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@@ -9,27 +9,24 @@ datasets:
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  - shunk031/jsnli
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  ---
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- # {MODEL_NAME}
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- 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.
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-
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- <!--- Describe your model here -->
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  ## Usage (Sentence-Transformers)
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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  ```
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- pip install -U sentence-transformers
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  ```
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  Then you can use the model like this:
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  ```python
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  from sentence_transformers import SentenceTransformer
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- sentences = ["This is an example sentence", "Each sentence is converted"]
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- model = SentenceTransformer('{MODEL_NAME}')
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
@@ -52,8 +49,8 @@ def cls_pooling(model_output, attention_mask):
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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- tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
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- model = AutoModel.from_pretrained('{MODEL_NAME}')
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -69,16 +66,6 @@ print("Sentence embeddings:")
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  print(sentence_embeddings)
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  ```
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-
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-
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- ## Evaluation Results
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-
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- <!--- Describe how your model was evaluated -->
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-
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- For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
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-
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-
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-
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  ## Full Model Architecture
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  ```
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  SentenceTransformer(
@@ -89,4 +76,14 @@ SentenceTransformer(
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  ## Citing & Authors
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- <!--- Describe where people can find more information -->
 
 
 
 
 
 
 
 
 
 
 
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  - shunk031/jsnli
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  ---
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+ # sup-simcse-ja-base
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  ## Usage (Sentence-Transformers)
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  Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
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  ```
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+ pip install -U fugashi[unidic-lite] sentence-transformers
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  ```
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  Then you can use the model like this:
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  ```python
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  from sentence_transformers import SentenceTransformer
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+ sentences = ["こんにちは、世界!", "文埋め込み最高!文埋め込み最高と叫びなさい", "極度乾燥しなさい"]
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+ model = SentenceTransformer("sup-simcse-ja-base")
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  embeddings = model.encode(sentences)
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  print(embeddings)
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  ```
 
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  sentences = ['This is an example sentence', 'Each sentence is converted']
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  # Load model from HuggingFace Hub
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+ tokenizer = AutoTokenizer.from_pretrained("sup-simcse-ja-base")
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+ model = AutoModel.from_pretrained("sup-simcse-ja-base")
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  # Tokenize sentences
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  encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
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  print(sentence_embeddings)
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  ```
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  ## Full Model Architecture
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  ```
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  SentenceTransformer(
 
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  ## Citing & Authors
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+ ```
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+ @misc{
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+ hayato-tsukagoshi-2023-simple-simcse-ja,
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+ author = {Hayato Tsukagoshi},
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+ title = {Japanese Simple-SimCSE},
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+ year = {2023},
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+ publisher = {GitHub},
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+ journal = {GitHub repository},
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+ howpublished = {\url{https://github.com/hppRC/simple-simcse-ja}}
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+ }
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+ ```