---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-classification
base_model: distilbert/distilbert-base-uncased
tags:
- distilbert
- netflix
- text-classification
- movie
- tv-show
---




# Netflix Movie-TV Show Classifier

## Model Description

This project uses a fine-tuned DistilBERT model to classify Netflix content as either a Movie or a TV Show.

## Base Model

distilbert-base-uncased

## Dataset

Netflix Titles Dataset (`netflix_titles.csv`)

The dataset contains information about Netflix movies and TV shows, including:

- Title
- Description
- Genre
- Director
- Cast
- Country
- Release Year
- Rating
- Type

## Model Input

The model uses:

Title + Description + Genre

Example:

"The Lost Kingdom. A young warrior travels across an ancient kingdom to rescue his family. Action, Adventure."

## Output Classes

- Movie
- TV Show

## Fine-Tuning

The pre-trained DistilBERT model was fine-tuned using the Netflix dataset for binary text classification.

Training settings:

- Epochs: 3
- Learning Rate: 2e-5
- Batch Size: 16
- Maximum Sequence Length: 256
- Optimizer: AdamW

## Prediction Example

Input:

Title: The Lost Kingdom

Description: A young warrior travels across an ancient kingdom to rescue his family and defeat a powerful enemy.

Genre: Action, Adventure

Output:

Prediction: Movie

## Evaluation

The model is evaluated using:

- Accuracy
- Precision
- Recall
- F1-score

## Technology Used

- Python
- PyTorch
- Hugging Face Transformers
- DistilBERT
- Pandas
- Scikit-learn
- Google Colab

## Purpose

The purpose of this project is to demonstrate how a pre-trained transformer model can be fine-tuned on a custom Netflix dataset for text classification.