docs: Add links to ml-backend directory (#625)

Co-authored-by: caitlinwheeless <[email protected]>

Author: caitlinwheeless

label_studio_ml/examples/bert_classifier/README.md

@@ -27,6 +27,12 @@ The NewModel is a BERT-based text classification model that is designed to work
 - Automatically download the labeled tasks from Label Studio and prepare the data for training.
 - Customize the training parameters such as learning rate, number of epochs, and weight decay.
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`bert_classifier` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/bert_classifier). 
+
 
 ## Running with Docker (recommended)
 

label_studio_ml/examples/easyocr/README.md

@@ -24,6 +24,12 @@ The primary function of this connection is to recognize and extract text from im
 
 In the context of Label Studio, this connection enhances the platform's labeling capabilities, allowing users to automatically generate labels for text in images. This can be particularly useful in tasks such as data annotation, document digitization, and more.
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`easyocr` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/easyocr). 
+
 ## Labeling configuration
 
 The EasyOCR model connection can be used with the default labeling configuration for OCR in Label Studio. This configuration typically involves defining the types of labels to be used (e.g., text, handwriting, etc.) and the regions of the image where these labels should be applied.

label_studio_ml/examples/flair/README.md

@@ -20,6 +20,12 @@ image: "/tutorials/flair.png"
 
 This example demonstrates how to use Flair NER model with Label Studio.
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`flair` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/flair). 
+
 ## Quickstart
 
 1. Build and start the Machine Learning backend on `http://localhost:9090`

label_studio_ml/examples/gliner/README.md

@@ -24,6 +24,12 @@ The GLiNER model is a BERT family model for generalist NER. We download the mode
 model is
 available on [GitHub](https://github.com/urchade/GLiNER).
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`gliner` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/gliner). 
+
 
 ## Running with Docker (recommended)
 

label_studio_ml/examples/grounding_dino/README.md

@@ -29,6 +29,12 @@ This integration will allow you to:
 
 See [here](https://github.com/IDEA-Research/GroundingDINO) for more details about the pre-trained Grounding DINO model. 
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`grounding_dino` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/grounding_dino). 
+
 
 ## Quickstart
 

label_studio_ml/examples/grounding_sam/README.md

@@ -32,6 +32,12 @@ This integration will allow you to:
 
 See [here](https://github.com/IDEA-Research/GroundingDINO) for more details about the pre-trained Grounding DINO model. 
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`grounding_sam` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/grounding_sam). 
+
 
 ## Quickstart
 

label_studio_ml/examples/huggingface_llm/README.md

@@ -23,6 +23,12 @@ This machine learning backend is designed to work with Label Studio, providing a
 
 Check [text generation pipelines on Hugging Face](https://huggingface.co/tasks/text-generation) for more details.
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`huggingface_llm` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/huggingface_llm). 
+
 ## Label Studio XML labeling config
 
 This ML backend is compatible with a Label Studio labeling configuration that uses a `<TextArea>` tag. Here is an example of a compatible labeling configuration:

label_studio_ml/examples/huggingface_ner/README.md

@@ -25,7 +25,13 @@ The model instantiates `AutoModelForTokenClassification` from Hugging Face's tra
 - If you want to use this model only in inference mode, it serves predictions from the pre-trained model. 
 - If you want to fine-tune the model, you can use the Label Studio interface to provide training data and train the model.
 
-Read more about the compatible models from [Hugging Face's official documentation](https://huggingface.co/docs/transformers/en/tasks/token_classification)
+Read more about the compatible models from [Hugging Face's official documentation](https://huggingface.co/docs/transformers/en/tasks/token_classification). 
+
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`huggingface_ner` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/huggingface_ner). 
 
 
 ## Labeling configuration

label_studio_ml/examples/interactive_substring_matching/README.md

@@ -20,6 +20,12 @@ image: "/tutorials/interactive-substring-matching.png"
 
 The Machine Learning (ML) backend is designed to enhance the efficiency of auto-labeling in Named Entity Recognition (NER) tasks. It achieves this by selecting a keyword and automatically matching the same keyword in the provided text. 
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`interactive_substring_matching` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/interactive_substring_matching). 
+
 ## Recommended labeling config
 
 This ML backend works with the default NER template from Label Studio. You can find this by selecting Label Studio's pre-built NER template when configuring the labeling interface. It is available under **Natural Language Processing > Named Entity Recognition**.

label_studio_ml/examples/langchain_search_agent/README.md

@@ -27,6 +27,12 @@ This example demonstrates how to use Label Studio with a custom Machine Learning
 It uses a [Langchain](https://www.langchain.com/)-based agent that accepts a text input, searches for Google,
 and returns the answer based on the search results (a.k.a Retrieval Augmented Generation).
 
+## Before you begin
+
+Before you begin, you must install the [Label Studio ML backend](https://github.com/HumanSignal/label-studio-ml-backend?tab=readme-ov-file#quickstart). 
+
+This tutorial uses the [`langchain_search_agent` example](https://github.com/HumanSignal/label-studio-ml-backend/tree/master/label_studio_ml/examples/langchain_search_agent). 
+
 ## Prerequisites
 
 ### Use Google Search