update writeup

Author: dnth

examples/embeddings-timm.ipynb

@@ -120,7 +120,7 @@
     "In this notebook we will the [Price Match Guarantee Dataset](https://www.kaggle.com/competitions/shopee-product-matching/) from Shopee from Kaggle.\n",
     "The dataset consists of images from users who sell products on the Shopee online platform.\n",
     "\n",
-    "Download the dataset [here](https://www.kaggle.com/competitions/shopee-product-matching/data) and place it in the current directory.\n",
+    "Download the dataset [here](https://www.kaggle.com/competitions/shopee-product-matching/data), unzip, and place it in the current directory.\n",
     "\n",
     "Here's snapshot showing some of the images from the dataset.\n",
     "![download.png](attachment:dfe9e6ec-0e53-422e-bb5c-b43ad4577d05.png)"
@@ -185,7 +185,7 @@
    "id": "9a1e56b1-d8cd-4457-9b2b-83c1aa3ccaaf",
    "metadata": {},
    "source": [
-    "## Extract Embeddings using TIMM\n",
+    "## Compute Embeddings using TIMM\n",
     "Loading TIMM models in fastdup is seamless with the `TimmEncoder` wrapper class. This ensures all TIMM models can be used in fastdup to compute the embeddings of your dataset.\n",
     "Under the hood, the wrapper class loads the model from TIMM excluding the final classification layer.\n",
     "\n",