bert-base-spoiler-detection

BERT Base Spoiler Detection

Model Description

This model is a fine-tuned version of bert-base-uncased for detecting spoilers in movie and TV show reviews. It classifies reviews as either containing spoilers or being spoiler-free.

Developed by: Tyler Jordan
Model type: Text Classification
Language: English
License: MIT
Base model: bert-base-uncased

Intended Use

Primary Use Case

Automatically detect spoilers in user-generated movie and TV show reviews to warn readers before they encounter plot-revealing content.

Intended Users

• Movie review platforms
• Content moderation systems
• Personal projects for filtering spoilers

Out-of-Scope Uses

• Reviews in languages other than English
• Non-entertainment content (news, academic papers, etc.)
• Legal or medical content requiring high accuracy

Training Data

Dataset: IMDB Review Dataset by Enam Biswas (2021)

Preprocessing:

• Sampled 200,000 balanced reviews (100k spoilers, 100k non-spoilers) from 5.5M total reviews
• Train/Validation/Test split: 140k/30k/30k (70%/15%/15%)
• Text cleaning: HTML tag removal, whitespace normalization
• Minimum review length: 30 characters
• Maximum sequence length: 512 tokens

Class Distribution:

• Spoiler: 50%
• Non-spoiler: 50%

Training Procedure

Training Hyperparameters

• Optimizer: AdamW
• Learning rate: 1e-5
• Batch size: 32
• Epochs: 5
• Max sequence length: 512
• Dropout: 0.3
• Weight decay: 0.01
• Warmup steps: 10% of total steps
• Learning rate schedule: Linear warmup with decay

Training Hardware

• GPU: NVIDIA T4 (Google Colab)
• Training time: ~2-3 hours

Framework

• PyTorch 2.5.1
• Transformers 4.x
• CUDA 12.1

Evaluation

Metrics

Evaluation Data

• 30,000 held-out reviews from the IMDB dataset
• Balanced split (50% spoilers, 50% non-spoilers)