Update README.md

README.md

@@ -7,29 +7,30 @@ tags:
 - transformers
 datasets:
 - shunk031/jsnli
+license: cc-by-sa-4.0
+language:
+- ja
 ---
 
-# {MODEL_NAME}
 
-This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+# sup-simcse-ja-large
 
-<!--- Describe your model here -->
 
 ## Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
 ```
-pip install -U sentence-transformers
+pip install -U fugashi[unidic-lite] sentence-transformers
 ```
 
 Then you can use the model like this:
 
 ```python
 from sentence_transformers import SentenceTransformer
-sentences = ["This is an example sentence", "Each sentence is converted"]
+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"]
 
-model = SentenceTransformer('{MODEL_NAME}')
+model = SentenceTransformer("sup-simcse-ja-large")
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -52,8 +53,8 @@ def cls_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
-model = AutoModel.from_pretrained('{MODEL_NAME}')
+tokenizer = AutoTokenizer.from_pretrained("sup-simcse-ja-large")
+model = AutoModel.from_pretrained("sup-simcse-ja-large")
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
@@ -69,24 +70,24 @@ 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={MODEL_NAME})
-
-
-
 ## Full Model Architecture
 ```
 SentenceTransformer(
   (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
-  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
 )
 ```
 
 ## Citing & Authors
 
-<!--- Describe where people can find more information -->
+```
+@misc{
+  hayato-tsukagoshi-2023-simple-simcse-ja,
+  author = {Hayato Tsukagoshi},
+  title = {Japanese Simple-SimCSE},
+  year = {2023},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/hppRC/simple-simcse-ja}}
+}
+```