TensorFlow Developer Specialization · NLP in TensorFlow

Sarcasm Detection with Conv1D

A fast text-CNN version of sarcasm detection that uses Conv1D and global max pooling to capture local headline patterns.

Conv1D text modelGlobalMaxPooling1DN-gram-like featuresSarcasm detectionFast sequence classifier

GitHub

What this project is about

The Conv1D sarcasm model treats text like a sequence where local phrase patterns can be detected by convolutional filters and pooled for classification.

Implementation focus

The learning was speed and locality: Conv1D can be a strong text baseline when local n-gram-like patterns matter, but it still needs regularization.

Key results and artifacts

Filters: 256
Kernel size: 6
Epoch 1 validation accuracy: ~85.20%
Epoch 10 validation accuracy: ~83.57% with overfitting

What I learned

This page pairs with the Bi-LSTM version and shows how convolutional text models can be simpler and faster while still competitive.

Milestone deep dive

What this project proves in my learning path.

A fast text-CNN version of sarcasm detection that uses Conv1D and global max pooling to capture local headline patterns. 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

Conv1D text modelGlobalMaxPooling1DN-gram-like featuresSarcasm detectionFast sequence classifier

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

Learning result

What changed after it

This page pairs with the Bi-LSTM version and shows how convolutional text models can be simpler and faster while still competitive.

Important outputs and evidence

Filters: 256
Kernel size: 6
Epoch 1 validation accuracy: ~85.20%
Epoch 10 validation accuracy: ~83.57% 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.