Fix typos

README.md

@@ -2678,10 +2678,10 @@ similarity = embedding1 @ embedding2.T
 print(similarity)
 ```
 
-Using pre-defined [SionicEmbeddingModel]() to obtain embeddings.
+Using pre-defined [SionicEmbeddingModel](https://huggingface.co/sionic-ai/sionic-ai-v1/blob/main/model_api.py) to obtain embeddings.
 
 ```python
-import SionicEmbeddingModel
+from model_api import SionicEmbeddingModel
 
 inputs1 = ["first query", "second query"]
 inputs2 = ["third query", "fourth query"]
@@ -2692,20 +2692,20 @@ embedding2 = model.encode(inputs2)
 similarity = embedding1 @ embedding2.T
 print(similarity)
 ```
-Inspired by [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding), we also apply instruction to encode short queries for retrieval task.
-By using `encode_queries()`, you can use instruction to encode queries which is added at the beginning of each query.
+Inspired by [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding), we apply the instruction to encode short queries for retrieval tasks.
+By using `encode_queries()`, you can use instruction to encode queries which is prefixed to each query.
 The instruction to use for both v1 and v2 models is `"query: "`. 
 
 ```python
-import SionicEmbeddingModel
+from model_api import SionicEmbeddingModel
 
 query = ["first query", "second query"]
 passage = ["This is a passage related to the first query", "This is a passage related to the second query"]
 model - SionicEmbeddingModel(url="https://api.sionic.ai/v1/embedding",
                              instruction="query: ",
                              dimension=2048)
-query_embedding = model.encode(query)
-passage_embedding = model.encode(passage)
+query_embedding = model.encode_queries(query)
+passage_embedding = model.encode_corpus(passage)
 similarity = query_embedding @ passage_embedding.T
 print(similarity)
 ```