chore: embed notebook urls in readme

Author: eloy-encord

README.md

@@ -59,7 +59,11 @@ You can easily benchmark different models and datasets against each other. Here
    export ENCORD_SSH_KEY_PATH=<path_to_the_encord_ssh_key_file>
    ```
 
-## CLI Usage
+## CLI Quickstart
+
+<a href="https://colab.research.google.com/github/encord-team/text-to-image-eval/blob/main/notebooks/tti_eval_CLI_Quickstart.ipynb" target="_blank" style="text-decoration:none">
+  <img alt="CLI Quickstart In Colab" src="https://img.shields.io/badge/Try_It_In_Colab-blue?logo=googlecolab&labelColor=555">
+</a>
 
 ### Embeddings Generation
 
@@ -103,6 +107,13 @@ To interactively explore the animation in a temporary session, use the `--intera
     <img width="600" src="https://storage.googleapis.com/docs-media.encord.com/static/img/text-to-image-eval/embeddings.gif">
 </div>
 
+> ℹ️ You can also carry out these operations using Python. Explore our Python Quickstart guide for more details.
+>
+> <a href="https://colab.research.google.com/github/encord-team/text-to-image-eval/blob/main/notebooks/tti_eval_Python_Quickstart.ipynb" target="_blank" style="text-decoration:none">
+>  <img alt="Python Quickstart In Colab" src="https://img.shields.io/badge/Python_Quickstart_In_Colab-blue?logo=googlecolab&labelColor=555">
+> </a>
+
+
 ## Some Example Results
 
 One example of where this `tti-eval` is useful is to test different open-source models against different open-source datasets within a specific domain.
@@ -185,6 +196,10 @@ The models are evaluated against four different medical datasets. Note, Further
 
 ## Datasets
 
+<a href="https://colab.research.google.com/github/encord-team/text-to-image-eval/blob/main/notebooks/tti_eval_Bring_Your_Own_Dataset_From_Encord_Quickstart.ipynb" target="_blank" style="text-decoration:none">
+  <img alt="Datasets Quickstart In Colab" src="https://img.shields.io/badge/Quickstart_In_Colab-blue?logo=googlecolab&labelColor=555">
+</a>
+
 This repository contains classification datasets sourced from [Hugging Face](https://huggingface.co/datasets) and [Encord](https://app.encord.com/projects).
 
 > ⚠️ Currently, only image and image groups datasets are supported, with potential for future expansion to include video datasets.
@@ -258,6 +273,10 @@ However, all embeddings previously built on that dataset will remain intact and
 
 ## Models
 
+<a href="https://colab.research.google.com/github/encord-team/text-to-image-eval/blob/main/notebooks/tti_eval_Bring_Your_Own_Model_From_Hugging_Face_Quickstart.ipynb" target="_blank" style="text-decoration:none">
+  <img alt="Models Quickstart In Colab" src="https://img.shields.io/badge/Quickstart_In_Colab-blue?logo=googlecolab&labelColor=555">
+</a>
+
 This repository contains models sourced from [Hugging Face](https://huggingface.co/models), [OpenCLIP](https://github.com/mlfoundations/open_clip) and local implementations based on OpenCLIP models.
 
 _TODO_: Some more prose about what's the difference between implementations.