Efficient Text Classification using Lightweight Transformer Models

This project implements an efficient natural language processing (NLP) pipeline for supervised text classification using Transformer-based models. The focus is on practical text understanding, model efficiency, and reproducible ML experimentation, reflecting real-world applied machine learning systems.

The system demonstrates how pretrained Transformer models can be adapted and evaluated for multi-class text classification tasks using supervised learning.

---

Project Overview

Text classification is a fundamental NLP task used in applications such as notification filtering, content categorization, and user intent detection. This project explores Transformer-based text representations to learn semantic features from raw text and perform accurate classification.

The project emphasizes:

• Efficient model selection
• Clean preprocessing pipelines
• Reproducible experimentation
• Evaluation using standard machine learning metrics

---

Notebooks

1. IMDB_text_classification.ipynb — Fine-tunes a Transformer model for IMDB sentiment classification and evaluates performance.
2. tokenization_and_input_representation.ipynb — Shows subword tokenization, special tokens, padding/truncation, and attention masks.
3. bert_model_size_and_memory_analysis.ipynb — Computes parameter counts and estimates model weight memory for Transformer checkpoints.
4. bert_inference_memory_requirements.ipynb — Estimates inference-time memory needs (weights + activations/KV-cache) under FP32/FP16.
5. NLP_tasks_with_Transformers.ipynb — Walkthrough of common NLP tasks supported by Transformer architectures and APIs.
6. Transformers_Text_classification_with_BERT_bias.ipynb — Runs basic bias probing + error analysis patterns for text classification outputs.

---

Machine Learning Approach

The classification pipeline consists of the following steps:

• Text ingestion and normalization
• Tokenization using pretrained Transformer tokenizers
• Feature representation using Transformer encoders
• Supervised fine-tuning for text classification
• Model evaluation using accuracy and F1-score

Lightweight Transformer variants are preferred to balance performance and inference efficiency, reflecting real-world deployment considerations.
This pipeline mirrors production-oriented NLP systems where trade-offs between accuracy, model size, and inference efficiency must be carefully evaluated.

---

Models and Techniques

• Transformer-based language models (BERT / DistilBERT)
• Tokenization and embedding generation
• Supervised fine-tuning for sequence classification
• Evaluation using standard classification metrics
• Experimentation using notebook-based workflows

---

Technologies Used

• Python
• HuggingFace Transformers
• TensorFlow and PyTorch (used across different experiments)
• NumPy
• scikit-learn
• Jupyter Notebook
• Git and GitHub

---

Dataset Description

The project operates on labeled text datasets suitable for supervised classification tasks. The datasets contain short to medium-length text samples with corresponding class labels.

Typical use cases include:

• Message or notification categorization
• Content or topic classification
• Sentiment or intent classification

Datasets are split into training and evaluation sets to assess generalization performance.
Publicly available benchmark datasets are used to ensure reproducibility and objective evaluation.

---

Model Training and Evaluation

During training:

• Pretrained Transformer models are fine-tuned on labeled text data
• Performance is evaluated using accuracy and F1-score
• Results are analyzed to understand trade-offs between model size and classification performance

This approach demonstrates practical experience with modern NLP pipelines, evaluation techniques, and efficiency-aware model design.

---

Key Highlights

• Applied NLP project using Transformer-based models
• Focus on efficiency and real-world usability
• Clean, reproducible experimentation workflow
• Strong alignment with applied machine learning and ML engineering roles

---

Future Enhancements

• Model optimization through quantization or distillation
• Inference benchmarking for latency and memory usage
• Extension to multi-label classification tasks
• Integration with downstream applications or services

---

Author

Developed as an independent applied machine learning project focused on NLP system design, Transformer-based models, and efficient text classification.