Minor typos in EmbeddingGemma MRL section

site/en/gemma/docs/embeddinggemma/inference-embeddinggemma-with-sentence-transformers.ipynb

@@ -493,7 +493,7 @@
         "\n",
         "EmbeddingGemma leverages MRL to provide multiple embedding sizes from one model. It's a clever training method that creates a single, high-quality embedding where the most important information is concentrated at the beginning of the vector.\n",
         "\n",
-        "This means you can get a smaller, still very useful embedding by simply taking the first `N` dimensions of the full embedding. Using smaller, truncated embedding is significantly cheaper to store and faster to process, but this efficiency comes at the cost of potential lower quality of embeddings. MRL gives you the power to choose the optimal balance between this speed and accuracy for your application's specific needs.\n",
+        "This means you can get a smaller but still very useful embedding by simply taking the first `N` dimensions of the full embedding. Using smaller, truncated embeddings is significantly cheaper to store and faster to process, but this efficiency comes at the cost of potential lower quality of embeddings. MRL gives you the power to choose the optimal balance between this speed and accuracy for your application's specific needs.\n",
         "\n",
         "Let's use three words `[\"apple\", \"banana\", \"car\"]` and create simplified embeddings to see how MRL works."
       ]
@@ -538,7 +538,7 @@
         "id": "_iv1xG0TPxkm"
       },
       "source": [
-        "Now, for a faster application, you don't need a new model. Simply **turncate** the full embeddings to the first **512 dimensions**. For optimal result, it is also recommended to set `normalize_embeddings=True`, which scales the vectors to a unit length of 1."
+        "Now, for a faster application, you don't need a new model. Simply **truncate** the full embeddings to the first **512 dimensions**. For optimal results, it is also recommended to set `normalize_embeddings=True`, which scales the vectors to a unit length of 1."
       ]
     },
     {
@@ -579,7 +579,7 @@
         "id": "lgkmgzfVP24M"
       },
       "source": [
-        "In extreamly constrained environments, you can further shorten the embeddings to just **256 dimensions**. You can also use the more efficient **dot-product** for similarity calculations instead of the standard **cosine** similarity."
+        "In extremely constrained environments, you can further shorten the embeddings to just **256 dimensions**. You can also use the more efficient **dot-product** for similarity calculations instead of the standard **cosine** similarity."
       ]
     },
     {