Update README.md

README.md

@@ -22,9 +22,9 @@ widget:
   example_title: "Sleepy"
 ---
 
-# snunlp/KR-SBERT-V40K-klueNLI-augSTS
+# marigold334/KR-SBERT-V40K-klueNLI-augSTS-ft
 
-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.
+SNUNLP lab에서 tuning한 [KR-SBERT](snunlp/KR-SBERT-V40K-klueNLI-augSTS)를 다시 [fine-tuning](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss)한 버전이다.
 
 <!--- Describe your model here -->
 
@@ -42,7 +42,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
+model = SentenceTransformer('snunlp/KR-SBERT-V40K-klueNLI-augSTS-ft')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -69,7 +69,7 @@ sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
-model = AutoModel.from_pretrained('snunlp/KR-SBERT-V40K-klueNLI-augSTS')
+model = AutoModel.from_pretrained('snunlp/KR-SBERT-V40K-klueNLI-augSTS-ft')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -85,16 +85,6 @@ print("Sentence embeddings:")
 print(sentence_embeddings)
 ```
 
-
-
-## 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=snunlp/KR-SBERT-V40K-klueNLI-augSTS)
-
-
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
@@ -102,29 +92,3 @@ SentenceTransformer(
   (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})
 )
 ```
-
-## Application for document classification
-
-Tutorial in Google Colab: https://colab.research.google.com/drive/1S6WSjOx9h6Wh_rX1Z2UXwx9i_uHLlOiM
-
-
-|Model|Accuracy|
-|-|-|
-|KR-SBERT-Medium-NLI-STS|0.8400|
-|KR-SBERT-V40K-NLI-STS|0.8400|
-|KR-SBERT-V40K-NLI-augSTS|0.8511|
-|KR-SBERT-V40K-klueNLI-augSTS|**0.8628**|
-
-
-## Citation
-
-```bibtex
-@misc{kr-sbert,
-  author = {Park, Suzi and Hyopil Shin},
-  title = {KR-SBERT: A Pre-trained Korean-specific Sentence-BERT model},
-  year = {2021},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/snunlp/KR-SBERT}}
-}
-```