라이브러리를 이용해 사용할 수 있습니다.
 
 ```
 pip install -U sentence-transformers
 ```
 
-Then you can use the model like this:
+사용법
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+sentences = ["흐르는 강물을 거꾸로 거슬러 오르는", "세월이 가면 가슴이 터질 듯한"]
 
 model = SentenceTransformer('ddobokki/klue-roberta-small-nli-sts')
 embeddings = model.encode(sentences)
@@ -35,7 +36,7 @@ print(embeddings)
 
 
 ## Usage (HuggingFace Transformers)
-Without [sentence-transformers](https://www.SBERT.net), 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.
+transformers 라이브러리만 사용할 경우
 
 ```python
 from transformers import AutoTokenizer, AutoModel
@@ -50,7 +51,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+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')
@@ -70,49 +71,12 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
+## Performance
+- Semantic Textual Similarity test set results <br>
 
-
-## Evaluation Results
-
-<!--- Describe how your model was evaluated -->
-
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ddobokki/klue-roberta-small-nli-sts)
-
-
-## Training
-The model was trained with the parameters:
-
-**DataLoader**:
-
-`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 18078 with parameters:
-```
-{'batch_size': 32}
-```
-
-**Loss**:
-
-`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
-  ```
-  {'scale': 20.0, 'similarity_fct': 'cos_sim'}
-  ```
-
-Parameters of the fit()-Method:
-```
-{
-    "epochs": 1,
-    "evaluation_steps": 1807,
-    "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": 1808,
-    "weight_decay": 0.01
-}
-```
+| 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