TensorFlow Developer Specialization · NLP in TensorFlow

Sarcasm Detection with Bidirectional LSTM

A headline-level sarcasm classifier using embeddings and a bidirectional LSTM to capture left/right context.

Sarcasm detectionBidirectional contextEmbedding layerSequence classificationValidation monitoring

What this project is about

Sarcasm detection is a compact headline classification task where context matters. The Bi-LSTM reads sequence information in both directions before classification.

Implementation focus

This project made overfitting visible: training accuracy can become almost perfect while validation loss worsens. That is a useful engineering lesson.

Key results and artifacts

Training examples: 20,000
Epoch 1 validation accuracy: ~84.84%
Epoch 10 training accuracy: ~99.54% with overfitting

What I learned

The useful lesson was not only building the classifier, but recognizing overfitting from validation curves.

Milestone deep dive

What this project proves in my learning path.

A headline-level sarcasm classifier using embeddings and a bidirectional LSTM to capture left/right context. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerimages, anchors, boxes, and class scores

Model layerYOLO-style detection pipeline with filtering and NMS

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerlocalized objects with confidence scores

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

Sarcasm detectionBidirectional contextEmbedding layerSequence classificationValidation monitoring

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

The useful lesson was not only building the classifier, but recognizing overfitting from validation curves.

Important outputs and evidence

Training examples: 20,000
Epoch 1 validation accuracy: ~84.84%
Epoch 10 training accuracy: ~99.54% with overfitting

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.