Update README.md

README.md

@@ -36,25 +36,6 @@ embeddings = model.encode(sentences)
 print(embeddings)
 ```
 
-## Model Summary
-
-- Fine-tuning method: Supervised SimCSE
-- Base model: [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3)
-- Training dataset: [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88)
-- Pooling strategy: cls (with an extra MLP layer only during training)
-- Hidden size: 768
-- Learning rate: 5e-5
-- Batch size: 512
-- Temperature: 0.05
-- Max sequence length: 64
-- Number of training examples: 2^20
-- Validation interval (steps): 2^6
-- Warmup ratio: 0.1
-- Dtype: BFloat16
-
-See the [GitHub repository](https://github.com/hppRC/simple-simcse-ja) for a detailed experimental setup.
-
-
 ## 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.
 
@@ -96,6 +77,24 @@ SentenceTransformer(
 )
 ```
 
+## Model Summary
+
+- Fine-tuning method: Supervised SimCSE
+- Base model: [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3)
+- Training dataset: [JSNLI](https://nlp.ist.i.kyoto-u.ac.jp/?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88)
+- Pooling strategy: cls (with an extra MLP layer only during training)
+- Hidden size: 768
+- Learning rate: 5e-5
+- Batch size: 512
+- Temperature: 0.05
+- Max sequence length: 64
+- Number of training examples: 2^20
+- Validation interval (steps): 2^6
+- Warmup ratio: 0.1
+- Dtype: BFloat16
+
+See the [GitHub repository](https://github.com/hppRC/simple-simcse-ja) for a detailed experimental setup.
+
 ## Citing & Authors
 
 ```