# capsnet-text-classification

**Repository Path**: mirrors_jmrozanec/capsnet-text-classification

## Basic Information

- **Project Name**: capsnet-text-classification
- **Description**: Provides a capsnet implementation for text classification
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-09
- **Last Updated**: 2026-01-17

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# capsnet-text-classification
Provides a capsnet implementation for text classification

We provide the following notebooks:
 - dataset-to-embeddings.ipynb: to transform headlines to embedding vectors used as new dataset to perform classification
 - dataset-analysis.ipynb: to obtain general information regarding the dataset: most frequent terms, which cannot be represented as embeddings, most frequent words particular to a specific topic, etc.
 - XGBoost.ipynb: classification performed with XGBoost
 - ConvRec.ipynb: ConvRec implementation and dataset classification
 - CapsNet.ipynb: CapsNet implementation and dataset classification

Results we obtained:


|Metric|CapsNet|XGBoost|ConvRec|
|------|-------|-------|-------|
|Accuracy|0.8901|0.8179|0.8897|
|Time trained|920 minutes|23 minutes|189 minutes|

To start a Docker image, run: 
- docker run -p 8888:8888 -v "$PWD":/home/jovyan jupyter/datascience-notebook
- docker exec b7f3abbf54da pip install keras