Update README.md

README.md

@@ -7699,8 +7699,8 @@ for query, query_scores in zip(queries, scores):
 
 #### Variation: Truncated Embeddings ####
 query_embeddings_256 = normalize(torch.from_numpy(query_embeddings)[:, :256])
-doument_embeddings_256 = normalize(torch.from_numpy(document_embeddings)[:, :256])
-scores_256 = query_embeddings_256 @ doument_embeddings_256.T
+document_embeddings_256 = normalize(torch.from_numpy(document_embeddings)[:, :256])
+scores_256 = query_embeddings_256 @ document_embeddings_256.T
 
 # Pretty-print the results.
 for query, query_scores in zip(queries, scores_256):
@@ -7752,11 +7752,11 @@ document_tokens =  tokenizer(documents, padding=True, truncation=True, return_te
 # Use the model to generate text embeddings.
 with torch.inference_mode():
     query_embeddings = model(**query_tokens)[0][:, 0]
-    doument_embeddings = model(**document_tokens)[0][:, 0]
+    document_embeddings = model(**document_tokens)[0][:, 0]
 
 # Remember to normalize embeddings.
 query_embeddings = normalize(query_embeddings)
-doument_embeddings = normalize(doument_embeddings)
+document_embeddings = normalize(document_embeddings)
 
 # Scores via dotproduct.
 scores = query_embeddings @ document_embeddings.T
@@ -7782,8 +7782,8 @@ for query, query_scores in zip(queries, scores):
 
 #### Variation: Truncated Embeddings ####
 query_embeddings_256 = normalize(query_embeddings[:, :256])
-doument_embeddings_256 = normalize(doument_embeddings[:, :256])
-scores_256 = query_embeddings_256 @ doument_embeddings_256.T
+document_embeddings_256 = normalize(document_embeddings[:, :256])
+scores_256 = query_embeddings_256 @ document_embeddings_256.T
 
 # Pretty-print the results.
 for query, query_scores in zip(queries, scores_256):