Spell Check Round 2

recipes/quickstart/Multi-Modal-RAG/README.md

@@ -2,8 +2,8 @@
 
 ## Story Overview: Multi-Modal RAG using `Llama-3.2-11B` model: 
 
-- **Data Labelling and Preperation:** We start by downloading 5000 images of clothing items and labelling them using 11B model
-- **Clearning Labels:** With the labels based on the notebook above, we will then clean the dataset and prepare it for RAG
+- **Data Labeling and Preparation:** We start by downloading 5000 images of clothing items and labeling them using 11B model
+- **Cleaning Labels:** With the labels based on the notebook above, we will then clean the dataset and prepare it for RAG
 - **Building Vector DB and RAG Pipeline:** With the final clean dataset, we can use descriptions and 11B model to generate recommendations
 
 ## Resources used: 
@@ -26,13 +26,13 @@ Here's the detailed outline:
 
 The dataset consists of 5000 images with some classification.
 
-The first half is preparing the dataset for labelling:
+The first half is preparing the dataset for labeling:
 - Clean/Remove corrupt images
 - EDA to understand existing distribution
 - Merging up categories of clothes to reduce complexity 
 - Balancing dataset by randomly sampling images
 
-Second Half consists of Labelling the dataset. We are bound by an interesting constraint here, 11B model can only caption one image at a time:
+Second Half consists of Labeling the dataset. We are bound by an interesting constraint here, 11B model can only caption one image at a time:
 - We load a few images and test captioning
 - We run this pipeline on random images and iterate on the prompt till we feel the model is giving good outputs
 - Finally, we can create a script to label all 5000 images on multi-GPU
@@ -52,7 +52,7 @@ Now, we are ready to try our vector db pipeline:
 
 With the cleaned descriptions and dataset, we can now store these in a vector-db
 
-You will note that we are not using the categorisation from our model-this is by design to show how RAG can simplify a lot of things. 
+You will note that we are not using the categorization from our model-this is by design to show how RAG can simplify a lot of things. 
 
 - We create embeddings using the text description of our clothes
 - Use 11-B model to describe the uploaded image
@@ -65,7 +65,7 @@ We try the approach with different retrieval methods.
 
 Finally, we can bring this all together in a Gradio App. 
 
-Task: We can futher improve the description prompt. You will notice sometimes the description starts with the title of the cloth which causes in retrival of "similar" clothes instead of "complementary" items
+Task: We can further improve the description prompt. You will notice sometimes the description starts with the title of the cloth which causes in retrieval of "similar" clothes instead of "complementary" items
 
 - Upload an image
 - 11B model describes the image