Update README.md

README.md

@@ -43,7 +43,7 @@ sentences = [
 
 embeddings = model.encode(sentences, convert_to_tensor=True)
 print(embeddings.size())
-# [4, 1024]
+# [4, 768]
 
 similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
 print(similarities)
@@ -87,7 +87,7 @@ Evaluated with [JMTEB](https://github.com/sbintuitions/JMTEB).
 - **Model Type:** Sentence Transformer
 - **Base model:** [cl-nagoya/ruri-pt-base](https://huggingface.co/cl-nagoya/ruri-pt-base) 
 - **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 1024
+- **Output Dimensionality:** 768
 - **Similarity Function:** Cosine Similarity
 - **Language:** Japanese
 - **License:** Apache 2.0
@@ -97,9 +97,9 @@ Evaluated with [JMTEB](https://github.com/sbintuitions/JMTEB).
 ### Full Model Architecture
 
 ```
-MySentenceTransformer(
+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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (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, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
 )
 ```