typo fixes

Author: dnth

examples/enrichment-ram-groundingdino-sam.ipynb

@@ -62,7 +62,7 @@
    "source": [
     "## Installation\n",
     "\n",
-    "First, let's install the neccessary packages:\n",
+    "First, let's install the necessary packages:\n",
     "\n",
     "- [fastdup](https://github.com/visual-layer/fastdup) - To analyze issues in the dataset.\n",
     "- [Recognize Anything](https://github.com/xinyu1205/recognize-anything) - To use the RAM and Tag2Text model.\n",
@@ -118,7 +118,7 @@
    "metadata": {},
    "source": [
     "## Download Dataset\n",
-    "Download the [coco-minitrain](https://github.com/giddyyupp/coco-minitrain) dataset - a curated mini training set consisting of 20% of COCO 2017 training dataset. The coco-minitrain consists of 25,000 images and annoatations."
+    "Download the [coco-minitrain](https://github.com/giddyyupp/coco-minitrain) dataset - a curated mini training set consisting of 20% of COCO 2017 training dataset. The coco-minitrain consists of 25,000 images and annotations."
    ]
   },
   {
@@ -271,7 +271,7 @@
    "source": [
     "## Zero-Shot Classification with RAM and Tag2Text\n",
     "\n",
-    "Within fastdup you can readily use the zero-shot classifier models such as [Recognize Anything Model (RAM)](https://github.com/xinyu1205/recognize-anything) and [Tag2Text](https://github.com/xinyu1205/recognize-anything). Both Tag2Text and RAM exihibit strong recognition ability.\n",
+    "Within fastdup you can readily use the zero-shot classifier models such as [Recognize Anything Model (RAM)](https://github.com/xinyu1205/recognize-anything) and [Tag2Text](https://github.com/xinyu1205/recognize-anything). Both Tag2Text and RAM exhibit strong recognition ability.\n",
     "\n",
     "+ RAM is an image tagging model, which can recognize any common category with high accuracy. Outperforms CLIP and BLIP.\n",
     "+ Tag2Text is a vision-language model guided by tagging, which can support caption, retrieval and tagging."
@@ -1182,7 +1182,7 @@
    "id": "59c8e8d0-1c00-403b-84d9-226458b9268a",
    "metadata": {},
    "source": [
-    "Once, done you'll notice that 3 new columns are appened into the DataFrame namely - `grounding_dino_bboxes`, `grounding_dino_scores`, and `grounding_dino_labels`. "
+    "Once, done you'll notice that 3 new columns are appended into the DataFrame namely - `grounding_dino_bboxes`, `grounding_dino_scores`, and `grounding_dino_labels`. "
    ]
   },
   {
@@ -1897,7 +1897,7 @@
    "id": "7a979b19-eaef-422b-944b-0285115e24d6",
    "metadata": {},
    "source": [
-    "Not all images contain \"face\", \"eye\" and \"hair\", let's remove the columns with no detections and plot the colums with detections."
+    "Not all images contain \"face\", \"eye\" and \"hair\", let's remove the columns with no detections and plot the column with detections."
    ]
   },
   {