[DOC] Refine Readme

Signed-off-by: LocoRichard <lichen.wang@zilliz.com>

README.md

@@ -12,10 +12,7 @@ The Longformer model was presented in Longformer: The Long-Document Transformer
 
 **Longformer** models were proposed in “[Longformer: The Long-Document Transformer][2].
 
-Transformer-based models are unable to process long sequences due to their self-attention
-operation, which scales quadratically with the sequence length. To address this limitation,
-we introduce the Longformer with an attention mechanism that scales linearly with sequence
-length, making it easy to process documents of thousands of tokens or longer[2].
+> Transformer-based models are unable to process long sequences due to their self-attention operation, which scales quadratically with the sequence length. To address this limitation, we introduce the Longformer with an attention mechanism that scales linearly with sequence length, making it easy to process documents of thousands of tokens or longer[2].
 
 ### References
 
@@ -27,7 +24,7 @@ length, making it easy to process documents of thousands of tokens or longer[2].
 
 ## Code Example
 
-Use the pretrained model "facebook/dpr-ctx_encoder-single-nq-base"
+Use the pre-trained model "facebook/dpr-ctx_encoder-single-nq-base"
 to generate a text embedding for the sentence "Hello, world.". 
 
 *Write the pipeline*:
@@ -43,7 +40,7 @@ towhee.dc(["Hello, world."]) \
 
 ## Factory Constructor
 
-Create the operator via the following factory method
+Create the operator via the following factory method:
 
 ***text_embedding.dpr(model_name="allenai/longformer-base-4096")***
 
@@ -66,7 +63,7 @@ Supported model names:
 ## Interface
 
 The operator takes a text in string as input.
-It loads tokenizer and pre-trained model using model name.
+It loads tokenizer and pre-trained model using model name
 and then return text embedding in ndarray.
 
 **Parameters:**