TEXTCNN

Introduction

[paper]

Title: Convolutional Neural Networks for Sentence Classification

Authors: Yoon Kim

Abstract: We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

For source code, please refer to TEXTCNN

If you want to change parameters, dataset or evaluation settings, take a look at

• Config Introduction

• Data Module Introduction

• Trainer Module

• Usage