Effectively Annotate Text Data for Transformers via Active Learning using Cleanlab (#63)

# What does this PR do?

Demonstrating how to effectively annotate text data for Transformer
models using active learning, specifically leveraging the Cleanlab
open-source package.

* Introduction to active learning and its importance in efficiently
utilizing labeling efforts under budget constraints.

* Implementation of the ActiveLab algorithm, which assists in
prioritizing data for annotation based on the potential impact on model
performance. This is particularly beneficial when dealing with noisy
annotators, as it helps in deciding whether to seek additional
annotations for previously labeled data or new data.

* A detailed walkthrough on iteratively improving a text classification
model by selecting the most impactful data points for annotation,
retraining the model, and evaluating its performance.

## Who can review?

 @MKhalusova appreciate your review.

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Co-authored-by: Maria Khalusova <[email protected]>

Author: aravindputrevu

notebooks/en/_toctree.yml

@@ -32,6 +32,7 @@
     title: Create a legal preference dataset
   - local: semantic_cache_chroma_vector_database
     title: Implementing semantic cache to improve a RAG system.
+  - local: annotate_text_data_transformers_via_active_learning
+    title: Annotate text data using Active Learning with Cleanlab
   - local: llm_judge
     title: Using LLM-as-a-judge for an automated and versatile evaluation
-