How to Classify Text with Embeddings? (No Fine-Tuning)

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In this session, we're going to take a classic approach to solve text classification problems with a powerful, yet efficient method that doesn't require extensive hardware resources.

Here’s what we'll cover:
1. Embedding Model (Encoder-only):Instead of using a complex, fine-tuned neural network for classification, we’ll leverage a pre-trained embedding model (like GloVe, FastText, or BERT) to convert sentences into dense numerical vectors or embeddings. These embeddings capture the semantic meaning of the sentences in a high-dimensional space.
2. Traditional Machine Learning Models:Once we have the sentence embeddings, we’ll pass them through traditional machine learning classifiers such as Logistic Regression, Support Vector Machines (SVM), or Decision Trees. These models are easier to train, require less computational power, and can run efficiently even on a standard CPU. The simplicity and efficiency of this approach make it an ideal choice for many text classification tasks where training time and resource limitations are a concern.
3. Why This Approach?This method offers several advantages:
Less computational overhead: No need for GPU-intensive fine-tuning.
Faster training times: Traditional classifiers train much faster than deep learning models.
Simplicity: You can quickly adapt this approach to any text classification problem with minimal code and training effort.
4. Step-by-Step Walkthrough:We'll guide you through the entire pipeline, from transforming text data into embeddings to training and evaluating machine learning models. You’ll learn how to implement this approach in Python using popular libraries like scikit-learn, spaCy, and transformers.

By the end of this session, you'll have a solid understanding of how to efficiently approach text classification problems using embeddings and traditional ML algorithms without the need for heavy deep learning infrastructure.

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🔗 Relevant Links:
Code - https://github.com/DiveshRKubal/Large...

📌 Key Takeaways:
Using embedding models (like BERT, GloVe, etc.) for transforming text into numerical representations.
Applying traditional machine learning classifiers such as Logistic Regression and Decision Trees for text classification.
How this method offers computational efficiency and simplicity for quick prototypes and production pipelines.


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