Text Classification Pipelines

What is Text Classification?

Text Classification is the process of categorizing unstructured text documents into predefined class labels:

  Raw Input Text ──► [ TEXT CLASSIFICATION PIPELINE ] ──► Predicted Category
  - "My card was charged twice!"              ──► Category: Billing Inquiry
  - "Great product, fast shipping!"           ──► Category: Positive Sentiment
  - "Win a free iPhone, click here now"      ──► Category: Spam

The Evolution of Text Pipelines

┌──────────────────────────┬──────────────────────────┬──────────────────────────┐
│ 1. CLASSICAL TF-IDF      │ 2. FINE TUNED BERT       │ 3. LLM ZERO SHOT         │
├──────────────────────────┼──────────────────────────┼──────────────────────────┤
│ TF-IDF n-grams +         │ Fine-tune BERT /         │ Prompt GPT-4 / LLaMA     │
│ Logistic Regression.     │ DistilBERT model.        │ with class descriptions. │
│ Sub-millisecond CPU speed│ High accuracy, sub-10ms  │ Zero training data,      │
│ Fast baseline.           │ inference. Best for scale│ slow & expensive for ops.│
└──────────────────────────┴──────────────────────────┴──────────────────────────┘

Building a Fine-Tuned BERT Pipeline

When accuracy matters, fine-tuning a small Encoder model (BERT, RoBERTa, DistilBERT) is the industry standard for high-volume production.

  Input Text ──► [ BERT Tokenizer ] ──► [ BERT Encoder ] ──► Extract [CLS] Vector ──► [ Linear Head ] ──► Softmax

1. Special [CLS] Token: BERT prepends a [CLS] token to every input. Its final embedding vector aggregates global sentence context.
2. Classification Head: A simple Linear layer maps the [CLS] vector from dimension $768$ to $C$ output class logits.
3. Training: Fine-tune all weights using Cross Entropy loss with a small learning rate (like $2 \times 10^{-5}$) for 3 to 5 epochs.

Production Best Practices

1. Always Start with a TF-IDF Baseline: Build a TF-IDF + Logistic Regression pipeline first. It trains in 5 seconds and tells you if the problem is easy or hard.
2. Calibrate Decision Thresholds: Default threshold is 0.5. For imbalanced classes (like fraud or toxicity detection), adjust decision thresholds using precision-recall curves.
3. Use DistilBERT for High Throughput: DistilBERT is 40 percent smaller and 60 percent faster than BERT while retaining 97 percent of its language understanding.

Say this out loud

Text classification assigns categories to unstructured text. Pipelines range from fast TF-IDF + Logistic Regression baselines to fine-tuned BERT models and LLM prompts. Fine-tuning BERT extracts the final [CLS] token vector into a linear head, providing high accuracy with sub-10ms inference speed for high-volume production systems.

Followups to expect

1. How do you handle multi label text classification (where a document can belong to multiple categories simultaneously)? Replace the Softmax layer with independent Sigmoid activation functions for each output class, using Binary Cross Entropy loss per class.
2. What is Data Drift in text classification? Shifts in real-world vocabulary over time (for example, new slang or product names). Detect drift by monitoring out-of-vocabulary rates and embedding distribution shifts over time.