Update README.md

README.md

@@ -2972,7 +2972,7 @@ Query: Where can I get the best tacos?
 ### Using Huggingface transformers
 
 
-You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
+You can use the transformers package for a snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
 
 
 
@@ -2995,14 +2995,14 @@ document_tokens =  tokenizer(documents, padding=True, truncation=True, return_te
 # Compute token embeddings
 with torch.no_grad():
     query_embeddings = model(**query_tokens)[0][:, 0]
-    doument_embeddings = model(**document_tokens)[0][:, 0]
+    document_embeddings = model(**document_tokens)[0][:, 0]
 
 
 # normalize embeddings
 query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
-doument_embeddings = torch.nn.functional.normalize(doument_embeddings, p=2, dim=1)
+document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)
 
-scores = torch.mm(query_embeddings, doument_embeddings.transpose(0, 1))
+scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
 for query, query_scores in zip(queries, scores):
     doc_score_pairs = list(zip(documents, query_scores))
     doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)